Advances in Artificial Intelligence The latest articles from Hindawi Publishing Corporation © 2015 , Hindawi Publishing Corporation . All rights reserved. Design and Implementation of Fuzzy Approximation PI Controller for Automatic Cruise Control System Sun, 15 Nov 2015 09:04:12 +0000 Fuzzy logic systems have been widely used for controlling nonlinear and complex dynamic systems by programming heuristic knowledge. But these systems are computationally complex and resource intensive. This paper presents a technique of development and porting of a fuzzy logic approximation PID controller (FLAC) in an automatic cruise control (ACC) system. ACC is a highly nonlinear process and its control is trivial due to the large change in parameters. Therefore, a suitable controller based on heuristic knowledge will be easy to develop and provide an effective solution. But the major problem with employing fuzzy logic controller (FLC) is its complexity. Moreover, the designing of Rulebase requires efficient heuristic knowledge about the system which is rarely found. Therefore, in this paper, a novel rule extraction process is used to derive a FLAC. This controller is then ported on a C6748 DSP hardware with timing and memory optimization. Later, it is seamlessly connected to a network to support remote reconfigurability. A performance analysis is drawn based on processor-in loop test with Simulink model of a cruise control system for vehicle. Pallab Maji, Sarat Kumar Patra, and Kamalakanta Mahapatra Copyright © 2015 Pallab Maji et al. All rights reserved. Wavelet Network: Online Sequential Extreme Learning Machine for Nonlinear Dynamic Systems Identification Sun, 20 Sep 2015 11:15:06 +0000 A single hidden layer feedforward neural network (SLFN) with online sequential extreme learning machine (OSELM) algorithm has been introduced and applied in many regression problems successfully. However, using SLFN with OSELM as black-box for nonlinear system identification may lead to building models for the identified plant with inconsistency responses from control perspective. The reason can refer to the random initialization procedure of the SLFN hidden node parameters with OSELM algorithm. In this paper, a single hidden layer feedforward wavelet network (WN) is introduced with OSELM for nonlinear system identification aimed at getting better generalization performances by reducing the effect of a random initialization procedure. Dhiadeen Mohammed Salih, Samsul Bahari Mohd Noor, Mohammad Hamiruce Merhaban, and Raja Mohd Kamil Copyright © 2015 Dhiadeen Mohammed Salih et al. All rights reserved. Impacts of the Load Models on Optimal Planning of Distributed Generation in Distribution System Thu, 17 Sep 2015 13:50:19 +0000 The optimal planning (sizing and siting) of the distributed generations (DGs) by using butterfly-PSO/BF-PSO technique to investigate the impacts of load models is presented in this work. The validity of the evaluated results is confirmed by comparing with well-known Genetic Algorithm (GA) and standard or conventional particle swarm optimization (PSO). To exhibit its compatibility in terms of load management, an impact of different load models on the size and location of DG has also been presented in this work. The fitness evolution function explored is the multiobjective function (FMO), which is based on the three significant indexes such as active power loss, reactive power loss, and voltage deviation index. The optimal solution is obtained by minimizing the multiobjective fitness function using BF-PSO, GA, and PSO technique. The comparison of the different optimization techniques is given for the different types of load models such as constant, industrial, residential, and commercial load models. The results clearly show that the BF-PSO technique presents the superior solution in terms of compatibility as well as computation time and efforts both. The algorithm has been carried out with 15-bus radial and 30-bus mesh system. Aashish Kumar Bohre, Ganga Agnihotri, Manisha Dubey, and Shilpa Kalambe Copyright © 2015 Aashish Kumar Bohre et al. All rights reserved. A Dirichlet Process Mixture Based Name Origin Clustering and Alignment Model for Transliteration Wed, 29 Jul 2015 08:54:41 +0000 In machine transliteration, it is common that the transliterated names in the target language come from multiple language origins. A conventional maximum likelihood based single model can not deal with this issue very well and often suffers from overfitting. In this paper, we exploit a coupled Dirichlet process mixture model (cDPMM) to address overfitting and names multiorigin cluster issues simultaneously in the transliteration sequence alignment step over the name pairs. After the alignment step, the cDPMM clusters name pairs into many groups according to their origin information automatically. In the decoding step, in order to use the learned origin information sufficiently, we use a cluster combination method (CCM) to build clustering-specific transliteration models by combining small clusters into large ones based on the perplexities of name language and transliteration model, which makes sure each origin cluster has enough data for training a transliteration model. On the three different Western-Chinese multiorigin names corpora, the cDPMM outperforms two state-of-the-art baseline models in terms of both the top-1 accuracy and mean F-score, and furthermore the CCM significantly improves the cDPMM. Chunyue Zhang, Tiejun Zhao, and Tingting Li Copyright © 2015 Chunyue Zhang et al. All rights reserved. Pop-Out: A New Cognitive Model of Visual Attention That Uses Light Level Analysis to Better Mimic the Free-Viewing Task of Static Images Wed, 10 Jun 2015 11:52:26 +0000 Human gaze is not directed to the same part of an image when lighting conditions change. Current saliency models do not consider light level analysis during their bottom-up processes. In this paper, we introduce a new saliency model which better mimics physiological and psychological processes of our visual attention in case of free-viewing task (bottom-up process). This model analyzes lighting conditions with the aim of giving different weights to color wavelengths. The resulting saliency measure performs better than a lot of popular cognitive approaches. Makiese Mibulumukini Copyright © 2015 Makiese Mibulumukini. All rights reserved. Study on Similarity among Indian Languages Using Language Verification Framework Tue, 19 May 2015 11:14:47 +0000 Majority of Indian languages have originated from two language families, namely, Indo-European and Dravidian. Therefore, certain kind of similarity among languages of a particular family can be expected to exist. Also, languages spoken in neighboring regions show certain similarity since there happens to be a lot of intermingling between population of neighboring regions. This paper develops a technique to measure similarity among Indian languages in a novel way, using language verification framework. Four verification systems are designed for each language. Acceptance of one language as another, which relates to false acceptance in language verification framework, is used as a measure of similarity. If language A shows false acceptance more than a predefined threshold with language B, in at least three out of the four systems, then languages A and B are considered to be similar in this work. It is expected that the languages belonging to the same family should manifest their similarity in experimental results. Also, similarity between neighboring languages should be detected through experiments. Any deviation from such fact should be due to specific linguistic or historical reasons. This work analyzes any such scenario. Debapriya Sengupta and Goutam Saha Copyright © 2015 Debapriya Sengupta and Goutam Saha. All rights reserved. Two Artificial Neural Networks for Modeling Discrete Survival Time of Censored Data Sun, 15 Mar 2015 08:58:34 +0000 Artificial neural network (ANN) theory is emerging as an alternative to conventional statistical methods in modeling nonlinear functions. The popular Cox proportional hazard model falls short in modeling survival data with nonlinear behaviors. ANN is a good alternative to the Cox PH as the proportionality of the hazard assumption and model relaxations are not required. In addition, ANN possesses a powerful capability of handling complex nonlinear relations within the risk factors associated with survival time. In this study, we present a comprehensive comparison of two different approaches of utilizing ANN in modeling smooth conditional hazard probability function. We use real melanoma cancer data to illustrate the usefulness of the proposed ANN methods. We report some significant results in comparing the survival time of male and female melanoma patients. Taysseer Sharaf and Chris P. Tsokos Copyright © 2015 Taysseer Sharaf and Chris P. Tsokos. All rights reserved. Genetic Algorithm Based PID Controller Tuning Approach for Continuous Stirred Tank Reactor Tue, 23 Dec 2014 10:02:22 +0000 Genetic algorithm (GA) based PID (proportional integral derivative) controller has been proposed for tuning optimized PID parameters in a continuous stirred tank reactor (CSTR) process using a weighted combination of objective functions, namely, integral square error (ISE), integral absolute error (IAE), and integrated time absolute error (ITAE). Optimization of PID controller parameters is the key goal in chemical and biochemical industries. PID controllers have narrowed down the operating range of processes with dynamic nonlinearity. In our proposed work, globally optimized PID parameters tend to operate the CSTR process in its entire operating range to overcome the limitations of the linear PID controller. The simulation study reveals that the GA based PID controller tuned with fixed PID parameters provides satisfactory performance in terms of set point tracking and disturbance rejection. A. Jayachitra and R. Vinodha Copyright © 2014 A. Jayachitra and R. Vinodha. All rights reserved. An Emotion Detection System Based on Multi Least Squares Twin Support Vector Machine Tue, 23 Dec 2014 06:27:39 +0000 Posttraumatic stress disorder (PTSD), bipolar manic disorder (BMD), obsessive compulsive disorder (OCD), depression, and suicide are some major problems existing in civilian and military life. The change in emotion is responsible for such type of diseases. So, it is essential to develop a robust and reliable emotion detection system which is suitable for real world applications. Apart from healthcare, importance of automatically recognizing emotions from human speech has grown with the increasing role of spoken language interfaces in human-computer interaction applications. Detection of emotion in speech can be applied in a variety of situations to allocate limited human resources to clients with the highest levels of distress or need, such as in automated call centers or in a nursing home. In this paper, we used a novel multi least squares twin support vector machine classifier in order to detect seven different emotions such as anger, happiness, sadness, anxiety, disgust, panic, and neutral emotions. The experimental result indicates better performance of the proposed technique over other existing approaches. The result suggests that the proposed emotion detection system may be used for screening of mental status. Divya Tomar, Divya Ojha, and Sonali Agarwal Copyright © 2014 Divya Tomar et al. All rights reserved. A New Evolutionary-Incremental Framework for Feature Selection Tue, 25 Nov 2014 13:10:06 +0000 Feature selection is an NP-hard problem from the viewpoint of algorithm design and it is one of the main open problems in pattern recognition. In this paper, we propose a new evolutionary-incremental framework for feature selection. The proposed framework can be applied on an ordinary evolutionary algorithm (EA) such as genetic algorithm (GA) or invasive weed optimization (IWO). This framework proposes some generic modifications on ordinary EAs to be compatible with the variable length of solutions. In this framework, the solutions related to the primary generations have short length. Then, the length of solutions may be increased through generations gradually. In addition, our evolutionary-incremental framework deploys two new operators called addition and deletion operators which change the length of solutions randomly. For evaluation of the proposed framework, we use that for feature selection in the application of face recognition. In this regard, we applied our feature selection method on a robust face recognition algorithm which is based on the extraction of Gabor coefficients. Experimental results show that our proposed evolutionary-incremental framework can select a few number of features from existing thousands features efficiently. Comparison result of the proposed methods with the previous methods shows that our framework is comprehensive, robust, and well-defined to apply on many EAs for feature selection. Mohamad-Hoseyn Sigari, Muhammad-Reza Pourshahabi, and Hamid-Reza Pourreza Copyright © 2014 Mohamad-Hoseyn Sigari et al. All rights reserved. Estimation of Missing Rainfall Data Using GEP: Case Study of Raja River, Alor Setar, Kedah Tue, 09 Sep 2014 00:00:00 +0000 Water resources and urban flood management require hydrologic and hydraulic modeling. However, incomplete precipitation data is often the issue during hydrological modeling exercise. In this study, gene expression programming (GEP) was utilised to correlate monthly precipitation data from a principal station with its neighbouring station located in Alor Setar, Kedah, Malaysia. GEP is an extension to genetic programming (GP), and can provide simple and efficient solution. The study illustrates the applications of GEP to determine the most suitable rainfall station to replace the principal rainfall station (station 6103047). This is to ensure that a reliable rainfall station can be made if the principal station malfunctioned. These were done by comparing principal station data with each individual neighbouring station. Result of the analysis reveals that the station 38 is the most compatible to the principal station where the value of R2 is 0.886. Nor Zaimah Che Ghani, Zorkeflee Abu Hasan, and Lau Tze Liang Copyright © 2014 Nor Zaimah Che Ghani et al. All rights reserved. Physical Violence Detection for Preventing School Bullying Sun, 24 Aug 2014 00:00:00 +0000 School bullying is a serious problem among teenagers, causing depression, dropping out of school, or even suicide. It is thus important to develop antibullying methods. This paper proposes a physical bullying detection method based on activity recognition. The architecture of the physical violence detection system is described, and a Fuzzy Multithreshold classifier is developed to detect physical bullying behaviour, including pushing, hitting, and shaking. Importantly, the application has the capability of distinguishing these types of behaviour from such everyday activities as running, walking, falling, or doing push-ups. To accomplish this, the method uses acceleration and gyro signals. Experimental data were gathered by role playing school bullying scenarios and by doing daily-life activities. The simulations achieved an average classification accuracy of 92%, which is a promising result for smartphone-based detection of physical bullying. Liang Ye, Hany Ferdinando, Tapio Seppänen, and Esko Alasaarela Copyright © 2014 Liang Ye et al. All rights reserved. Hybrid Wavelet-Postfix-GP Model for Rainfall Prediction of Anand Region of India Mon, 02 Jun 2014 12:06:20 +0000 An accurate prediction of rainfall is crucial for national economy and management of water resources. The variability of rainfall in both time and space makes the rainfall prediction a challenging task. The present work investigates the applicability of a hybrid wavelet-postfix-GP model for daily rainfall prediction of Anand region using meteorological variables. The wavelet analysis is used as a data preprocessing technique to remove the stochastic (noise) component from the original time series of each meteorological variable. The Postfix-GP, a GP variant, and ANN are then employed to develop models for rainfall using newly generated subseries of meteorological variables. The developed models are then used for rainfall prediction. The out-of-sample prediction performance of Postfix-GP and ANN models is compared using statistical measures. The results are comparable and suggest that Postfix-GP could be explored as an alternative tool for rainfall prediction. Vipul K. Dabhi and Sanjay Chaudhary Copyright © 2014 Vipul K. Dabhi and Sanjay Chaudhary. All rights reserved. Intelligent Control for USV Based on Improved Elman Neural Network with TSK Fuzzy Sun, 18 May 2014 07:13:23 +0000 In recent years, based on the rising of global personal safety demand and human resource cost considerations, development of unmanned vehicles to replace manpower requirement to perform high-risk operations is increasing. In order to acquire useful resources under the marine environment, a large boat as an unmanned surface vehicle (USV) was implemented. The USV is equipped with automatic navigation features and a complete substitute artificial manipulation. This USV system for exploring the marine environment has more carrying capacity and that measurement system can also be self-designed through a modular approach in accordance with the needs for various types of environmental conditions. The investigation work becomes more flexible. A catamaran hull is adopted as automatic navigation test with CompactRIO embedded system. Through GPS and direction sensor we not only can know the current location of the boat, but also can calculate the distance with a predetermined position and the angle difference immediately. In this paper, the design of automatic navigation is calculated in accordance with improved Elman neural network (ENN) algorithms. Takagi-Sugeno-Kang (TSK) fuzzy and improved ENN control are applied to adjust required power and steering, which allows the hull to move straight forward to a predetermined target position. The route will be free from outside influence and realize automatic navigation purpose. Shang-Jen Chuang, Chiung-Hsing Chen, Chih-Ming Hong, and Guan-Yu Chen Copyright © 2014 Shang-Jen Chuang et al. All rights reserved. Design of a T Factor Based RBFNC for a Flight Control System Thu, 24 Apr 2014 08:38:50 +0000 This paper presents the design of modified radial basic function neural controller (MRBFNC) for the pitch control of an aircraft to obtain the desired pitch angel as required by the pilot while maneuvering an aircraft. In this design, the parameters of radial basis function neural controller (RBFNC) are optimized by implementing a feedback mechanism which is controlled by a tuning factor “α” (T factor). For a given input, the response of the RBFN controller is tuned by using T factor for better performance of the aircraft pitch control system. The proposed system is demonstrated under different condition (absence and presence of sensor noise). The simulation results show that MRBFNC performs better, in terms of settling time and rise time for both conditions, than the conventional RBFNC. It is also seen that, as the value of the T factor increases, the aircraft pitch control system performs better and settles quickly to its reference trajectory. A comparison between MRBFNC and conventional RBFNC is also established to discuss the superiority of the former techniques. C. S. Mohanty, P. S. Khuntia, and D. Mitra Copyright © 2014 C. S. Mohanty et al. All rights reserved. Analysis of Changes in Market Shares of Commercial Banks Operating in Turkey Using Computational Intelligence Algorithms Tue, 15 Apr 2014 13:28:01 +0000 This paper aims to model the change in market share of 30 domestic and foreign banks, which have been operating between the years 1990 and 2009 in Turkey by taking into consideration 20 financial ratios of those banks. Due to the fragile structure of the banking sector in Turkey, this study plays an important role for determining the changes in market share of banks and taking the necessary measures promptly. For this reason, computational intelligence methods have been used in the study. According to the research results, it is seen that it was not able to properly anticipate the data for the banking sector in the periods of financial crises (2000-2001 and 2008-2009). However, it is seen that, Simple Linear Regression is distinguished as a good algorithm among the computational intelligence algorithms for all periods between the years 1990 and 2009. M. Fatih Amasyali, Ayse Demırhan, and Mert Bal Copyright © 2014 M. Fatih Amasyali et al. All rights reserved. Reinforcement Learning in an Environment Synthetically Augmented with Digital Pheromones Thu, 13 Mar 2014 17:31:21 +0000 Reinforcement learning requires information about states, actions, and outcomes as the basis for learning. For many applications, it can be difficult to construct a representative model of the environment, either due to lack of required information or because of that the model's state space may become too large to allow a solution in a reasonable amount of time, using the experience of prior actions. An environment consisting solely of the occurrence or nonoccurrence of specific events attributable to a human actor may appear to lack the necessary structure for the positioning of responding agents in time and space using reinforcement learning. Digital pheromones can be used to synthetically augment such an environment with event sequence information to create a more persistent and measurable imprint on the environment that supports reinforcement learning. We implemented this method and combined it with the ability of agents to learn from actions not taken, a concept known as fictive learning. This approach was tested against the historical sequence of Somali maritime pirate attacks from 2005 to mid-2012, enabling a set of autonomous agents representing naval vessels to successfully respond to an average of 333 of the 899 pirate attacks, outperforming the historical record of 139 successes. Salvador E. Barbosa and Mikel D. Petty Copyright © 2014 Salvador E. Barbosa and Mikel D. Petty. All rights reserved. A Comparative Study between Optimization and Market-Based Approaches to Multi-Robot Task Allocation Tue, 12 Nov 2013 08:29:31 +0000 This paper presents a comparative study between optimization-based and market-based approaches used for solving the Multirobot task allocation (MRTA) problem that arises in the context of multirobot systems (MRS). The two proposed approaches are used to find the optimal allocation of a number of heterogeneous robots to a number of heterogeneous tasks. The two approaches were extensively tested over a number of test scenarios in order to test their capability of handling complex heavily constrained MRS applications that include extended number of tasks and robots. Finally, a comparative study is implemented between the two approaches and the results show that the optimization-based approach outperforms the market-based approach in terms of optimal allocation and computational time. Mohamed Badreldin, Ahmed Hussein, and Alaa Khamis Copyright © 2013 Mohamed Badreldin et al. All rights reserved. Handling Data Uncertainty and Inconsistency Using Multisensor Data Fusion Sun, 03 Nov 2013 14:09:40 +0000 Data provided by sensors is always subjected to some level of uncertainty and inconsistency. Multisensor data fusion algorithms reduce the uncertainty by combining data from several sources. However, if these several sources provide inconsistent data, catastrophic fusion may occur where the performance of multisensor data fusion is significantly lower than the performance of each of the individual sensor. This paper presents an approach to multisensor data fusion in order to decrease data uncertainty with ability to identify and handle inconsistency. The proposed approach relies on combining a modified Bayesian fusion algorithm with Kalman filtering. Three different approaches, namely, prefiltering, postfiltering and pre-postfiltering are described based on how filtering is applied to the sensor data, to the fused data or both. A case study to find the position of a mobile robot by estimating its x and y coordinates using four sensors is presented. The simulations show that combining fusion with filtering helps in handling the problem of uncertainty and inconsistency of the data. Waleed A. Abdulhafiz and Alaa Khamis Copyright © 2013 Waleed A. Abdulhafiz and Alaa Khamis. All rights reserved. Adaptive Group Formation in Multirobot Systems Mon, 21 Oct 2013 12:12:57 +0000 Multirobot systems (MRSs) are capable of solving task complexity, increasing performance in terms of maximizing spatial/temporal/radio coverage or minimizing mission completion time. They are also more reliable than single-robot systems as robustness is increased through redundancy. Many applications such as rescue, reconnaissance, and surveillance and communication relaying require the MRS to be able to self-organize the team members in a decentralized way. Group formation is one of the benchmark problems in MRS to study self-organization in these systems. This paper presents a hybrid approach to group formation problem in multi-robot systems. This approach combines the efficiency of the cellular automata as finite state machine, the interconnectivity of the virtual grid and its bonding technique, and last but not least the decentralization of the adaptive dynamic leadership. Ahmed Wagdy and Alaa Khamis Copyright © 2013 Ahmed Wagdy and Alaa Khamis. All rights reserved. A Novel Reinforcement Learning Architecture for Continuous State and Action Spaces Thu, 18 Apr 2013 17:00:14 +0000 We introduce a reinforcement learning architecture designed for problems with an infinite number of states, where each state can be seen as a vector of real numbers and with a finite number of actions, where each action requires a vector of real numbers as parameters. The main objective of this architecture is to distribute in two actors the work required to learn the final policy. One actor decides what action must be performed; meanwhile, a second actor determines the right parameters for the selected action. We tested our architecture and one algorithm based on it solving the robot dribbling problem, a challenging robot control problem taken from the RoboCup competitions. Our experimental work with three different function approximators provides enough evidence to prove that the proposed architecture can be used to implement fast, robust, and reliable reinforcement learning algorithms. Víctor Uc-Cetina Copyright © 2013 Víctor Uc-Cetina. All rights reserved. Imprecise Imputation as a Tool for Solving Classification Problems with Mean Values of Unobserved Features Mon, 15 Apr 2013 11:39:23 +0000 A method for solving a classification problem when there is only partial information about some features is proposed. This partial information comprises the mean values of features for every class and the bounds of the features. In order to maximally exploit the available information, a set of probability distributions is constructed such that two distributions are selected from the set which define the minimax and minimin strategies. Random values of features are generated in accordance with the selected distributions by using the Monte Carlo technique. As a result, the classification problem is reduced to the standard model which is solved by means of the support vector machine. Numerical examples illustrate the proposed method. Lev V. Utkin and Yulia A. Zhuk Copyright © 2013 Lev V. Utkin and Yulia A. Zhuk. All rights reserved. Selection for Reinforcement-Free Learning Ability as an Organizing Factor in the Evolution of Cognition Sun, 31 Mar 2013 12:13:18 +0000 This research explores the relation between environmental structure and neurocognitive structure. We hypothesize that selection pressure on abilities for efficient learning (especially in settings with limited or no reward information) translates into selection pressure on correspondence relations between neurocognitive and environmental structure, since such correspondence allows for simple changes in the environment to be handled with simple learning updates in neurocognitive structure. We present a model in which a simple form of reinforcement-free learning is evolved in neural networks using neuromodulation and analyze the effect this selection for learning ability has on the virtual species' neural organization. We find a higher degree of organization than in a control population evolved without learning ability and discuss the relation between the observed neural structure and the environmental structure. We discuss our findings in the context of the environmental complexity thesis, the Baldwin effect, and other interactions between adaptation processes. Solvi Arnold, Reiji Suzuki, and Takaya Arita Copyright © 2013 Solvi Arnold et al. All rights reserved. Predicting Asthma Outcome Using Partial Least Square Regression and Artificial Neural Networks Wed, 27 Mar 2013 14:19:02 +0000 The long-term solution to the asthma epidemic is believed to be prevention and not treatment of the established disease. Most cases of asthma begin during the first years of life; thus the early determination of which young children will have asthma later in their life counts as an important priority. Artificial neural networks (ANN) have been already utilized in medicine in order to improve the performance of the clinical decision-making tools. In this study, a new computational intelligence technique for the prediction of persistent asthma in children is presented. By employing partial least square regression, 9 out of 48 prognostic factors correlated to the persistent asthma have been chosen. Multilayer perceptron and probabilistic neural networks topologies have been investigated in order to obtain the best prediction accuracy. Based on the results, it is shown that the proposed system is able to predict the asthma outcome with a success of 96.77%. The ANN, with which these high rates of reliability were obtained, will help the doctors to identify which of the young patients are at a high risk of asthma disease progression. Moreover, this may lead to better treatment opportunities and hopefully better disease outcomes in adulthood. E. Chatzimichail, E. Paraskakis, and A. Rigas Copyright © 2013 E. Chatzimichail et al. All rights reserved. A Novel Method for Training an Echo State Network with Feedback-Error Learning Wed, 27 Mar 2013 10:13:31 +0000 Echo state networks are a relatively new type of recurrent neural networks that have shown great potentials for solving non-linear, temporal problems. The basic idea is to transform the low dimensional temporal input into a higher dimensional state, and then train the output connection weights to make the system output the target information. Because only the output weights are altered, training is typically quick and computationally efficient compared to training of other recurrent neural networks. This paper investigates using an echo state network to learn the inverse kinematics model of a robot simulator with feedback-error-learning. In this scheme teacher forcing is not perfect, and joint constraints on the simulator makes the feedback error inaccurate. A novel training method which is less influenced by the noise in the training data is proposed and compared to the traditional ESN training method. Rikke Amilde Løvlid Copyright © 2013 Rikke Amilde Løvlid. All rights reserved. A Hybrid Reasoning Model for “Whole and Part” Cardinal Direction Relations Thu, 28 Feb 2013 10:01:04 +0000 We have shown how the nine tiles in the projection-based model for cardinal directions can be partitioned into sets based on horizontal and vertical constraints (called Horizontal and Vertical Constraints Model) in our previous papers (Kor and Bennett, 2003 and 2010). In order to come up with an expressive hybrid model for direction relations between two-dimensional single-piece regions (without holes), we integrate the well-known RCC-8 model with the above-mentioned model. From this expressive hybrid model, we derive 8 basic binary relations and 13 feasible as well as jointly exhaustive relations for the x- and y-directions, respectively. Based on these basic binary relations, we derive two separate composition tables for both the expressive and weak direction relations. We introduce a formula that can be used for the computation of the composition of expressive and weak direction relations between “whole or part” regions. Lastly, we also show how the expressive hybrid model can be used to make several existential inferences that are not possible for existing models. Ah-Lian Kor and Brandon Bennett Copyright © 2013 Ah-Lian Kor and Brandon Bennett. All rights reserved. Artificial Intelligence Applications in Biomedicine Wed, 27 Feb 2013 09:53:56 +0000 Panayiotis Vlamos, Konstantinos Lefkimmiatis, Catalina Cocianu, Luminita State, and Zhiyuan Luo Copyright © 2013 Panayiotis Vlamos et al. All rights reserved. Efficacious End User Measures—Part 1: Relative Class Size and End User Problem Domains Tue, 26 Feb 2013 13:21:29 +0000 Biological and medical endeavors are beginning to realize the benefits of artificial intelligence and machine learning. However, classification, prediction, and diagnostic (CPD) errors can cause significant losses, even loss of life. Hence, end users are best served when they have performance information relevant to their needs, this paper’s focus. Relative class size (rCS) is commonly recognized as a confounding factor in CPD evaluation. Unfortunately, rCS-invariant measures are not easily mapped to end user conditions. We determine a cause of rCS invariance, joint probability table (JPT) normalization. JPT normalization means that more end user efficacious measures can be used without sacrificing invariance. An important revelation is that without data normalization, the Matthews correlation coefficient (MCC) and information coefficient (IC) are not relative class size invariants; this is a potential source of confusion, as we found not all reports using MCC or IC normalize their data. We derive MCC rCS-invariant expression. JPT normalization can be extended to allow JPT rCS to be set to any desired value (JPT tuning). This makes sensitivity analysis feasible, a benefit to both applied researchers and practitioners (end users). We apply our findings to two published CPD studies to illustrate how end users benefit. E. Earl Eiland and Lorie M. Liebrock Copyright © 2013 E. Earl Eiland and Lorie M. Liebrock. All rights reserved. Conservative Intensional Extension of Tarski's Semantics Tue, 26 Feb 2013 09:00:42 +0000 We considered an extension of the first-order logic (FOL) by Bealer's intensional abstraction operator. Contemporary use of the term “intension” derives from the traditional logical Frege-Russell doctrine that an idea (logic formula) has both an extension and an intension. Although there is divergence in formulation, it is accepted that the “extension” of an idea consists of the subjects to which the idea applies, and the “intension” consists of the attributes implied by the idea. From the Montague's point of view, the meaning of an idea can be considered as particular extensions in different possible worlds. In the case of standard FOL, we obtain a commutative homomorphic diagram, which is valid in each given possible world of an intensional FOL: from a free algebra of the FOL syntax, into its intensional algebra of concepts, and, successively, into an extensional relational algebra (different from Cylindric algebras). Then we show that this composition corresponds to the Tarski's interpretation of the standard extensional FOL in this possible world. Zoran Majkić Copyright © 2013 Zoran Majkić. All rights reserved. Discrete Artificial Bee Colony for Computationally Efficient Symbol Detection in Multidevice STBC MIMO Systems Sun, 24 Feb 2013 18:22:32 +0000 A Discrete Artificial Bee Colony (DABC) is presented for joint symbol detection at the receiver in a multidevice Space-Time Block Code (STBC) Mutli-Input Multi-Output (MIMO) communication system. Exhaustive search (maximum likelihood detection) for finding an optimal detection has a computational complexity that increases exponentially with the number of mobile devices, transmit antennas per mobile device, and the number of bits per symbol. ABC is a new population-based, swarm-based Evolutionary Algorithms (EA) presented for multivariable numerical functions and has shown good performance compared to other mainstream EAs for problems in continuous domain. This algorithm simulates the intelligent foraging behavior of honeybee swarms. An enhanced discrete version of the ABC algorithm is presented and applied to the joint symbol detection problem to find a nearly optimal solution in real time. The results of multiple independent simulation runs indicate the effectiveness of DABC with other well-known algorithms previously proposed for joint symbol detection such as the near-optimal sphere decoding, minimum mean square error, zero forcing, and semidefinite relaxation, along with other EAs such as genetic algorithm, estimation of distributions algorithm, and the more novel biogeography-based optimization algorithm. Saeed Ashrafinia, Muhammad Naeem, and Daniel Lee Copyright © 2013 Saeed Ashrafinia et al. All rights reserved.