http://www.hindawi.com The latest articles from Hindawi Publishing Corporation © 2013 , Hindawi Publishing Corporation . All rights reserved. Tue, 07 May 2013 09:15:36 +0000 http://www.hindawi.com/journals/aans/2013/485913/ A novel approach based on the neural network (NN) ensemble technique is formulated and used for development of a NN stochastic convection parameterization for climate and numerical weather prediction (NWP) models. This fast parameterization is built based on learning from data simulated by a cloud-resolving model (CRM) initialized with and forced by the observed meteorological data available for 4-month boreal winter from November 1992 to February 1993. CRM-simulated data were averaged and processed to implicitly define a stochastic convection parameterization. This parameterization is learned from the data using an ensemble of NNs. The NN ensemble members are trained and tested. The inherent uncertainty of the stochastic convection parameterization derived following this approach is estimated. The newly developed NN convection parameterization has been tested in National Center of Atmospheric Research (NCAR) Community Atmospheric Model (CAM). It produced reasonable and promising decadal climate simulations for a large tropical Pacific region. The extent of the adaptive ability of the developed NN parameterization to the changes in the model environment is briefly discussed. This paper is devoted to a proof of concept and discusses methodology, initial results, and the major challenges of using the NN technique for developing convection parameterizations for climate and NWP models. Vladimir M. Krasnopolsky, Michael S. Fox-Rabinovitz, and Alexei A. Belochitski Copyright © 2013 Vladimir M. Krasnopolsky et al. All rights reserved. Tue, 26 Mar 2013 14:45:37 +0000 http://www.hindawi.com/journals/aans/2013/962734/ The present study intends to propose identification methodologies for multistorey shear buildings using the powerful technique of Artificial Neural Network (ANN) models which can handle fuzzified data. Identification with crisp data is known, and also neural network method has already been used by various researchers for this case. Here, the input and output data may be in fuzzified form. This is because in general we may not get the corresponding input and output values exactly (in crisp form), but we have only the uncertain information of the data. This uncertain data is assumed in terms of fuzzy number, and the corresponding problem of system identification is investigated. Deepti Moyi Sahoo and S. Chakraverty Copyright © 2013 Deepti Moyi Sahoo and S. Chakraverty. All rights reserved. Tue, 05 Mar 2013 16:30:27 +0000 http://www.hindawi.com/journals/aans/2013/240564/ Multipath mitigation is a long-standing problem in global positioning system (GPS) research and is essential for improving the accuracy and precision of positioning solutions. In this work, we consider multipath error estimation as a regression problem and propose a unified framework for both code and carrier-phase multipath mitigation for ground fixed GPS stations. We use the kernel support vector machine to predict multipath errors, since it is known to potentially offer better-performance traditional models, such as neural networks. The predicted multipath error is then used to correct GPS measurements. We empirically show that the proposed method can reduce the code multipath error standard deviation up to 79% on average, which significantly outperforms other approaches in the literature. A comparative analysis of reduction of double-differential carrier-phase multipath error reveals that a 57% reduction is also achieved. Furthermore, by simulation, we also show that this method is robust to coexisting signals of phenomena (e.g., seismic signals) we wish to preserve. Quoc-Huy Phan, Su-Lim Tan, Ian McLoughlin, and Duc-Lung Vu Copyright © 2013 Quoc-Huy Phan et al. All rights reserved. Sun, 03 Mar 2013 18:02:32 +0000 http://www.hindawi.com/journals/aans/2013/150209/ In this research, dynamic response of a cracked shaft having transverse crack is analyzed using theoretical neural network and experimental analysis. Structural damage detection using frequency response functions (FRFs) as input data to the back-propagation neural network (BPNN) has been explored. For deriving the effect of crack depths and crack locations on FRF, theoretical expressions have been developed using strain energy release rate at the crack section of the shaft for the calculation of the local stiffnesses. Based on the flexibility, a new stiffness matrix is deduced that is subsequently used to calculate the natural frequencies and mode shapes of the cracked beam using the neural network method. The results of the numerical analysis and the neural network method are being validated with the result from the experimental method. The analysis results on a shaft show that the neural network can assess damage conditions with very good accuracy. Dhirendranath Thatoi and Prabir Kumar Jena Copyright © 2013 Dhirendranath Thatoi and Prabir Kumar Jena. All rights reserved. Sun, 24 Feb 2013 09:30:14 +0000 http://www.hindawi.com/journals/aans/2013/292567/ A constrained neural network optimization algorithm is presented for factorizing simultaneously the numerator and denominator polynomials of the transfer functions of 2-D IIR filters. The method minimizes a cost function based on the frequency response of the filters, along with simultaneous satisfaction of appropriate constraints, so that factorization is facilitated and the stability of the resulting filter is respected. Nicholas Ampazis and Stavros J. Perantonis Copyright © 2013 Nicholas Ampazis and Stavros J. Perantonis. All rights reserved. Wed, 09 Jan 2013 10:15:03 +0000 http://www.hindawi.com/journals/aans/2013/539570/ This paper aims to construct intelligence models by applying the technologies of artificial neural networks including back-propagation network (BPN), generalized feedforward neural networks (GRNN), and modular neural network (MNN) that are developed, respectively, for the early detection of chronic kidney disease (CKD). The comparison of accuracy, sensitivity, and specificity among three models is subsequently performed. The model of best performance is chosen. By leveraging the aid of this system, CKD physicians can have an alternative way to detect chronic kidney diseases in early stage of a patient. Meanwhile, it may also be used by the public for self-detecting the risk of contracting CKD. Ruey Kei Chiu, Renee Y. Chen, Shin-An Wang, Yen-Chun Chang, and Li-Chien Chen Copyright © 2013 Ruey Kei Chiu et al. All rights reserved. Mon, 31 Dec 2012 17:59:13 +0000 http://www.hindawi.com/journals/aans/2012/571358/ For the Hopfield Neural Network problem we consider unbounded monotone nondecreasing activation functions. We prove convergence to zero in an exponential manner provided that we start with sufficiently small initial data. Nasser-eddine Tatar Copyright © 2012 Nasser-eddine Tatar. All rights reserved. Sun, 21 Oct 2012 07:45:28 +0000 http://www.hindawi.com/journals/aans/2012/219860/ Anke Meyer-Baese, Sylvain Lespinats, Juan Manuel Gorriz Saez, and Olivier Bastien Copyright © 2012 Anke Meyer-Baese et al. All rights reserved. Wed, 10 Oct 2012 13:51:19 +0000 http://www.hindawi.com/journals/aans/2012/713581/ This paper presents a deterministic and adaptive spike model derived from radial basis functions and a leaky integrate-and-fire sampler developed for training spiking neural networks without direct weight manipulation. Several algorithms have been proposed for training spiking neural networks through biologically-plausible learning mechanisms, such as spike-timing-dependent synaptic plasticity and Hebbian plasticity. These algorithms typically rely on the ability to update the synaptic strengths, or weights, directly, through a weight update rule in which the weight increment can be decided and implemented based on the training equations. However, in several potential applications of adaptive spiking neural networks, including neuroprosthetic devices and CMOS/memristor nanoscale neuromorphic chips, the weights cannot be manipulated directly and, instead, tend to change over time by virtue of the pre- and postsynaptic neural activity. This paper presents an indirect learning method that induces changes in the synaptic weights by modulating spike-timing-dependent plasticity by means of controlled input spike trains. In place of the weights, the algorithm manipulates the input spike trains used to stimulate the input neurons by determining a sequence of spike timings that minimize a desired objective function and, indirectly, induce the desired synaptic plasticity in the network. X. Zhang, G. Foderaro, C. Henriquez, A. M. J. VanDongen, and S. Ferrari Copyright © 2012 X. Zhang et al. All rights reserved. Thu, 06 Sep 2012 17:50:29 +0000 http://www.hindawi.com/journals/aans/2012/808602/ Motion-induced artifacts represent a major problem in detection and diagnosis of breast cancer in dynamic contrast-enhanced magnetic resonance imaging. The goal of this paper is to evaluate the performance of a new nonrigid motion correction algorithm based on the optical flow method. For each of the small lesions, we extracted morphological and dynamical features describing both global and local shape, and kinetics behavior. In this paper, we compare the performance of each extracted feature set under consideration of several 2D or 3D motion compensation parameters for the differential diagnosis of enhancing lesions in breast MRI. Based on several simulation results, we determined the optimal motion compensation parameters. Our results have shown that motion compensation can improve the classification results. The results suggest that the computerized analysis system based on the non-rigid motion compensation technique and spatiotemporal features has the potential to increase the diagnostic accuracy of MRI mammography for small lesions and can be used as a basis for computer-aided diagnosis of breast cancer with MR mammography. F. Steinbruecker, A. Meyer-Baese, T. Schlossbauer, and D. Cremers Copyright © 2012 F. Steinbruecker et al. All rights reserved. Sun, 26 Aug 2012 13:13:51 +0000 http://www.hindawi.com/journals/aans/2012/190359/ This paper introduces two unsupervised learning methods for analyzing functional magnetic resonance imaging (fMRI) data based on hidden Markov model (HMM). HMM approach is focused on capturing the first-order statistical evolution among the samples of a voxel time series, and it can provide a complimentary perspective of the BOLD signals. Two-state HMM is created for each voxel, and the model parameters are estimated from the voxel time series and the stimulus paradigm. Two different activation detection methods are presented in this paper. The first method is based on the likelihood and likelihood-ratio test, in which an additional Gaussian model is used to enhance the contrast of the HMM likelihood map. The second method is based on certain distance measures between the two state distributions, in which the most likely HMM state sequence is estimated through the Viterbi algorithm. The distance between the on-state and off-state distributions is measured either through a t-test, or using the Kullback-Leibler distance (KLD). Experimental results on both normal subject and brain tumor subject are presented. HMM approach appears to be more robust in detecting the supplemental active voxels comparing with SPM, especially for brain tumor subject. Rong Duan and Hong Man Copyright © 2012 Rong Duan and Hong Man. All rights reserved. Thu, 09 Aug 2012 14:19:50 +0000 http://www.hindawi.com/journals/aans/2012/835903/ The kidneys are very vital organs. Failing kidneys lose their ability to filter out waste products, resulting in kidney disease. To extend or save the lives of patients with impaired kidney function, kidney replacement is typically utilized, such as hemodialysis. This work uses an entropy function to identify key features related to hemodialysis. By identifying these key features, one can determine whether a patient requires hemodialysis. This work uses these key features as dimensions in cluster analysis. The key features can effectively determine whether a patient requires hemodialysis. The proposed data mining scheme finds association rules of each cluster. Hidden rules for causing any kidney disease can therefore be identified. The contributions and key points of this paper are as follows. (1) This paper finds some key features that can be used to predict the patient who may has high probability to perform hemodialysis. (2) The proposed scheme applies k-means clustering algorithm with the key features to category the patients. (3) A data mining technique is used to find the association rules from each cluster. (4) The mined rules can be used to determine whether a patient requires hemodialysis. Tzu-Chuen Lu and Chun-Ya Tseng Copyright © 2012 Tzu-Chuen Lu and Chun-Ya Tseng. All rights reserved. Mon, 30 Jul 2012 10:35:11 +0000 http://www.hindawi.com/journals/aans/2012/534683/ Accident management became a popular research issue in the early 1990s. Computerized decision support was studied from many points of view. Early fault detection and information visualization are important key issues in accident management also today. In this paper we make a brief review on this research history mostly from the last two decades including the severe accident management. The author’s studies are reflected to the state of the art. The self-organizing map method is combined with other more or less traditional methods. Neural methods used together with knowledge-based methods constitute a methodological base for the presented decision support prototypes. Two application examples with modern decision support visualizations are introduced more in detail. A case example of detecting a pressure drift on the boiling water reactor by multivariate methods including innovative visualizations is studied in detail. Promising results in early fault detection are achieved. The operators are provided by added information value to be able to detect anomalies in an early stage already. We provide the plant staff with a methodological tool set, which can be combined in various ways depending on the special needs in each case. Miki Sirola and Jaakko Talonen Copyright © 2012 Miki Sirola and Jaakko Talonen. All rights reserved. Tue, 24 Jul 2012 10:56:37 +0000 http://www.hindawi.com/journals/aans/2012/107046/ Most attempts at training computers for the difficult and time-consuming task of sleep stage classification involve a feature extraction step. Due to the complexity of multimodal sleep data, the size of the feature space can grow to the extent that it is also necessary to include a feature selection step. In this paper, we propose the use of an unsupervised feature learning architecture called deep belief nets (DBNs) and show how to apply it to sleep data in order to eliminate the use of handmade features. Using a postprocessing step of hidden Markov model (HMM) to accurately capture sleep stage switching, we compare our results to a feature-based approach. A study of anomaly detection with the application to home environment data collection is also presented. The results using raw data with a deep architecture, such as the DBN, were comparable to a feature-based approach when validated on clinical datasets. Martin Längkvist, Lars Karlsson, and Amy Loutfi Copyright © 2012 Martin Längkvist et al. All rights reserved. Thu, 12 Jul 2012 11:07:15 +0000 http://www.hindawi.com/journals/aans/2012/919281/ Automated detection and diagnosis of small lesions in breast MRI represents a challenge for the traditional computer-aided diagnosis (CAD) systems. The goal of the present research was to compare and determine the optimal feature sets describing the morphology and the enhancement kinetic features for a set of small lesions and to determine their diagnostic performance. For each of the small lesions, we extracted morphological and dynamical features describing both global and local shape, and kinetics behavior. In this paper, we compare the performance of each extracted feature set for the differential diagnosis of enhancing lesions in breast MRI. Based on several simulation results, we determined the optimal feature number and tested different classification techniques. The results suggest that the computerized analysis system based on spatiotemporal features has the potential to increase the diagnostic accuracy of MRI mammography for small lesions and can be used as a basis for computer-aided diagnosis of breast cancer with MR mammography. F. Steinbruecker, A. Meyer-Baese, C. Plant, T. Schlossbauer, and U. Meyer-Baese Copyright © 2012 F. Steinbruecker et al. All rights reserved. Wed, 20 Jun 2012 08:10:41 +0000 http://www.hindawi.com/journals/aans/2012/703878/ Many living systems, from cells to brains to governments, are controlled by the activity of a small subset of their constituents. It has been argued that coherence is of evolutionary advantage and that this active subset of constituents results from competition between two processes, a Next process that brings about coherence over time, and a Now process that brings about coherence between the interior and the exterior of the system at a particular time. This competition has been termed competitive coherence and has been implemented in a toy-learning program in order to clarify the concept and to generate—and ultimately test—new hypotheses covering subjects as diverse as complexity, emergence, DNA replication, global mutations, dreaming, bioputing (computing using either the parts of biological system or the entire biological system), and equilibrium and nonequilibrium structures. Here, we show that a program using competitive coherence, Coco, can learn to respond to a simple input sequence 1, 2, 3, 2, 3, with responses to inputs that differ according to the position of the input in the sequence and hence require competition between both Next and Now processes. Vic Norris, Maurice Engel, and Maurice Demarty Copyright © 2012 Vic Norris et al. All rights reserved. Thu, 07 Jun 2012 12:48:42 +0000 http://www.hindawi.com/journals/aans/2012/457590/ The primary goal of brain image segmentation is to partition a given brain image into different regions representing anatomical structures. Magnetic resonance image (MRI) segmentation is especially interesting, since accurate segmentation in white matter, grey matter and cerebrospinal fluid provides a way to identify many brain disorders such as dementia, schizophrenia or Alzheimer’s disease (AD). Then, image segmentation results in a very interesting tool for neuroanatomical analyses. In this paper we show three alternatives to MR brain image segmentation algorithms, with the Self-Organizing Map (SOM) as the core of the algorithms. The procedures devised do not use any a priori knowledge about voxel class assignment, and results in fully-unsupervised methods for MRI segmentation, making it possible to automatically discover different tissue classes. Our algorithm has been tested using the images from the Internet Brain Image Repository (IBSR) outperforming existing methods, providing values for the average overlap metric of 0.7 for the white and grey matter and 0.45 for the cerebrospinal fluid. Furthermore, it also provides good results for high-resolution MR images provided by the Nuclear Medicine Service of the “Virgen de las Nieves” Hospital (Granada, Spain). A. Ortiz, J. M. Gorriz, J. Ramirez, and D. Salas-Gonzalez Copyright © 2012 A. Ortiz et al. All rights reserved. Mon, 04 Jun 2012 08:37:08 +0000 http://www.hindawi.com/journals/aans/2012/962105/ Independent component analysis (ICA) is an essential building block for data analysis in many applications. Selecting the truly meaningful components from the result of an ICA algorithm, or comparing the results of different algorithms, however, is nontrivial problems. We introduce a very general technique for evaluating ICA results rooted in information-theoretic model selection. The basic idea is to exploit the natural link between non-Gaussianity and data compression: the better the data transformation represented by one or several ICs improves the effectiveness of data compression, the higher is the relevance of the ICs. We propose two different methods which allow an efficient data compression of non-Gaussian signals: Phi-transformed histograms and fuzzy histograms. In an extensive experimental evaluation, we demonstrate that our novel information-theoretic measures robustly select non-Gaussian components from data in a fully automatic way, that is, without requiring any restrictive assumptions or thresholds. Claudia Plant, Son Mai Thai, Junming Shao, Fabian J. Theis, Anke Meyer-Baese, and Christian Böhm Copyright © 2012 Claudia Plant et al. All rights reserved. Thu, 08 Mar 2012 11:10:31 +0000 http://www.hindawi.com/journals/aans/2012/517234/ Artificial neural network (ANN) modeling methods are becoming more widely used as both a research and application paradigm across a much wider variety of business, medical, engineering, and social science disciplines. The combination or triangulation of ANN methods with more traditional methods can facilitate the development of high-quality research models and also improve output performance for real world applications. Prior methodological triangulation that utilizes ANNs is reviewed and a new triangulation of ANNs with structural equation modeling and cluster analysis for predicting an individual's computer self-efficacy (CSE) is shown to empirically analyze the effect of methodological triangulation, at least for this specific information systems research case. A new construct, engagement, is identified as a necessary component of CSE models and the subsequent triangulated ANN models are able to achieve an 84% CSE group prediction accuracy. Steven Walczak Copyright © 2012 Steven Walczak. All rights reserved. Tue, 28 Feb 2012 09:55:23 +0000 http://www.hindawi.com/journals/aans/2012/397146/ A four-dimensional neural network model with delay is investigated. With the help of the theory of delay differential equation and Hopf bifurcation, the conditions of the equilibrium undergoing Hopf bifurcation are worked out by choosing the delay as parameter. Applying the normal form theory and the center manifold argument, we derive the explicit formulae for determining the properties of the bifurcating periodic solutions. Numerical simulations are performed to illustrate the analytical results. Changjin Xu and Peiluan Li Copyright © 2012 Changjin Xu and Peiluan Li. All rights reserved. Mon, 26 Dec 2011 10:35:54 +0000 http://www.hindawi.com/journals/aans/2011/374816/ Multilayer perceptron (MLP) with back-propagation learning rule is adopted to predict the winning rates of two teams according to their official statistical data of 2006 World Cup Football Game at the previous stages. There are training samples from three classes: win, draw, and loss. At the new stage, new training samples are selected from the previous stages and are added to the training samples, then we retrain the neural network. It is a type of on-line learning. The 8 features are selected with ad hoc choice. We use the theorem of Mirchandani and Cao to determine the number of hidden nodes. And after the testing in the learning convergence, the MLP is determined as 8-2-3 model. The learning rate and momentum coefficient are determined in the cross-learning. The prediction accuracy achieves 75% if the draw games are excluded. Kou-Yuan Huang and Kai-Ju Chen Copyright © 2011 Kou-Yuan Huang and Kai-Ju Chen. All rights reserved. Thu, 08 Dec 2011 09:37:24 +0000 http://www.hindawi.com/journals/aans/2011/523094/ The use of hybrid intelligent systems (HISs) is necessary to bring the behavior of intelligent autonomous vehicles (IAVs) near the human one in recognition, learning, adaptation, generalization, decision making, and action. First, the necessity of HIS and some navigation approaches based on fuzzy ArtMap neural networks (FAMNNs) are discussed. Indeed, such approaches can provide IAV with more autonomy, intelligence, and real-time processing capabilities. Second, an FAMNN-based navigation approach is suggested. Indeed, this approach must provide vehicles with capability, after supervised fast stable learning: simplified fuzzy ArtMap (SFAM), to recognize both target-location and obstacle-avoidance situations using FAMNN1 and FAMNN2, respectively. Afterwards, the decision making and action consist of two association stages, carried out by reinforcement trial and error learning, and their coordination using NN3. Then, NN3 allows to decide among the five (05) actions to move towards 30∘, 60∘, 90∘, 120∘, and 150∘. Third, simulation results display the ability of the FAMNN-based approach to provide IAV with intelligent behaviors allowing to intelligently navigate in partially structured environments. Finally, a discussion, dealing with the suggested approach and how its robustness would be if implemented on real vehicle, is given. Amine Chohra and Ouahiba Azouaoui Copyright © 2011 Amine Chohra and Ouahiba Azouaoui. All rights reserved. Wed, 30 Nov 2011 08:09:37 +0000 http://www.hindawi.com/journals/aans/2011/941426/ For the first time the global dissipativity of a class of cellular neural networks with multipantograph delays is studied. On the one hand, some delay-dependent sufficient conditions are obtained by directly constructing suitable Lyapunov functionals; on the other hand, firstly the transformation transforms the cellular neural networks with multipantograph delays into the cellular neural networks with constant delays and variable coefficients, and then constructing Lyapunov functionals, some delay-independent sufficient conditions are given. These new sufficient conditions can ensure global dissipativity together with their sets of attraction and can be applied to design global dissipative cellular neural networks with multipantograph delays and easily checked in practice by simple algebraic methods. An example is given to illustrate the correctness of the results. Liqun Zhou Copyright © 2011 Liqun Zhou. All rights reserved. Sun, 20 Nov 2011 14:35:06 +0000 http://www.hindawi.com/journals/aans/2011/751908/ We used five years of global solar radiation data to estimate the monthly average of daily global solar irradiation on a horizontal surface based on a single parameter, sunshine hours, using the artificial neural network method. The station under the study is located in Kampala, Uganda at a latitude of 0.19°N, a longitude of 32.34°E, and an altitude of 1200 m above sea level. The five-year data was split into two parts in 2003–2006 and 2007-2008; the first part was used for training, and the latter was used for testing the neural network. Amongst the models tested, the feed-forward back-propagation network with one hidden layer (65 neurons) and with the tangent sigmoid as the transfer function emerged as the more appropriate model. Results obtained using the proposed model showed good agreement between the estimated and actual values of global solar irradiation. A correlation coefficient of 0.963 was obtained with a mean bias error of 0.055 MJ/m2 and a root mean square error of 0.521 MJ/m2. The single-parameter ANN model shows promise for estimating global solar irradiation at places where monitoring stations are not established and stations where we have one common parameter (sunshine hours). Karoro Angela, Ssenyonga Taddeo, and Mubiru James Copyright © 2011 Karoro Angela et al. All rights reserved. Thu, 03 Nov 2011 16:08:47 +0000 http://www.hindawi.com/journals/aans/2011/673016/ This paper introduces some novel models for all steps of a face recognition system. In the step of face detection, we propose a hybrid model combining AdaBoost and Artificial Neural Network (ABANN) to solve the process efficiently. In the next step, labeled faces detected by ABANN will be aligned by Active Shape Model and Multi Layer Perceptron. In this alignment step, we propose a new 2D local texture model based on Multi Layer Perceptron. The classifier of the model significantly improves the accuracy and the robustness of local searching on faces with expression variation and ambiguous contours. In the feature extraction step, we describe a methodology for improving the efficiency by the association of two methods: geometric feature based method and Independent Component Analysis method. In the face matching step, we apply a model combining many Neural Networks for matching geometric features of human face. The model links many Neural Networks together, so we call it Multi Artificial Neural Network. MIT + CMU database is used for evaluating our proposed methods for face detection and alignment. Finally, the experimental results of all steps on CallTech database show the feasibility of our proposed model. Thai Hoang Le Copyright © 2011 Thai Hoang Le. All rights reserved. Tue, 01 Nov 2011 10:45:21 +0000 http://www.hindawi.com/journals/aans/2011/786535/ A damage assessment procedure has been developed using artificial neural network (ANN) for prestressed concrete beams. The methodology had been formulated using the results obtained from an experimental study conducted in the laboratory. Prestressed concrete (PSC) rectangular beams were cast, and pitting corrosion was introduced in the prestressing wires and was allowed to be snapped using accelerated corrosion process. Both static and dynamic tests were conducted to study the behaviour of perfect and damaged beams. The measured output from both static and dynamic tests was taken as input to train the neural network. Back propagation network was chosen for this purpose, which was written using the programming package MATLAB. The trained network was tested using separate test data obtained from the tests. A damage assessment procedure was developed using the trained network, it was validated using the data available in literature, and the outcome is presented in this paper. K. Sumangala and C. Antony Jeyasehar Copyright © 2011 K. Sumangala and C. Antony Jeyasehar. All rights reserved. Wed, 12 Oct 2011 11:29:05 +0000 http://www.hindawi.com/journals/aans/2011/617427/ The Self-Organizing Map (SOM) algorithm is widely used for building topographic maps of data represented in a vectorial space, but it does not operate with dissimilarity data. Soft Topographic Map (STM) algorithm is an extension of SOM to arbitrary distance measures, and it creates a map using a set of units, organized in a rectangular lattice, defining data neighbourhood relationships. In the last years, a new standard for identifying bacteria using genotypic information began to be developed. In this new approach, phylogenetic relationships of bacteria could be determined by comparing a stable part of the bacteria genetic code, the so-called “housekeeping genes.” The goal of this work is to build a topographic representation of bacteria clusters, by means of self-organizing maps, starting from genotypic features regarding housekeeping genes. Massimo La Rosa, Riccardo Rizzo, and Alfonso Urso Copyright © 2011 Massimo La Rosa et al. All rights reserved. Tue, 11 Oct 2011 10:26:51 +0000 http://www.hindawi.com/journals/aans/2011/142054/ This paper explores the possibility of developing a prediction model using artificial neural networks (ANNs), which could be used to estimate monthly average daily direct solar radiation for locations in Uganda. Direct solar radiation is a component of the global solar radiation and is quite significant in the performance assessment of various solar energy applications. Results from the paper have shown good agreement between the estimated and measured values of direct solar irradiation. A correlation coefficient of 0.998 was obtained with mean bias error of 0.005 MJ/m2 and root mean square error of 0.197 MJ/m2. The comparison between the ANN and empirical model emphasized the superiority of the proposed ANN prediction model. The application of the proposed ANN model can be extended to other locations with similar climate and terrain. James Mubiru Copyright © 2011 James Mubiru. All rights reserved. Sun, 25 Sep 2011 08:26:41 +0000 http://www.hindawi.com/journals/aans/2011/453169/ The increased complexity of plants and the development of sophisticated control systems have encouraged the parallel development of efficient rapid fault detection and isolation (FDI) systems. FDI in industrial system has lately become of great significance. This paper proposes a new technique for short time fault detection and diagnosis in nonlinear dynamic systems with multi inputs and multi outputs. The main contribution of this paper is to develop a FDI schema according to reference models of fault-free and faulty behaviors designed with neural networks. Fault detection is obtained according to residuals that result from the comparison of measured signals with the outputs of the fault free reference model. Then, Euclidean distance from the outputs of models of faults to the measurements leads to fault isolation. The advantage of this method is to provide not only early detection but also early diagnosis thanks to the parallel computation of the models of faults and to the proposed decision algorithm. The effectiveness of this approach is illustrated with simulations on DAMADICS benchmark. Yahia Kourd, Dimitri Lefebvre, and Noureddine Guersi Copyright © 2011 Yahia Kourd et al. All rights reserved. Sun, 04 Sep 2011 10:58:56 +0000 http://www.hindawi.com/journals/aans/2011/249136/ A novel and effective approach to synchronization analysis of neural networks is investigated by using the nonlinear operator named the generalized Dahlquist constant and the general intermittent control. The proposed approach offers a design procedure for synchronization of a large class of neural networks. The numerical simulations whose theoretical results are applied to typical neural networks with and without delayed item demonstrate the effectiveness and feasibility of the proposed technique. Zhang Qunli Copyright © 2011 Zhang Qunli. All rights reserved.