At present, it is obvious that different sections of nervous system utilize different methods for information coding. Primary afferent signals in most cases are represented in form of spike trains using a combination of rate coding and population coding while there are clear evidences that temporal coding is used in various regions of cortex. In the present paper, it is shown that conversion between these two coding schemes can be performed under certain conditions by a homogenous chaotic neural network. Interestingly, this effect can be achieved without network training and synaptic plasticity.

Mikhail V. Kiselev

Homogenous Chaotic Network Serving as a Rate/Population Code to Temporal Code Converter

Topics identification (TI) is the process that consists in determining the main themes present in natural language documents. The current TI modeling paradigm aims at acquiring semantic information from statistic properties of large text datasets. We investigate the mental mechanisms responsible for the identification of topics in a single document given existing knowledge. Our main hypothesis is that topics are the result of accumulated neural activation of loosely organized information stored in long-term memory (LTM). We experimentally tested our hypothesis with a computational model that simulates LTM activation. The model assumes activation decay as an unavoidable phenomenon originating from the bioelectric nature of neural systems. Since decay should negatively affect the quality of topics, the model predicts the presence of short-term memory (STM) to keep the focus of attention on a few words, with the expected outcome of restoring quality to a baseline level. Our experiments measured topics quality of over 300 documents with various decay rates and STM capacity. Our results showed that accumulated activation of loosely organized information was an effective mental computational commodity to identify topics. It was furthermore confirmed that rapid decay is detrimental to topics quality but that limited capacity STM restores quality to a baseline level, even exceeding it slightly.

Louis Massey

Mental Mechanisms for Topics Identification

Occurrence of faults in wind energy conversion systems (WECSs) is inevitable. In order to detect the occurred faults at the appropriate time, avoid heavy economic losses, ensure safe system operation, prevent damage to adjacent relevant systems, and facilitate timely repair of failed components; a fault detection system (FDS) is required. Recurrent neural networks (RNNs) have gained a noticeable position in FDSs and they have been widely used for modeling of complex dynamical systems. One method for designing an FDS is to prepare a dynamic neural model emulating the normal system behavior. By comparing the outputs of the real system and neural model, incidence of the faults can be identified. In this paper, by utilizing a comprehensive dynamic model which contains both mechanical and electrical components of the WECS, an FDS is suggested using dynamic RNNs. The presented FDS detects faults of the generator's angular velocity sensor, pitch angle sensors, and pitch actuators. Robustness of the FDS is achieved by employing an adaptive threshold. Simulation results show that the proposed scheme is capable to detect the faults shortly and it has very low false and missed alarms rate.

Nasser Talebi

Mohammad Ali Sadrnia

Ahmad Darabi

Robust Fault Detection of Wind Energy Conversion Systems Based on Dynamic Neural Networks

Granular computing classification algorithms are proposed based on distance measures between two granules from the view of set. Firstly, granules are represented as the forms of hyperdiamond, hypersphere, hypercube, and hyperbox. Secondly, the distance measure between two granules is defined from the view of set, and the union operator between two granules is formed to obtain the granule set including the granules with different granularity. Thirdly the threshold of granularity determines the union between two granules and is used to form the granular computing classification algorithms based on distance measures (DGrC). The benchmark datasets in UCI Machine Learning Repository are used to verify the performance of DGrC, and experimental results show that DGrC improved the testing accuracies.

Hongbing Liu

Chunhua Liu

Chang-an Wu

Granular Computing Classification Algorithms Based on Distance Measures between Granules from the View of Set

Forecasting stock market has been a difficult job for applied researchers owing to nature of facts which is very noisy and time varying. However, this hypothesis has been featured by several empirical experiential studies and a number of researchers have efficiently applied machine learning techniques to forecast stock market. This paper studied stock prediction for the use of investors. It is always true that investors typically obtain loss because of uncertain investment purposes and unsighted assets. This paper proposes a rough set model, a neural network model, and a hybrid neural network and rough set model to find optimal buy and sell of a share on Dhaka stock exchange. Investigational findings demonstrate that our proposed hybrid model has higher precision than the single rough set model and the neural network model. We believe this paper findings will help stock investors to decide about optimal buy and/or sell time on Dhaka stock exchange.

Shipra Banik

A. F. M. Khodadad Khan

Mohammad Anwer

Hybrid Machine Learning Technique for Forecasting Dhaka Stock Market Timing Decisions

Gold price forecasting has been a hot issue in economics recently. In this work, wavelet neural network (WNN) combined with a novel artificial bee colony (ABC) algorithm is proposed for this gold price forecasting issue. In this improved algorithm, the conventional roulette selection strategy is discarded. Besides, the convergence statuses in a previous cycle of iteration are fully utilized as feedback messages to manipulate the searching intensity in a subsequent cycle. Experimental results confirm that this new algorithm converges faster than the conventional ABC when tested on some classical benchmark functions and is effective to improve modeling capacity of WNN regarding the gold price forecasting scheme.

Bai Li

Research on WNN Modeling for Gold Price Forecasting Based on Improved Artificial Bee Colony Algorithm

Cuckoo search (CS) is a new robust swarm intelligence method that is based on the brood parasitism of some cuckoo species. In this paper, an improved hybrid encoding cuckoo search   algorithm (ICS) with greedy strategy is put forward for solving 0-1 knapsack problems. First of all, for solving binary optimization problem with ICS, based on the idea of individual hybrid encoding, the cuckoo search over a continuous space is transformed into the synchronous evolution search over discrete space. Subsequently, the concept of confidence interval (CI) is introduced; hence, the new position updating is designed and genetic mutation with a small probability is introduced. The former enables the population to move towards the global best solution rapidly in every generation, and the latter can effectively prevent the ICS from trapping into the local optimum. Furthermore, the greedy transform method is used to repair the infeasible solution and optimize the feasible solution. Experiments with a large number of KP instances show the  effectiveness of the proposed algorithm and its ability to achieve good quality solutions.

Yanhong Feng

Ke Jia

Yichao He

An Improved Hybrid Encoding Cuckoo Search Algorithm for 0-1 Knapsack Problems

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Computational Intelligence and Neuroscience is a forum for the interdisciplinary field of neural computing, neural engineering and artificial intelligence. The journal’s focus is on intelligent systems for computational neuroscience.

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Chief Editor, Professor Cichocki, engages in world-leading research in the field of artificial intelligence and biomedical applications of advanced data analytics technologies.

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Computational Intelligence and Neuroscience - Table of contents 2014

Computational Intelligence and Neuroscience | Hindawi

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Computational Intelligence and Neuroscience is a forum for the interdisciplinary field of neural computing, neural engineering and artificial intelligence. The journal’s focus is on intelligent systems for computational neuroscience.

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Computational Intelligence and Neuroscience

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Computational Intelligence and Neuroscience