In this study, we propose a novel method for reducing the attributes of sensory datasets using Master–Slave Synchronization of chaotic Lorenz Systems (DPSMS). As part of the performance testing, three benchmark datasets and one Electronic Nose (EN) sensory dataset with 3 to 13 attributes were presented to our algorithm to be projected into two attributes. The DPSMSprocessed datasets were then u...

This paper presents a new learning algorithm, Multi-Population Cooperative Particle Swarm Optimizer (MCPSO), for neural network training. MCPSO is based on a master-slave model, in which a population consists of a master group and several slave groups. The slave groups execute a single PSO or its variants independently to maintain the diversity of particles, while the master group evolves based...

This paper presents analysis of a modified Feed Forward Multilayer Perceptron (FMP) by inserting an ARMA (Auto Regressive Moving Average) model at each neuron (processor node) with the Backp ropagation learning algorithm. The stability analysis is presented to establish the convergence theory of the Back propagation algorithm based on the Lyapunov function. Furthermore, the analysis extends the...

Once the behaviour of particular brain circuits has been analyzed, wehave added up some of these patterns to Artificial Neural Networks; thus a newhybrid learning method has emerged. In order to find the best solution to agiven problem, this method combines the use of Genetic Algorithms withparticular changes to connection weights basOOin the behaviour observOO in thebrain c...

Retrieval of images based on low level visual features such as color, texture and shape have proven to have its own set of limitations under different conditions. As the number and size of image databases grows, accurate and efficient content-based image retrieval systems become increasingly important in business and in the everyday lives of people around the world. In this paper we describe a ...

Recently, several algorithms have been proposed for using neural networks in dynamic analysis of small structural systems, and also constructing adaptive material modeling subroutines with the aim of their implementation in finite element computer programs. In these algorithms, the neural networks are trained based on the data obtained from tests at structural or material levels. In this paper,...

In this paper, an Artificial Neural Networks (ANN) model has been developed to design the multilayer Rectangular microstrip patch. In the design procedure, synthesis ANN model is used as feed forward network to calculate the resonant frequency. Analysis ANN model is used as the reverse side of the problem to calculate the antenna dimension. The network is trained with the data obtained from mea...

This paper presents Neural Network (NN) model of Polymer Electrolyte Membran (PEM) Fuel Cell for electric vehicle. The NN model simplifies the conventional model that considered thermodynamics, electrochemistry, hydrodynamics and mass transfer theory. The NN has a multilayer feed forward network structure and is trained using a back propagation learning rule. The NN model is used to predict the...

We present in this paper a system of English handwriting recognition based on 40-point feature extraction of the character. Basically an off-line handwritten alphabetical character recognition system using multilayer feed forward neural network has been described in our work. Firstly a new method, called, 40-point feature extraction is introduced for extracting the features of the handwritten a...

Random cost simulations were introduced as a method to investigate optimization problems in systems with con icting constraints. Here I study the approach in connection with the training of a feed-forward multilayer perceptron, as used in high energy physics applications. It is suggested to use random cost simulations for generating a set of selected con gurations. On each of those nal minimiza...