In the present scenario, most of the information is available in the form of audio and video. Accumulation of this data is observed to be exponential. Therefore for effective handling of this data, powerful indexing and retrieval mechanisms are necessary. For indexing the audio data, the basic components of audio such as speech, music and speech with background environment are to be characteriz...

Online gradient algorithm has been widely used as a learning algorithm for feedforward neural networks training. Penalty is a common and popular method for improving the generalization performance of networks. In this paper, a convergence theorem is proved for the online gradient learning algorithm with penalty, a term proportional to the magnitude of the weights. The monotonicity of the error ...

Study of visual evoked potential (VEP) is one of the utilized methods in clinical diagnosis of ophthalmology and neurological disorders. The automatic detection of VEP spectral components is an important tool in the diagnosis of mental activity. This paper presents a novel computational approach using feedforward neural network to identify abnormal subjects from changes in spectral components. ...

Forecasting the short run behavior of foreign exchange rates is a challenging problem that has attracted considerable attention. High frequency financial data are typically characterized by noise and non–stationarity. In this work we investigate the profitability of a forecasting methodology based on unsupervised clustering and feedforward neural networks and compare its performance with that o...

There is presented the design of the feedforward neural network for calculation of coefficients of the robot model. Proposed method distinguishes the degrees of freedom and improves the performance of the network using information about the control signals. A numerical example for calculation of the neural network model of Puma 560 robot is presented.

The paper deals with a discrete-time recurrent neural network designed with dynamic neuron models. Dynamics are reproduced within each single neuron, hence the considered network is a locally recurrent globally feedforward. In the paper, conditions for global stability of the neural network considered are derived using the Lyapunov’s second method.

Recently, neural networks have become a very important method in the field of medical diagnostic. The objective of this work is to diagnose hepatitis disease by using different neural network architectures. Standard feedforward networks and a hybrid network were investigated. Results obtained show that especially the hybrid network can be successfully used for diagnosing of hepatitis.

This paper presents the parallel architecture of the conjugate gradient learning algorithm for the feedforward neural networks. The proposed solution is based on the high parallel structures to speed up learning performance. Detailed parallel neural network structures are explicitly shown.

Ventricular fibrillation is a cardiac arrhythmia that can result in sudden death. Understanding and treatment of this disorder would be improved if patterns of electrical activation could be accurately identified and studied during fibrillation. A feedforward artificial neural network using backpropagation was trained with the Rule-Based Method and the Current Source Density Method to identify ...

We compare the performance of a speci®cally designed feedforward arti®cial neural network with one layer of hidden units to the K-means clustering technique in solving the problem of cluster-based market segmentation. The data set analyzed consists of usages of brands (product category: household cleaners) in di€erent usage situations. The proposed feedforward neural network model results in a ...