In this paper, we will describe several extensions to our earlier work, utilizing a segment-based approach. We will formulate our segmental framework and report our study on the use of multi-layer perceptrons for detection and classification of phonemes. We will also examine the outputs of the network, and compare the network performance with other classifiers. Our investigation is performed wi...

In this paper, a multilayer perceptron guided encryption/decryption (STMLP) in wireless communication has been proposed for exchange of data/information. Multilayer perceptron transmitting systems at both ends generate an identical output bit and the network are trained based on the output which is used to synchronize the network at both ends and thus forms a secret-key at end of synchronizatio...

For decades, scientists have studied the blast wave profile produced by an explosive detonation. Based on a significant amount of experimental data, propagation has been predicted under given parameters. However, most studies only looked at central point initiation for spherical form explosives. The purpose this research is to compare prediction performance peak overpressure based type explosiv...

Metaplasticity property of biological synapses is interpreted in this paper as the concept of placing greater emphasis on training patterns that are less frequent. A novel implementation is proposed in which, during the network learning phase, a priority is given to weight updating of less frequent activations over the more frequent ones. Modeling this interpretation in the training phase, the ...

Several neural network architectures have been developed over the past several years. One of the most popular and most powerful architectures is the multilayer perceptron. This architecture will be described in detail and recent advances in training of the multilayer perceptron will be presented. Multilayer perceptrons are trained using various techniques. For years the most used training metho...

The problem of selecting the best set of features for target recognition using a multilayer perceptron is addressed in this paper. A technique has been developed which analyzes the weights in a multilayer perceptron to determine which features the network finds important and which are unimportant. A brief introduction to the use of multilayer perceptrons for classification and the training rule...

We present a perceptron model with processing units consisting of coupled phase oscillators. The processing units are able to compute the input signals through a high order synapse mechanism. We show how a network of these elements can be used in analogy to the classical multilayer feedforward neural network. The main characteristics of the classical multilayer perceptron model are conserved, a...

This paper describes the implementation of a Multilayer Perceptron Neural Network for handwritten digit recognition. The paper provides the knowledge about previously implemented techniques for same application and also provides their merits and demerits. In this paper Optimal Multilayer Perceptron.Neural network has been designed to reduce complexity of the circuit. Results are also stated to ...

In recent years, many attempts have been made to predict the behavior of bonds, currencies, stocks, or stock markets. In this paper, the StandardlkPoors 500 Index is modeled using different neural network classification architectures. Most previous experiments used multilayer perceptrons for stock market forecasting. In this paper, a multilayer perceptron architecture and ZL probabilistic neura...