The feed forward neural network which is a model of the cerebral neural network has in-built fault tolerance. The conventional back-propagation algorithm reduces errors between the learning examples and the output of a multilayer neural network (MNN). However, it is not assured that the MNN behaves in the same manner when faults occur. For these reasons the study of fault tolerance in artificia...

ARTIFICIAL neural network (NN) architectures have been recognized for a number of years as a powerful technology for solving real-world image processing problems. The primary purpose of this special issue is to demonstrate some recent success in solving image processing problems and hopefully to motivate other image processing researchers to utilize this technology to solve their real-world pro...

malting is a complex biotechnological process that includes steeping; germination and drying of cereal grains under controlled conditions of temperature and humidity. in this research malting process parameters were predict by modular neural network with different activation function included, logsig-logsig, tanh-tanh, logsigtanh, logsig-identity and tanh-identity. steeping time (x1) and germin...

The neuromechanical control principles of animal locomotion provide good insights for the development of bio-inspired legged robots for walking on challenging surfaces. Based on such principles, we developed a neuromechanical controller consisting of a modular neural network (MNN) and of virtual agonist-antagonist muscle mechanisms (VAAMs). The controller allows for variable compliant leg motio...

Abstract— By adding a negative self-feedback to the maximum neural network (MNN), we propose a new parallel algorithm for the broadcast scheduling problem in packet radio networks. The developed algorithm introduces richer and more flexible nonlinear dynamics, and can prevent the network from getting stuck at local minima. The algorithm is verified by simulating 13 different packet radio networks.

Digital images require large amounts of memory for storage. Thus, the transmission of an image from one computer to another can be very time consuming. By using data compression techniques, it is possible to remove some of the redundant information contained in images, requiring less storage space and less time to transmit. Artificial Neural networks can be used for the purpose of image compres...

In this paper we present a new method for voice disorders classification based on a gammachirp wavelet transform and multilayer neural network (MNN). The processing algorithm is based on a hybrid technique which uses the gammachirp wavelets coefficients as input of the MNN. The training step uses a speech database of several pathological and normal voices collected from the national hospital “R...

The decisions made by admission control algorithms are based on the availability of network resources viz. bandwidth, energy, memory buffers, etc., without degrading the Quality-of-Service (QoS) requirement of applications that are admitted. In this paper, we present an energy-aware admission control (EAAC) scheme which provides admission control for flows in an ad hoc network based on the know...

This paper proposes a novel approach for an optimal multi-objective optimization for VLSI implementation of Artificial Neural Network (ANN) which is area-power-speed efficient and has high degree of accuracy and dynamic range. A VLSI implementation of feed forward neural network in floating point arithmetic IEEE-754 single precision 32 bit format is presented that makes the use of digital weigh...