A Modular Multi-Net System consist on some networks which solve partially a problem. The original problem has been decomposed into subproblems and each network focuses on solving a subproblem. The Mixture of Neural Networks consist on some expert networks which solve the subproblems and a gating network which weights the outputs of the expert networks. The expert networks and the gating network...

A new class of methods for training multilayer feedforward neural networks is proposed. The proposed class of methods draws from methods for solving initial value problems of ordinary differential equations, and belong to the subclass of trajectory methods. The training of a multilayer feedforward neural network is equivalent to the minimization of the network’s error function with respect to t...

This paper presents a neural network with a novel neuron model. In this model, the neuron has two activation functions and exhibits a node-to-node relationship in the hidden layer. This neural network provides better performance than a traditional feedforward neural network, and fewer hidden nodes are needed. The parameters of the proposed neural network are tuned by a genetic algorithm with ar...

The paper addresses the problem of deening a neural system which combines pieces of independent information available in both the data and parameters spaces. The problem is approached in the framework of the probabilistic interpretation of neural modelling: in order to take into account the indetermination associated to the training process, a distribution in the weight space is associated to e...

The two well-known neural network, Hopfield networks and Radial Basis Function networks, have different structures and characteristics. Hopfield neural network and RBF neural network are two of the most commonly-used types of feedback networks and feedforward networks respectively. This study gives an overview for Hopfield neural network and RBF neural network in architectures, the learning pro...

In this paper, several neural network and statistical learning approaches are proposed that learn to make human like decisions for the job assignment problem of the US Navy. Comparison study of Feedforward Neural Networks (FFNN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine (SVM) and Adaptive Bayes (AB) classifier with Generalized Estimation Equation (GEE) is provided. ...

A visual system not only needs to recognize a stimulus, it also needs to find the location of the stimulus. In this paper, we present a neural network model that is able to generalize its ability to identify objects to new locations in its visual field. The model consists of a feedforward network for object identification and a feedback network for object location. The feedforward network first...

The emerging technology of artificial neural networks has been successfully used in a variety of different areas such as fault detection, control, signal processing, and many others. This paper presents the general design considerations of feedforward artificial neural networks to perform motor fault detection. The paper first discusses a few noninvasive fault detection techniques, including th...

Voiced-unvoiced-silence classhation of speech was made using a multilayer feedforward network. The network was evaluated and compared to a maximum-likelihood classiller. Results indicated that the network performance was not significantly affected by the size of training set and a classification rate as high as 96% was obtained.