Strategic Application of Feedforward Neural Networks to Large-Scale Classification

Entropic Analysis and Incremental Synthesis of Multilayered Feedforward Neural Networks

Neural network architecture optimization is often a critical issue, particularly when VLSI implementation is considered. This paper proposes a new minimization method for multilayered feedforward ANNs and an original approach to their synthesis, both based on the analysis of the information quantity (entropy) flowing through the network. A layer is described as an information filter which selec...

Application of Two Methods of Artificial Neural Network MLP, RBF for Estimation of Wind of Sediments (Case Study: Korsya of Darab Plain)

The lack of sediment gauging stations in the process of wind erosion, caused of estimate of sediment be process of necessary and important. Artificial neural networks can be used as an efficient and effective of tool to estimate and simulate sediments. In this paper two model multi-layer perceptron neural networks and radial neural network was used to estimate the amount of sediment in Korsya o...

Application of Artificial Neural Networks in a Two-step Classification for Acute Lymphocytic Leukemia Diagnosis by Blood Lamella Images

Introduction: This study aimed to present a system based on intelligent models that can enhance the accuracy of diagnostic systems for acute leukemia. The three parts including preprocessing, feature extraction, and classification network are considered as associated series of actions. Therefore, any dysfunction or poor accuracy in each part might lead in general dysfunction of...

Numerical solution of fuzzy linear Fredholm integro-differential equation by \fuzzy neural network

In this paper, a novel hybrid method based on learning algorithmof fuzzy neural network and Newton-Cotesmethods with positive coefficient for the solution of linear Fredholm integro-differential equation of the second kindwith fuzzy initial value is presented. Here neural network isconsidered as a part of large field called neural computing orsoft computing. We propose alearning algorithm from ...

Decentralized Interaction Estimators in Large Scale Power Systems with Neural Networks.