The Parallel Approach to the Conjugate Gradient Learning Algorithm for the Feedforward Neural Networks
نویسندگان
چکیده
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.
منابع مشابه
Handwritten Character Recognition using Modified Gradient Descent Technique of Neural Networks and Representation of Conjugate Descent for Training Patterns
The purpose of this study is to analyze the performance of Back propagation algorithm with changing training patterns and the second momentum term in feed forward neural networks. This analysis is conducted on 250 different words of three small letters from the English alphabet. These words are presented to two vertical segmentation programs which are designed in MATLAB and based on portions (1...
متن کاملA parallel genetic/neural network learning algorithm for MIMD shared memory machines
A new algorithm is presented for training of multilayer feedforward neural networks by integrating a genetic algorithm with an adaptive conjugate gradient neural network learning algorithm. The parallel hybrid learning algorithm has been implemented in C on an MIMD shared memory machine (Cray Y-MP8/864 supercomputer). It has been applied to two different domains, engineering design and image re...
متن کاملDeterministic convergence of conjugate gradient method for feedforward neural networks
Conjugate gradient methods have many advantages in real numerical experiments, such as fast convergence and low memory requirements. This paper considers a class of conjugate gradient learning methods for backpropagation (BP) neural networks with three layers. We propose a new learning algorithm for almost cyclic BP neural networks based on PRP conjugate gradient method. We then establish the d...
متن کاملA conjugate gradient based method for Decision Neural Network training
Decision Neural Network is a new approach for solving multi-objective decision-making problems based on artificial neural networks. Using inaccurate evaluation data, network training has improved and the number of educational data sets has decreased. The available training method is based on the gradient decent method (BP). One of its limitations is related to its convergence speed. Therefore,...
متن کاملNumerical 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 ...
متن کامل