Combinations of Gradient and Evolutionary Methods for Neural Network Weights Adaptation
نویسندگان
چکیده
In this paper, the use of evolutionary computation for feedforward neural network learning is discussed. The aim is to combine benefits of evolutionary and gradient learning into two methods: BP/ES and ES/LMS. We compared experimental results obtained on XOR data by back-propagation algorithm, evolution strategies, and combined approach.
منابع مشابه
Solving Fuzzy Equations Using Neural Nets with a New Learning Algorithm
Artificial neural networks have the advantages such as learning, adaptation, fault-tolerance, parallelism and generalization. This paper mainly intends to offer a novel method for finding a solution of a fuzzy equation that supposedly has a real solution. For this scope, we applied an architecture of fuzzy neural networks such that the corresponding connection weights are real numbers. The ...
متن کاملSolving Fuzzy Equations Using Neural Nets with a New Learning Algorithm
Artificial neural networks have the advantages such as learning, adaptation, fault-tolerance, parallelism and generalization. This paper mainly intends to offer a novel method for finding a solution of a fuzzy equation that supposedly has a real solution. For this scope, we applied an architecture of fuzzy neural networks such that the corresponding connection weights are real numbers. The ...
متن کاملPrediction of Surface Roughness by Hybrid Artificial Neural Network and Evolutionary Algorithms in End Milling
Machining processes such as end milling are the main steps of production which have major effect on the quality and cost of products. Surface roughness is one of the considerable factors that production managers tend to implement in their decisions. In this study, an artificial neural network is proposed to minimize the surface roughness by tuning the conditions of machining process such as cut...
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 کاملUtilizing a new feed-back fuzzy neural network for solving a system of fuzzy equations
This paper intends to offer a new iterative method based on articial neural networks for finding solution of a fuzzy equations system. Our proposed fuzzied neural network is a ve-layer feedback neural network that corresponding connection weights to output layer are fuzzy numbers. This architecture of articial neural networks, can get a real input vector and calculates its corresponding fuzzy o...
متن کامل