Title :
Genetic optimisation of control parameters of a neural network
Author :
Choi, Belinda ; Bluff, Kevin
Author_Institution :
Dept. of Inf. Technol., La Trobe Univ., Bundoora, Vic., Australia
Abstract :
One of the shortcomings of artificial neural networks (ANNs) is the difficulty in predicting the best control parameters for a certain application. The number of combinations of parameters is very large. This makes it very inefficient and expensive to search manually by trial and error. Genetic algorithms (GAs) are an excellent and effective search technique suitable for this task. This paper describes an investigation into the use of GAs to automate the choice of parameters in both a standard backpropagation (SBP) and a fuzzy backpropagation (FBP) network for different applications
Keywords :
backpropagation; fuzzy neural nets; genetic algorithms; neural net architecture; search problems; control parameters; fuzzy backpropagation network; genetic algorithms; genetic optimisation; neural network; neural network architecture; search technique; standard backpropagation network; Artificial neural networks; Frequency; Fuzzy sets; Genetic algorithms; Information technology; Neural networks; Optimal control; Pattern recognition; Space technology; Training data;
Conference_Titel :
Artificial Neural Networks and Expert Systems, 1995. Proceedings., Second New Zealand International Two-Stream Conference on
Conference_Location :
Dunedin
Print_ISBN :
0-8186-7174-2
DOI :
10.1109/ANNES.1995.499466
