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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/26078
- Genetic optimisation of control parameters of a neural network
- Choi, Belinda; Bluff, Kevin
- 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 Back Propagation (SBP) and a Fuzzy Back Propagation (FBP) network for different applications.
- Publication type
- Conference paper
- Research centre
- Swinburne University of Technology. Department of Computer Science
- Proceedings Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems (ANNES 95), Dunedin, New Zealand, 20-23 November 1995, p. 174-177
- Publication year
- ANNs; Artificial neural networks; Fuzzy back propagation networks; Genetic algorithms; Genetic optimisation; Neural net architecture; Search technique; Standard back propagation
- Publisher URL
- Copyright © 1995 IEEE.
- Peer reviewed