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2nd New Zealand Two-Stream International Conference on Artificial Neural Networks and Expert Systems (ANNES '95)
Genetic Optimization of Control Parameters of a Neural Network
Dunedin, New Zealand
November 20-November 23
ISBN: 0-8186-7174-2
Belinda Choi, La Trobe University, Bendigo
Kevin Bluff, Swinburne University of Technology, Melbourne
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.
Citation:
Belinda Choi, Kevin Bluff, "Genetic Optimization of Control Parameters of a Neural Network," annes, pp.174, 2nd New Zealand Two-Stream International Conference on Artificial Neural Networks and Expert Systems (ANNES '95), 1995
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