Training product unit neural networks with genetic algorithms | Zendy

David J. Janson | Zendy; James F. Frenzel | Zendy

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<title>Training product unit neural networks with genetic algorithms</title>

Author(s) -

David J. Janson,

James F. Frenzel

Publication year - 1992

Publication title -

proceedings of spie, the international society for optical engineering/proceedings of spie

Language(s) - English

Resource type - Conference proceedings

SCImago Journal Rank - 0.192

H-Index - 176

eISSN - 1996-756X

pISSN - 0277-786X

DOI - 10.1117/12.139958

Subject(s) - artificial neural network , backpropagation , genetic algorithm , maxima and minima , computer science , product (mathematics) , algorithm , artificial intelligence , machine learning , mathematics , mathematical analysis , geometry

This paper discusses the training of product neural networks using genetic algorithms. Two unusual techniques are combined; product units are employed in addition to the traditional summing units and a genetic algorithm is used to train the network rather than using backpropagation. As an example, a neural network is trained to calculate the optimum width of transistors in a CMOS switch. It is shown how local minima can affect the performance of a genetic algorithm, and one method of overcoming this is presented.

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