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Civil-Comp Proceedings
ISSN 1759-3433 CCP: 74
PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON THE APPLICATION OF ARTIFICIAL INTELLIGENCE TO CIVIL AND STRUCTURAL ENGINEERING Edited by: B.H.V. Topping and B. Kumar
Paper 36
Genetic Algorithm Trained Counter-Propagation Neural Net in Structural Optimization A. Iranmanesh and M. Fahimi
Department of Civil Engineering, Shahid Bahonar University of Kerman, Iran Full Bibliographic Reference for this paper
A. Iranmanesh, M. Fahimi, "Genetic Algorithm Trained Counter-Propagation Neural Net in Structural Optimization", in B.H.V. Topping, B. Kumar, (Editors), "Proceedings of the Sixth International Conference on the Application of Artificial Intelligence to Civil and Structural Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 36, 2001. doi:10.4203/ccp.74.36
Keywords: neural network, counter-propagation, genetic algorithm, structural optimization.
Summary
The main objective of this research is to improve the efficiency of the Counter-
Propagation Neural net response in structural analysis and optimization. To achieve
this, a modification has been made on the learning coefficients, which resulted in a
higher performance. The net is trained by two different procedures, random and
genetic generation of training pairs. To examine the efficiency of the net, different
examples has been investigated. The results of genetic trained Counter-Propagation
net and the random trained one are compared with the exact solution.. The purpose
of using Genetic Algorithms (GAs) is mainly to investigate its efficiency in the net
response.
Counter-Propagation neural network is a combination of two well-known
algorithms: the self-organizing map of Kohonen and the Grossberg outstars. In the
process of training, the weight matrices are computed internally. To define criteria
for proximity of weight matrices in the Kohonen layer and input vector, parameter
For the learning coefficient, Hecht-Neilsen suggests a number in the range ![]() ![]()
parameters ![]() ![]() ![]() ![]() ![]()
a,b : Learning coefficients; N: Number of iterations Genetic Algorithms are computationally simple, but powerful in their search for improvement, and they are not limited by restrictive assumptions about the search space, such as continuity or existence of derivatives. Genetic Algorithms are search procedures based on the mechanics of natural genetic and natural selection. They combine the concept of the artificial of the artificial survival of the fittest with genetic operators abstracted from nature to form a powerful search mechanism[4,5]. The main objective is error reduction of the Counter-Propagation Neural net (CPN) response by application of the Genetic Algorithms. Genetic operators are applied so that the net is improved and error on the output units is reduced. By error minimizing of the net response and generating proper genetic training pairs, the overall performance of the net as compared with random training pairs is improved. The optimim design of a three spans girder with uniform load a cocentrated load at each mid span was considered. The results show that modifying parametes a and b, the learing coefficients of the Kohonen and the Grossberg layers will improve the net efficiency. To investigate the effects of other alternatives on the CPN neural net response, GAs are used in the training process. Based on the minimization of the error function and application of the genetic operators, Reproduction, Crossover, and Mutation proper net response is achieved. Stress analysis of plane and space trusses is considered, the overall performance of the net has been improved. References
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