Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
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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 effects of various parameters, such as number of training pairs, number of Kohonen units, and the number of winning nodes are researched. The work has been compared with others and the results are much better and overall performance of the net is improved. The purpose of using Genetic Algorithms is mainly to investigate its efficiency in the net response.