Self-adaptation Optimize BP Neural Network Design Based on the Genetic Algorithms
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Abstract:
BP(Back Propagation) Neural networks is in the presence of the local optimization in the Neural networks training.The algorithm have slow convergence and the local convergence problem which impact the neural networks work performance.In order to cover these shortcomings and solves the size's hugeness and the low efficiency of the net problem in the traditional NN designing,the action principles of BP-Neural network's structure are analyzed,and a new method is formed which is confirmed from the Enhance genetic algorithms(EGA).The method can identify network configuration and network training methods.By adopting the number coding,self-adaptable multi-point variations operation,this method can effectively reduce the network size and the network convergence time,increase the network training speed.Tomatoes disease diagnosis examples illustrate the feasibility of this approach.
