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Optimization Design based on BP Neural Network and GA Method

机译:基于BP神经网络和遗传算法的优化设计。

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This study puts forward one kind optimization controlling solution method on complicated system. At first modeling using neural network then adopt the real data to structure the neural network model of pertinence, make the parameter to seek to the neural network model excellently by mixing GA finally, thus got intelligence to the complicated system to optimize and control. The method can identify network configuration and network training methods. By adopting the number coding and effectively reducing the network size and the network convergence time, increase the network training speed. The study provides this and optimizes relevant MATLAB procedure which controls the method, so long as adjust a little to the concrete problem, can believe this procedure well the optimization of the complicated system controls the problem in the reality of solving.
机译:提出了一种针对复杂系统的优化控制解决方案。首先使用神经网络进行建模,然后采用实际数据构建相关的神经网络模型,最后通过混合遗传算法使参数寻优至神经网络模型,从而对复杂的系统进行智能优化和控制。该方法可以识别网络配置和网络训练方法。通过采用数字编码,有效减少网络规模和网络收敛时间,提高网络训练速度。研究提供了该方法并优化了相关的MATLAB程序控制方法,只要对具体问题进行一点调整,就可以相信该程序很好地解决了复杂系统的优化控制问题。

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