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混沌遗传算法优化管网状态神经网络模型
张磊
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针对BP 算法易陷入局部最优,提出将一种新的混沌遗传算法(CGA) 用于全局优化给水管网状态神经网络模型的初始权阈值. 该算法将混沌搜索与自适应遗传算法相结合,根据混沌运动的初值敏感性、内在随机性以及遍历性的特点,通过混沌映射搜索自适应遗传算法的较优初始种群,并利用自适应遗传算法进一步寻优,对混沌映射和遗传进化进行循环计算直至达到最大进化代数,最终获得BP 模型的较优权阈值. 实例分析结果表明,与自适应遗传算法(A GA) 相比,该算法搜索稳健,全局搜索能力强,并且新算法优化模型具有更高的预测性能.
As back2propagation (BP) neural network suffer s f rom the existence of many local minima , a
novel chaos genetic algorit hm ( CGA) combining chaotic search wit h self2adaptive genetic algorit hm(A GA) was proposed for globally optimizing t he initial weight and t hreshold of neural network2based state model for pipe network. Wit h the characteristics of sensitive dependence on initial conditions , int rinsic stochastic property and ergodicity of chaotic motion , chaotic map was used to search the optimal initial pop ulation of A GA , t hen A GA was employed to optimize t he weight and threshold. The operations of chaotic map and A GA evolution were performed until CGA reached t he maximum generation , and t he optimal weight and t hreshold of BP neural network was achieved. The case analysis shows t hat CGA has bet ter global convergence and st ronger running robustness t han A GA , and t hat the model optimized by CGA has higher forecasting performance.
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