【摘要】 粒子群算法是解决非线性、不可微问题的一种优秀算法。利用混沌映射的随机性与遍历性,引入防早熟机制,加强了粒子群的全局搜索能力,但该算法仍然容易在进化后期出现速度变慢现象。BP神经网络具有很强的非线性处理能力和逼近能力,但BP算法是基于梯度下降的方法,存在容易陷入局部最优及初值敏感的缺点。将两种算法优势互补,构建了一种混沌粒子群优化BP神经网络(CPSO-BPNN)的算法。该算法应用到开关磁阻发电机(SRG)的非线性建模中,建模效果表明CPSO-BPNN算法的泛化能力很强,可以比较完美地表达开关磁阻发电机的磁链和转矩特性。更多还原

【Abstract】 A new algorithm,which is named as chaotic hybrid particle swarm optimization BP neural network(CPSO-BPNN),is proposed.CPSO integrates chaotic mechanism for its ergodicity,stochastic property,and regularity,which enhance the global exploitation of PSO.BP neural network has strong nonlinear approximation ability,but its nature of gradient descent algo-rithm determines that it’s easy to fall into local optimum and sensitive to the initial values.The CPSO-BPNN algorithm is in order to take the advantages of the two algorithms.It is applied to the non-linear modeling of Switched Reluctance Gener-ator(SRG).The efforts suggest that the IPSO-BPNN model has strong generalization ability,it can expression the flux and torque characteristics of SRG perfectly.更多还原