SA-HHGA优化RBF神经网络的方法及应用  

Study on RBF neural network method with application based on SA-HHGA optimization algorithm

作　　者：黄建招 谢建[1] 高钦和[1] 李良[1] 

机构地区：[1]第二炮兵工程大学兵器发射理论与技术国家重点学科实验室,西安710025

出　　处：《计算机工程与应用》2013年第17期209-212,共4页Computer Engineering and Applications

摘　　要：提出一种利用模拟退火和混合递阶遗传算法优化RBF神经网络的方法。通过利用混合递阶遗传算法对RBF神经网络的拓扑结构、径向基中心和半径进行参数寻优,引入模拟退火算法对交叉和变异概率进行控制,采用最小二乘法确定网络的输出权值。将此方法应用于典型实例,并与其他四种方法进行对比,通过试验结果证明了该方法的准确率明显优于其他四种方法,方法的可行性和优越性得到验证。An optimization method of RBF neural network based on simulated annealing and hybrid hierarchy genetic algorithm is put forward. In this method, the network topology, centers and radius of RBF neural network are optimized by hybrid hierarchy genetic algorithm, the probabilities of cross and mutation in genetic algorithm are controlled by simulated annealing algorithm, and the output weights of network are calculated by least square method. To validate the feasibility and effectiveness, this method and other four methods are implemented in typical case, the result shows that the accuracy of the proposed method is obviously higher than other methods. The feasibility and superiority of the method are validated.

关 键 词：模拟退火 混合递阶遗传算法 径向基神经网络 故障诊断 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]