CAJ | 학술논문

　　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。
　　제출일충이용모의퇴화화혼합체계유전산법우화RBF신경망락적방법。통과이용혼합체계유전산법대RBF신경망락적탁복결구、경향기중심화반경진행삼수심우，인입모의퇴화산법대교차화변이개솔진행공제，채용최소이승법학정망락적수출권치。장차방법응용우전형실례，병여기타사충방법진행대비，통과시험결과증명료해방법적준학솔명현우우기타사충방법，방법적가행성화우월성득도험증。
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.