一种基于随机GA的提高BP网络泛化能力的方法

LM-BP网络对其初始权值和阈值敏感，泛化能力不强，针对该缺点，采用遗传算法（GA）对其初始权阈值进行优化，在一定程度上能提高LM-BP网络的泛化能力。为进一步扩展GA初始种群的覆盖范围，进一步提高LM—BP网络的泛化能力，采用多次随机产生初始种群多次优化的方法。以伦河孝感段氟化物含量为实例，建立随机GA的LM—BP网络模型，对原始数据进行拟合及测试，结果表明该方法基本能100％拟合，测试误差不超过2．3％。经过对比实验，证明了该方法的有效性。

A Method of Improving Generalization for BP Network Based on Random GA

The LM-BP neural network was sensitive to its initial weight values and threshold, and it had bad generalization ability. In view of its shortcomings, the initial weights and threshold of LM-BP neural network were optimized with GA. The generalization of LM -BP neural network was improved to a certain extent. To expand the coverage of initial population, the initial populations were randomly generated iteratively and the network was optimized multi times. Thus, the generalization of LM-BP network was further improved. Take the content of fluorine in Lun River from Xiaogan as an example, the LM-BP neural network model based on random GA was estab- lished, and the raw data were fitted and tested. The results showed that the accordance of fitting data is approximately 100%, and the tes- ting errors were less than 2.3 %. Through contrast experiments, the validity of this method was proved.