摘要
本文将遗传算法与局部寻优算法相结合代替传统的BP算法寻找BP神经网络模型中最佳的权值与阀值,进而建立了税收预测模型。对某区域的数值仿真表明该方法比单纯用BP方法或单纯用遗传算法有更高的精度,效果令人满意。
The author adopts a new combined method of genetic algorithm and local optimization algorithm, instead of traditional BP algorithm, to find the fittest values of weights and biases of the neural network model and then sets up a taxationforecasting model. By simulating this model using the data of certain district,this method has better precision than using BP algorithm only or using genetic algorithm only, and the result is satisfied.
出处
《微计算机信息》
北大核心
2007年第3期185-189,251,共6页
Control & Automation
基金
国家自然科学基金(60374016)
广东省自然科学基金(020848)
关键词
遗传算法
神经网络
税收模型
genetic algorithm,neural network,taxation model