基于Chebyshev神经网络的衍生算法  

A deriving arithmetic based on Chebyshev neural network

作　　者：邹阿金[1] 成继勋[1] 沈建中[2] 

机构地区：[1]湘潭工学院自动化与信息工程系,湖南湘潭411201 [2]中国科学院声学研究所,湖南长沙410000

出　　处：《湘潭矿业学院学报》2001年第1期76-78,共3页Journal of Xiangtan Mining Institute

基　　金：国家自然科学基金!(1 99740 0 2 )

摘　　要：根据辨识对象 ,神经网络的输入层和输出层神经元的个数易于确定 ,而隐层数和隐层神经元个数难以确定 .借助于生物的生长发育知识 ,在正交基函数神经网络的基础上提出衍生算法 ,该算法的基本思想是 :先选取较少个数的隐层神经元作为初始发育细胞 ,训练K次后 ,如果目标函数J不再变化且大于给定的ε ,则网络自动衍生 ,以上过程反复进行 ,直至J小于等于ε ,则停止衍生和训练 .仿真实验表明 ,该算法在训练过程中改善了收敛速度 ,并自动调整网络的拓扑结构 ,解决了隐含神经元个数难以确定的问题且具有优良的逼进任意非线性特性的能力 .图 2 ,表 2 ,参 7.In the research of neural networks, the numbers of the neurons in the input-layer and output-layer are easily fixed with the identification objects. But it is difficult to determine the numbers of hidden-layer and hidden neurons. By employing the knowledge of biologic growth, this paper presents a deriving algorithm based on the neural networks. Firstly, less hidden neurons are chosen to be the original cells; secondly, the network is continually deriving if the target function J dose not change and J > Ε, after the network is trained K-times. This process does not stop until J &le Ε. The simulation results show that the deriving algorithm have improves the convergence rates in the training process, corrects the top structure of network automatically, and solves the problem on determining the number of the hidden neurons. The giver algorithm is proved of excellent performance of approximating nonlinear properties.

关 键 词：神经网络 正交基函数 突触 生物 衍生算法 拓扑结构 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]