计算机科学 ›› 2013, Vol. 40 ›› Issue (1): 203-207.

• 人工智能 • 上一篇    下一篇

基函数可递归的泛函神经元网络学习算法

肖倩,周永权,陈振   

  1. (广西大学计算机与电子信息学院 南宁530004) (广西民族大学信息科学与工程学院 南宁530006)
  • 出版日期:2018-11-16 发布日期:2018-11-16

Functional Network Learning Algorithm with Recursively Base Functions

  • Online:2018-11-16 Published:2018-11-16

摘要: 将泛函神经元结构做了一个变形,给出了一种基函数可递归的泛函神经元网络学习算法,该算法借助于矩阵伪逆递归求解方法,完成对泛函神经元网络基函数的自适应调整,最终实现泛函网络结构和参数共同的最优求解。数值仿真实验结果表明,该算法具有自适应性、鲁棒性和较高的收敛精度,将在实时在线辫识中有着广泛的应用。

关键词: 泛函神经元。某函教。矩阵伪拼。学习算法

Abstract: By transforming the functional neuron,we proposed a functional network learning algorithm with recursively base functions. The algorithm uses a recursive method for solving matrix's pseudo-inverse to achieve adaptive adjustment of base functions in functional neural network, finally realizes the functional network structure and parameters of the optimal solution together. The experimental results show that the learning algorithm has adaptive, robustness and high accuracy of convergence, and will have broad application in real-time online identification.

Key words: Functional neuron, Base functions, Matrix's pseudo inverse, Learning algorithm

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