新型Sigma-Pi泛函网络模型

New Sigma-Pi Functional Networks Model

将泛函神经元结构变形,建立Sigma-Pi泛函网络模型,给出Sigma-Pi泛函网络学习算法。采用数值分析的方法,将Sigma-Pi泛函网络应用于异或问题,结果表明,该网络对于某些问题具有很强的分类能力。该方法的优点在于利用一元函数作为基函数来实现高维函数的逼近,在函数逼近技术上,有着重要的应用价值。

This paper converts a structure of a functional neuron, presents a Sigma-Pi functional network（SPFN） structure, and proposes the Sigma-Pi functional networks learning algorithm. Using numerical analysis method, the Sigma-Pi functional network is applied to XOR problem. The results demonstrate that the functional network has powerful classification capability. The method has the advantages of using a variable function for multi-dimensional function approximation and important practical significance.