The Pi-Sigma Network Learning from Numerical Patterns and Fuzzy Rules

孙怀江; 孙进才; 倪昌祥

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The Pi-Sigma Network Learning from Numerical Patterns and Fuzzy Rules

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- The Pi-Sigma Network Learning from Numerical Patterns and Fuzzy Rules
- Acta Electronica Sinica Issue 5, (1996)
- 作者机构：
  
  西北工业大学
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP18
- Published：1996
- 稿件说明：
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[1]孙怀江,孙进才,倪昌祥.学习数值模式和模糊规则的Pi－Sigma网络[J].电子学报,1996(05):96-98.

孙怀江, 孙进才, 倪昌祥. The Pi-Sigma Network Learning from Numerical Patterns and Fuzzy Rules[J]. Acta Electronica Sinica, 1996, (5).
[1]孙怀江,孙进才,倪昌祥.学习数值模式和模糊规则的Pi－Sigma网络[J].电子学报,1996(05):96-98. DOI：

孙怀江, 孙进才, 倪昌祥. The Pi-Sigma Network Learning from Numerical Patterns and Fuzzy Rules[J]. Acta Electronica Sinica, 1996, (5). DOI：

摘要

本文推广了Ｐｉ－ｓｉｇｍａ网络学习算法，并提出了一种计算模糊数α水平集乘积的简单算法，使得在设计Ｐｉ－ｓｉｇｍａ网络分类器时，既能利用数值数据，又能利用以模糊ＩＦ－ＴＨＥＮ规则表达的专家知识，仿真结果验证了这种推广学习算法的正确性，并表明，在某些情况下，基于Ｐｉ－ｓｉｇｍａ网络的方法比基于多层感知机的方法需要更简单的网络结构和短得多的训练时间。

Abstract

The Pi-sigma network learning algorithm is generalized

and a simple method for computing product of fuzzy numbers is proposed

so that it is possible to utilize both numerical patterns and expert knowledge represented by fuzzy IF-THEN rules for designing the Pi-sigma network classifiers. Simulation results indicate that the generalized learnig algorithm is correct

and in some cases

the method based on the Pi-sigma network requires simpler structure and much shorter training time than that based on the Multilayer Perceptron.

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