Fuzzy expert systems vs. neural networks-truck backer-upper control revisited

IEEE 1991 International Conference on Systems Engineering Pub Date : 1900-01-01 DOI:10.1109/ICSYSE.1991.161118

P. A. Ramamoorthy, S. Huang

引用次数: 20

Abstract

It is pointed out that by merging the advantages of fuzzy expert systems and neural networks one can arrive at a more powerful yet more flexible system for inferencing and learning. The advantages of fuzzy expert systems are their ability to provide nonlinear mapping through the membership functions and fuzzy rules, and the ability to deal with fuzzy information and incomplete and/or imprecise data. The merger of these two concepts is explained using the truck backer-upper control problem. Novel network architectures obtained by merging these two concepts and simulation results for the truck backer-upper problem using the architecture are shown.<>

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模糊专家系统vs.神经网络——卡车后轮控制重访

通过融合模糊专家系统和神经网络的优点，可以得到一个更强大、更灵活的推理和学习系统。模糊专家系统的优点是能够通过隶属函数和模糊规则提供非线性映射，能够处理模糊信息和不完整或不精确的数据。这两个概念的合并是用卡车后车顶控制问题来解释的。文中给出了将这两个概念融合得到的新型网络体系结构，并给出了应用该体系结构求解卡车后置问题的仿真结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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IEEE 1991 International Conference on Systems Engineering

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