| 引用本文: | 王耀南 ，张昌.复杂工业过程的遗传模糊神经网络控制[J].控制理论与应用,1999,16(6):886~891.[点击复制] |
| Wang Yaonan ， Zhang Changfan.Genetic-Based Neurofuzzy Control for Complex Industrial Process*[J].Control Theory and Technology,1999,16(6):886~891.[点击复制] |
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| 复杂工业过程的遗传模糊神经网络控制 |
| Genetic-Based Neurofuzzy Control for Complex Industrial Process* |
| 摘要点击 964 全文点击 526 投稿时间：1996-07-08 修订日期：1999-02-23 |
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| DOI编号 |
| 1999,16(6):886-891 |
| 中文关键词 模糊神经网络 学习算法 智能控制 |
| 英文关键词 fuzzy net learning algorithm intelligent control |
| 基金项目 |
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| 中文摘要 |
| 本文提出一种基于遗传算法和监督学习方法的有效模糊神经网络控制，这种控制器采用并行处理的模糊推理网络，具有两个重要特点：自适应和学习性，所提方法经过仿真实验和温控验证表明控制性能良好． |
| 英文摘要 |
| This paper proposes an effective fuzzy neural nerwork controUer based on genetic algorithm (GA) and super-vised gradient descent leaming.The fuzzy network control processing can be viewed as a parallel neural network where each neu-ron represents a fuzzy membership function and each link represents the weight of a fuuy rule, and it has two important charac-teristics of adaptation and leaming.The effectiveness of the proposed scheme is illustrated through simulation and temperature control processes. |