| 一种基于遗传算法的模糊神经网络最优控制 |
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| 引用本文： | 周志坚,毛宗源. 一种基于遗传算法的模糊神经网络最优控制[J]. 控制理论与应用, 2000, 17(5): 784-788 |
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| 作者姓名： | 周志坚 毛宗源 |
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| 作者单位： | 华南理工大学自动控制工程系,广州,510640 |
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| 基金项目： | 广东省自然科学基金!(96 0 30 4)资助项目 |
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| 摘 要： | 通过对控制系统的过程模拟，提出一种模糊神经网络最优控制方案。离线化部分基于遗传算法，分三阶段实现模糊神经网络控制器结构和参数的优化。在线优化部分通过重构模糊神经网络控制器的去模糊化部分，进一步调整控制规则，实现在线去模糊优化。仿真结果表明该方案优于常模糊控制方案和基于专家经验的模糊神经网络控制方案。
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| 关 键 词： | 最优控制 遗传算法 模糊神经网络 去模糊化 |
| 修稿时间： | 1998-10-28 |
On Designing an Optimal Fuzzy Neural Network Controller Using Genetic Algorithms |
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| ZHOU Zhijian,MAO Zongyuan. On Designing an Optimal Fuzzy Neural Network Controller Using Genetic Algorithms[J]. Control Theory & Applications, 2000, 17(5): 784-788 |
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| Authors: | ZHOU Zhijian MAO Zongyuan |
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| Abstract: | An optimal control scheme based on fuzzy neural network is proposed through simulating the process of the control system. Firstly, the fuzzy neural network is optimized by three step scheme based on genetic algorithms off line. Then the defuzzification part of the fuzzy neural network is reconstructed and is optimized to refine control rules on line. The simulation result demonstrates that the response is more favorable than that of conventional fuzzy controllers and that of {FNNC} based on expert experience. |
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| Keywords: | optimal control genetic algorithms fuzzy neural network defuzzification |
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