Generalized Takagi-Sugeno Fuzzy Logical System Optimal Parameter Identification Based on Genetic Algorithm
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摘要: 针对Takagi-Sugeno模糊逻辑系统的隶属函数不具有自适应性且模糊规则数的确定 带有很大的人为主观性,这里引入了一类广义Takagi-Sugeno模糊逻辑系统;在模型实现上，以 广义Takagi-Sugeno模型为个体,采用简单、有效的矩阵编码方式,借助遗传算法得到一个次优 的广义Takagi-Sugeno模糊系统模型,该模型不仅能很好地逼近所要辨识的非线性系统,而且 还具有较低的复杂度.仿真结果表明了广义Takagi-Sugeno模型及其参数辨识方法的正确性和 有效性.Abstract: In Takagi-Sugeno fuzzy logical system, its membership functions have no self-adaptability and the number of fuzzy ruels is defined subjectively. In this paper, a generalized Takagi-Sugeno fuzzy logical system model is quoted. In search of optimal parameters of the generalized Takagi-Sugeno model the matrix coding is adopted. The structure of the generalized Takagi-Sugeno model is evolved by GA and the resulting suboptimal solution can be found quickly, which has lower complexity and approximates to a nonliner system very well. The validity of this method has been demonstrated by a numerical simulation.
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Key words:
- Fuzzy logical system /
- genetic algorithm /
- matrix coding /
- parameter identification
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