多级离散模糊神经网络稳定性的优化算法
Optimization Algorithm of Multi-Level Discrete Fuzzy Neural Networks for Solving Global Stability
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摘要: 针对模糊集理论在建模中对变化的外部环境适应能力差,以及基本神经网络模型不容易获得模糊集之间关系等问题,提出了一个具有基本模糊推理系统"IF-THEN"规则的多级离散模糊神经网络模型。分析了该模型的基本稳定性条件,并使用硬C均值聚类方法获得数据集之间的关系,采用遗传算法优化了该模型。最后通过计算机仿真验证了该模型的有效性。Abstract: To solve the problems of low adaptability for mutative external environment existed in the theory of fuzzy sets and the disadvantage of failing to obtain eventual relationship among the fuzzy sets existed in the model of neural networks, this paper proposes a multi-level discrete fuzzy neural networks model which has the "if-then" rule of fuzzy inference system. The essential stability conditon of the model is analyzed.The model uses Hard C-Means (HCM) clustering to obtain fuzzy neural networks' eventual relationship among the fuzzy sets and applies evolutionary algorithm to optimize the model. The simulation results show the effectiveness of the proposed model.