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An Interval Type-2 Fuzzy System with a Species-Based Hybrid Algorithm for Nonlinear System Control Design

机译：带有种群混合算法的区间型2型模糊系统的非线性系统控制设计

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摘要

We propose a species-based hybrid of the electromagnetism-like mechanism (EM) and back-propagation algorithms (SEMBP) for an interval type-2 fuzzy neural system with asymmetric membership functions (AIT2FNS) design. The interval type-2 asymmetric fuzzy membership functions (IT2 AFMFs) and the TSK-type consequent part are adopted to implement the network structure in AIT2FNS. In addition, the type reduction procedure is integrated into an adaptive network structure to reduce computational complexity. Hence, the AIT2FNS can enhance the approximation accuracy effectively by using less fuzzy rules. The AIT2FNS is trained by the SEMBP algorithm, which contains the steps of uniform initialization, species determination, local search, total force calculation, movement, and evaluation. It combines the advantages of EM and back-propagation (BP) algorithms to attain a faster convergence and a lower computational complexity. The proposed SEMBP algorithm adopts the uniform method (which evenly scatters solution agents over the feasible solution region) and the species technique to improve the algorithm's ability to find the global optimum. Finally, two illustrative examples of nonlinear systems control are presented to demonstrate the performance and the effectiveness of the proposed AIT2FNS with the SEMBP algorithm.
机译：我们为具有不对称隶属度函数（AIT2FNS）设计的区间2型模糊神经系统提出了一种基于类物种的电磁学机制（EM）和反向传播算法（SEMBP）的混合体。采用区间2型不对称模糊隶属度函数（IT2 AFMF）和TSK型结果部分来实现AIT2FNS中的网络结构。另外，类型减少过程被集成到自适应网络结构中以减少计算复杂度。因此，AIT2FNS可以通过使用较少的模糊规则来有效地提高近似精度。 AIT2FNS由SEMBP算法训练，该算法包含统一初始化，物种确定，局部搜索，总力计算，移动和评估的步骤。它结合了EM和反向传播（BP）算法的优势，以实现更快的收敛速度和更低的计算复杂度。所提出的SEMBP算法采用统一方法（在可行解区域上均匀分散解决方案代理）和种技术，以提高算法寻找全局最优解的能力。最后，给出了非线性系统控制的两个说明性示例，以用SEMBP算法演示所提出的AIT2FNS的性能和有效性。

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  • 来源
    《Mathematical Problems in Engineering》 |2014年第8期|735310.1-735310.19|共19页
  • 作者

    Chung-Ta Li; Ching-Hung Lee; Feng-Yu Chang; Chih-Min Lin;

  • 作者单位

    Department of Electrical Engineering, Yuan Ze University, Taiwan;

    Department of Mechanical Engineering, National Chung Hsing University, Taiwan;

    Department of Electrical Engineering, Yuan Ze University, Taiwan;

    Department of Electrical Engineering, Yuan Ze University, Taiwan;

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