Abstract
This study presents a modified algorithm of the grey wolf optimizer to solve the problem of learning rate selection in the multilayer type-2 asymmetric fuzzy controller (MT2AFC). The improvements of our modified optimizer are: the best position of the swarm is memorized, thus making the alpha wolves only update when a better position appears in the next iteration; search performance is enhanced by giving more freedom to update the grey wolf position. The proposed optimizer algorithm is then applied to optimize the suitable learning rates for the proposed controller. The multilayer type-2 asymmetric membership function is used in the fuzzy control network to enhance the learning ability and flexibility of the designed network architecture. The gradient descent method is used to adjust the parameters of the proposed MT2AFC controller online. The stability of the system is guaranteed using the Lyapunov approach. Besides, the self-evolving algorithm is used to construct the network structure autonomously. Ultimately, the numerical simulations of the chaotic synchronization systems are carried out to verify the effectiveness of our proposed method.
Highlights
In the past decade, some studies have shown type-2 fuzzy logic systems (T2FLSs) have better uncertainty handling capacity than type-1 fuzzy logic systems (T1FLSs) [1]–[5]
The membership degrees in T1FLSs are crisp numbers, whereas the membership degrees in T2FLSs are fuzzy membership grades
This paper proposes a multi-layer structure for T2AFM to enhance the learning ability and flexibility of the interval type-2 fuzzy logic systems (IT2FLSs)
Summary
Some studies have shown type-2 fuzzy logic systems (T2FLSs) have better uncertainty handling capacity than type-1 fuzzy logic systems (T1FLSs) [1]–[5]. This study designed a modified algorithm of a grey wolf optimizer (MGWO) to optimize the learning rates for the adaptive laws of the proposed MT2AFC controller. Based on the above discussion, we propose a new method to synchronize the chaotic systems, combining the advantages of interval type-2 fuzzy logic systems, asymmetric membership functions, the multi-layer structure, the modified grey wolf optimizer, and the self-evolving algorithm. The novelty of this study is the design of a multilayer structure for the type-2 asymmetric fuzzy controller, which uses the self-evolving algorithm to autonomously construct the network structure. Based on the proposed structure learning algorithm, the structure of the MMT2AFC network can adjust online to achieve a suitable number of layers
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