Feng-Yu Chang

An Efficient Interval Type-2 Fuzzy CMAC for Chaos Time-Series Prediction and Synchronization

This paper aims to propose a more efficient control algorithm for chaos time-series prediction and synchronization. A novel type-2 fuzzy cerebellar model articulation controller (T2FCMAC) is proposed. In some special cases, this T2FCMAC can be reduced to an interval type-2 fuzzy neural network, a fuzzy neural network, and a fuzzy cerebellar model articulation controller (CMAC). So, this T2FCMAC is a more generalized network with better learning ability, thus, it is used for the chaos time-series prediction and synchronization. Moreover, this T2FCMAC realizes the un-normalized interval type-2 fuzzy logic system based on the structure of the CMAC. It can provide better capabilities for handling uncertainty and more design degree of freedom than traditional type-1 fuzzy CMAC. Unlike most of the interval type-2 fuzzy system, the type-reduction of T2FCMAC is bypassed due to the property of un-normalized interval type-2 fuzzy logic system. This causes T2FCMAC to have lower computational complexity and is more practical. For chaos time-series prediction and synchronization applications, the training architectures with corresponding convergence analyses and optimal learning rates based on Lyapunov stability approach are introduced. Finally, two illustrated examples are presented to demonstrate the performance of the proposed T2FCMAC.

A species-based improved electromagnetism-like mechanism algorithm for TSK-type interval-valued neural fuzzy system optimization

This paper proposes a novel TSK-type interval-valued neural fuzzy system with asymmetric membership functions (TIVNFS-A) which is optimized by a species-based improved electromagnetism-like mechanism (SIEM) algorithm. The proposed TIVNFS-A uses interval-valued asymmetric fuzzy membership functions and the TSK-type consequent part to improve the approximation performance and to reduce the number of fuzzy rules. In addition, the corresponding type reduction procedure is integrated in the adaptive network layers to reduce the amount of computation in the system. To train the TIVNFS-As, the SIEM combines the advantages of electromagnetism-like mechanism (EM) algorithm and gradient-descent technique to obtain faster convergence and lower computational complexity. Based on a similarity measurement, the swarm of the SIEM has a dynamic population size, and its population is also divided dynamically into subpopulations for optimization. The gradient-descent method is applied to the dominant particle in each species for a neighborhood search. Finally, the nonlinear system identification and classification problems are shown to demonstrate the performance and effectiveness of the proposed TIVNFS-A with the SIEM.

Interval Type-2 Recurrent Fuzzy Neural System for Nonlinear Systems Control Using Stable Simultaneous Perturbation Stochastic Approximation Algorithm

This paper proposes a new type fuzzy neural systems, denoted IT2RFNS-A (interval type-2 recurrent fuzzy neural system with asymmetric membership function), for nonlinear systems identification and control. To enhance the performance and approximation ability, the triangular asymmetric fuzzy membership function (AFMF) and TSK-type consequent part are adopted for IT2RFNS-A. The gradient information of the IT2RFNS-A is not easy to obtain due to the asymmetric membership functions and interval valued sets. The corresponding stable learning is derived by simultaneous perturbation stochastic approximation (SPSA) algorithm which guarantees the convergence and stability of the closed-loop systems. Simulation and comparison results for the chaotic system identification and the control of Chuas chaotic circuit are shown to illustrate the feasibility and effectiveness of the proposed method.

On-line Adaptive Interval Type-2 Fuzzy Controller Design via Stable SPSA Learning Mechanism

This paper proposes an interval type-2 Takagi-Sugeno-Kang fuzzy neural system (IT2TFNS) to develop an on-line adaptive controller using stable simultaneous perturbation stochastic approximation (SPSA) algorithm. The proposed IT2TFNS realizes an interval type-2 TSK fuzzy logic system formed by the neural network structure. Differ from the most of interval type-2 fuzzy systems, the type-reduction of the proposed IT2TFNS is embedded in the network by using uncertainty bounds method such that the time-consuming Karnik-Mendel (KM) algorithm is replaced. The proposed stable SPSA algorithm provides the gradient free property and faster convergence. However, the stable SPSA algorithm inherently has the problem for on-line adaptive control. Hence, in order to achieve the on-line result, we utilize the sliding surface to develop a new on-line adaptive control scheme. In addition, the corresponding stable learning is derived by Lyapunov theorem which guarantees the convergence and stability of the closed-loop systems. Simulation and comparison results are shown to demonstrate the performance and effectiveness of our approach.

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

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.

Chaos sychronization using un-normalized interval type-2 fuzzy neural controller

This paper proposes an un-normalized interval type-2 fuzzy neural network (UIT2FNN) and its application on chaos synchronization. The UIT2FNN combines the advantages of un-normalized interval type-2 fuzzy logic system and neural network. For chaos synchronization, the UIT2FNN plays the role of controller that generates the proper control input such that the two chaotic systems can be synchronized. The simulation and comparison results of Duffing-Holmes system synchronization are presented to illustrate the performance of the proposed UIT2FNN.