Observer Design for Fractional-Order Chaotic Neural Networks With Unknown Parameters

Zhang, Xiulan

doi:10.1109/access.2020.3005661

Cited by 4 publications

(3 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wang and Zhang [61] designed two fractional observers for CNN with and without parametric uncertainties. eir observation errors eventually converged in a small region using fractional stability criteria, and their proposed observers were robust.…”

Section: Introductionmentioning

confidence: 99%

Chaotic Honeybees Optimization Algorithms Approach for Traveling Salesperson Problem

et al. 2022

View full text Add to dashboard Cite

Due to the difficulty in solving combinatorial optimization problems, it is necessary to improve the performance of the algorithms by improving techniques to deal with complex optimizations. This research addresses the metaheuristics of marriage in honey-bees optimization (MBO) based on the behavior of bees. The current study proposes a technique for solving combinatorial optimization problems within proper computation times. The purpose of this study focuses on the travelling salesperson problem and the application of chaotic methods in important sections of the MBO metaheuristic. Three experiments were conducted to measure the efficiency and quality of the solutions: (1) MBO with chaos to generate initial solutions (MBO2); (2) MBO with chaos in the workers (MBO3); and (3) MBO with chaos to generate initial solutions and the workers (MBO4). The application of chaotic functions in MBO was significantly better at solving the travelling salesperson problem.

show abstract

Section: Introductionmentioning

confidence: 99%

Chaotic Honeybees Optimization Algorithms Approach for Traveling Salesperson Problem

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In this case, observer-based control is usually required. For example, [13] introduced a disturbance observer to estimate disturbance and uncertainty and [25] designed a state observer to obtain the system unmeasurable state for fractional order chaotic systems.…”

Section: Introductionmentioning

confidence: 99%

Event-Triggered Adaptive Neural Network Backstepping Sliding Mode Control for Fractional Order Chaotic Systems Synchronization With Input Delay

2021

View full text Add to dashboard Cite

This paper investigates the fractional order chaotic systems synchronization with input delay. To reduce the utilization of communication resources and achieve system synchronization, an adaptive neural network backstepping sliding mode controller is proposed based on event-triggered scheme without Zeno behavior. To avoid ''explosion of complexity'' and obtain fractional derivatives for virtual control functions continuously, the fractional order dynamic surface control (DSC) technology is introduced into the controller. The sliding term is introduced to enhance robustness. The Pade delay approximation method is used to handle the input delay, which can reduce the analysis complexity of fractional order chaotic systems with input delay. The unknown nonlinear functions and uncertain disturbances are approximated by the RBF neural network. An observer is used for state estimation of the fractional order system. By applying the Lyapunov stability theory, we can prove that the all closed-loop signals are bounded. Examples and simulations prove the feasibility of the proposed control method.INDEX TERMS Fractional order chaotic systems synchronization, event-triggered, dynamic surface control, neural network, observer, input delay.

show abstract

“…F RACTIONAL order system, as a more general dynamic system, has attracted increasing concerns [1], [2], [3], [4], [5] owing to their special properties: (1) the fractional order parameters can help analyze the dynamic behavior of systems by adding a degree of freedom [1], [2], [3]; (2) fractional-order systems have merits of memory and hereditary [4], [5]. Therefore, fractional-order systems have been intensively applied in many fields [6], [7], [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Global Mittag-Leffler Synchronization of Fractional-Order Neural Networks With Time-Varying Delay via Hybrid Sliding Mode Control

Yang

Chen

2020

IEEE Access

View full text Add to dashboard Cite

In this paper, the global Mittag-Leffler synchronization (GMLS) for fractional-order neural networks with time-varying delay (FNNTVD) is studied based on hybrid sliding mode control (HSMC). First, a class of sliding surface is designed to study the dynamic behavior of FNNTVD by using the integer order Lyapunov direct method, which resolves the problem that the activation function of FNNTVD cannot be applied to the construction of Lyapunov function in the previous literature. Then, a new HSMC with the fixed signal transmission time delay is developed to ensure GMLS of FNNTVD. Next, based on Lyapunov direct method and the designed HSMC, some criteria are put forward to achieve GMLS of FNNTVD. Finally, two examples are given to verify the effectiveness of the proposed synchronization criteria for FNNTVD.

show abstract

Observer Design for Fractional-Order Chaotic Neural Networks With Unknown Parameters

Cited by 4 publications

References 36 publications

Chaotic Honeybees Optimization Algorithms Approach for Traveling Salesperson Problem

Chaotic Honeybees Optimization Algorithms Approach for Traveling Salesperson Problem

Event-Triggered Adaptive Neural Network Backstepping Sliding Mode Control for Fractional Order Chaotic Systems Synchronization With Input Delay

Global Mittag-Leffler Synchronization of Fractional-Order Neural Networks With Time-Varying Delay via Hybrid Sliding Mode Control

Contact Info

Product

Resources

About