Simulated Annealing Strategy in Chaotic Neural Network with Chebyshev Polynomials

Xu, Yaoqun; Yang, Zhenhua; Zhen, Xinxin

doi:10.23919/ccc52363.2021.9549590

Cited by 2 publications

(1 citation statement)

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“…In the initial stage, the ergodic property of chaos is used to optimize rough search in a large range to avoid falling into local minimum [7,8]. In order to improve the optimization performance of chaotic neural networks, many scholars have proposed innovative research [9][10][11][12][13][14][15]. Based on Chen's chaotic neural network, Xu et al proposed the wavelet chaotic neural network using the Morlet wavelet function as an excitation function [10].…”

Section: Introductionmentioning

confidence: 99%

Dynamical System in Chaotic Neurons with Time Delay Self‐Feedback and Its Application in Color Image Encryption

Zhen

Tang

2022

Complexity

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The time delay caused by transmission in neurons is often ignored, but it is demonstrated by theories and practices that time delay is unavoidable. A new chaotic neuron model with time delay self-feedback is proposed based on Chen’s chaotic neuron. The bifurcation diagram and Lyapunov exponential diagram are used to analyze the chaotic characteristics of neurons in the model when they receive the output signals at different times. The experimental results exhibit that it has a rich dynamic behavior. In addition, the randomness of chaotic series generated by chaotic neurons with time delay self-feedback under different conditions is verified. In order to investigate the application of this model in image encryption, an image encryption scheme is proposed. The security analysis of the simulation results shows that the encryption algorithm has an excellent anti-attack ability. Therefore, it is necessary and practical to study chaotic neurons with time delay self-feedback.

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Section: Introductionmentioning

confidence: 99%

Dynamical System in Chaotic Neurons with Time Delay Self‐Feedback and Its Application in Color Image Encryption

Zhen

Tang

2022

Complexity

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A Novel Neuroevolutionary Paradigm for Solving Strongly Nonlinear Singular Boundary Value Problems in Physiology

2022

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In the present research work, we designed a hybrid stochastic numerical solver to investigate nonlinear singular two-point boundary value problems with Neumann and Robin boundary conditions arising in various physical models. In this method, we hybridized Harris Hawks Optimizer with Interior Point Algorithm named HHO-IPA. We construct artificial neural networks (ANNs) model for the problem, and this model is tuned with the proposed scheme. This scheme overcomes the singular behavior of problems. The accuracy and applicability of the method are illustrated by finding absolute errors in the solution. The outcomes are compared with the results present in the literature to demonstrate the effectiveness and robustness of the scheme by considering four different nonlinear singular boundary value problems. Further, the convergence of the scheme is proved by performing computational complexity analysis. Moreover, the graphical overview of statistical analysis is added to our investigation to elaborate further on the scheme's stability, accuracy, and consistency.

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Simulated Annealing Strategy in Chaotic Neural Network with Chebyshev Polynomials

Cited by 2 publications

References 11 publications

Dynamical System in Chaotic Neurons with Time Delay Self‐Feedback and Its Application in Color Image Encryption

Dynamical System in Chaotic Neurons with Time Delay Self‐Feedback and Its Application in Color Image Encryption

A Novel Neuroevolutionary Paradigm for Solving Strongly Nonlinear Singular Boundary Value Problems in Physiology

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