Frequency Conversion Sinusoidal Chaotic Neural Network Based on Self-adaptive Simulated Annealing

doi:10.3969/j.issn.0372-2112.2019.03.014

您当前的位置：

首页 >

文章列表页 >

Frequency Conversion Sinusoidal Chaotic Neural Network Based on Self-adaptive Simulated Annealing

更新时间：2025-07-02

- Frequency Conversion Sinusoidal Chaotic Neural Network Based on Self-adaptive Simulated Annealing
- Acta Electronica Sinica Vol. 47, Issue 3, Pages: 613-622(2019)
- 作者机构：
  
  1. 北京工业大学信息学部,北京,100124
  2. 泰山学院机械与建筑工程学院,山东,泰安,271000
  3. 计算智能与智能系统北京市重点实验室,北京,100124
  4. 北京工业大学信息学部,北京,100124
  5. 泰山学院机械与建筑工程学院,山东,泰安,271000
  6. 计算智能与智能系统北京市重点实验室,北京,100124
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2019.03.014
  CLC： TP183
- Published Online：25 March 2019，
  
  Published：2019
- 稿件说明：
移动端阅览
Frequency Conversion Sinusoidal Chaotic Neural Network Based on Self-adaptive Simulated Annealing[J]. Acta Electronica Sinica, 2019, 47(3): 613-622.
DOI：

Frequency Conversion Sinusoidal Chaotic Neural Network Based on Self-adaptive Simulated Annealing[J]. Acta Electronica Sinica, 2019, 47(3): 613-622. DOI： 10.3969/j.issn.0372-2112.2019.03.014.

摘要

针对变频正弦混沌神经网络寻优精度与收敛速度无法兼顾的问题，通过分析暂态混沌神经网络的优化机制和现有的退火策略，提出了一种基于自适应模拟退火策略的变频正弦混沌神经网络模型.该模型可以根据混沌神经元的Lyapunov指数来确定合适的自反馈连接权值.给出了混沌神经元的倒分岔图、Lyapunov指数及不同退火函数的时间演化图，证明了自适应模拟退火策略能够自主选择合适的退火速度，更有效的利用混沌全局搜索能力，并加快非混沌态的演化时间.为了证明该模型的有效性，将其应用于函数优化和组合优化问题中.仿真实验表明：（1）对于该模型退火速度的选择，自适应模拟退火策略比现有的几种退火方法更具有灵活性和适应性；（2）该模型在寻优精度和速度上比暂态混沌神经网络及其他改进模型具有更好的兼顾性.

Abstract

The frequency conversion sinusoidal chaotic neural network (FCSCNN) cannot consider search accuracy and convergence speed simultaneously.In order to solve the mentioned problem

a novel self-adaptive simulated annealing (SSA) strategy is proposed by analyzing the optimization mechanism of the transiently chaotic neural network (TCNN) and the existing annealing strategy.It can give appropriate self-feedback connection weights based on the characteristics of Lyapunov exponent.The reversed bifurcation

Lyapunov exponent and annealing function evolution diagram of the chaotic neuron are given and the dynamic characteristic is analyzed.It shows that the SSA strategy can choose appropriate annealing speed in different stages

which can not only make full use of chaotic global searching ability but also accelerate convergence speed.Based on the neuron model

a novel FCSCNN with SSA strategy (FCSCNN-SSA) is proposed and applied to nonlinear function optimization and combinational optimization problems.The simulation results show that:(1) The SSA strategy can targeted choose the appropriate annealing speed

which is superior to other several existing simulated annealing methods for pertinence and adaptability and can be expanded to other similar models with same optimization mechanism; (2)FCSCNN-SSA can converge with a fast speed and search accuracy simultaneously than TCNN

TCNN-SEA

I-TCNN

NCNN

BFS-TCNN

FCSCNN.

关键词

Keywords

references

Views

342

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Study on Nonlinear Dynamics Behavior in PWM Switched-Capacitor DC-DC Converter

Periodic Windows of a Chaotic Map with Infinite Collapses and the Test of Even Distribution

An Algorithm for Computing the Largest Lyapunov Exponent Based on Space Grid Method

Cryptographic Feature Analysis on 2D Coupled Map Lattices Based on Piecewise Logistic Map

Multi-Strategy Ensemble Salp Swarm Algorithm for Robot Path Planning

Related Author

WANG Li-zhi

ZHANG Li-sen

HU Guang-rui

ZHU Hong-wen

JIANG Ling-ge

HE Chen

HE Di

LIU Shu-yong

Related Institution

Institute of Science,Air Force Engineering University

Department of Electronic Engineering,Xi’an University of Technology

Institute of ScienceAir Force Engineering UniversityXi’anShaanxi 710051China

Department of Electronic EngineeringXi’an University of TechnologyXi’anShaanxi710048China

Dept.of Electronic Engineering,Shanghai Jiao Tong University

⁰