Neural Global Optimization Model for the Continuous Network States with the Fast Simulated-Annealing

Liu Jun.Lan Jialong.Wang Zhaoming

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Neural Global Optimization Model for the Continuous Network States with the Fast Simulated-Annealing

更新时间：2025-12-08

- Neural Global Optimization Model for the Continuous Network States with the Fast Simulated-Annealing
- Acta Electronica Sinica Issue 1, Pages: 24-27(1993)
- 作者机构：
  
  1. 电子科技大学电子工程学院
  2. 电子科技大学电子工程学院成都 610054
- 作者简介：
- 基金信息：
- DOI：
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- Published：1993
- 稿件说明：
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[1]刘军,兰家隆,王兆明.连续状态快速模拟退火神经全局优化模型[J].电子学报,1993(01):24-27.

Liu Jun.Lan Jialong.Wang Zhaoming. Neural Global Optimization Model for the Continuous Network States with the Fast Simulated-Annealing[J]. Acta Electronica Sinica, 1993, (1): 24-27.
[1]刘军,兰家隆,王兆明.连续状态快速模拟退火神经全局优化模型[J].电子学报,1993(01):24-27. DOI：

Liu Jun.Lan Jialong.Wang Zhaoming. Neural Global Optimization Model for the Continuous Network States with the Fast Simulated-Annealing[J]. Acta Electronica Sinica, 1993, (1): 24-27. DOI：

摘要

本文提出了一种针对连续状态的具有快速模拟退火特性的神经网络全局优化模型。讨论了模型的算法实现和参数选取

给出了实验例子。

Abstract

A neural network model with the continuous output states of neurons is proposed to solve the global optimization problems. This model has the property of the fast simulated annealing with the continuous variable states. The implementation of the algorithm and the determination of the parameters are discussed in this paper. Examples are also given.

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