提出了用混沌模拟退火法估计非线性马斯京根模型参数的优化算法。
This paper presents an optimization algorithm based on chaotic simulated annealing algorithm, which is used to estimate the parameter of Nonlinear Muskingum model.
本文主要研究混沌模拟退火神经网络(CSAN)在求解tsp中的应用。
In this paper, We mainly do researches on using chaotic neural network based on simulated annealing (CSAN) to solve TSP.
基于混沌变量，提出一种混沌模拟退火优化方法，给出了初始温度的确定方法。
Based on chaotic variable, a chaos simulated annealing algorithm is proposed and the method of defining the initial temperature is given.
在传统混沌神经网络模型的基础上，提出了一种具有衰减混沌噪声的混沌模拟退火神经网络模型(CSA - DCN)。
Based on deeply discussing the principle of chaotic neural network model, the chaotic simulated annealing model with decaying chaotic noise (CSA-DCN) is presented.
混沌优化算法将混沌载波和模拟退火策略结合加快了寻优速度。
Chaos optimization algorithm, which combines chaos carrier wave with simulated annealing algorithm improves system optimization speed.
在寻优过程中，通过不断衰减混沌扰动幅度及混沌扰动的接受概率来实现混沌的模拟退火。
During the process of optimization, chaos simulated annealing was realized by decaying the amplitude of the chaos noise and the probability of accepting continuously.
在寻优过程中，通过不断衰减混沌扰动幅度及混沌扰动的接受概率来实现混沌的模拟退火。
During the process of optimization, chaos simulated annealing was realized by decaying the amplitude of the chaos noise and the probability of accepting continuously.
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