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仿真1:首先把网络温度参数T固定在100,按工作规则共进行1000次状态更新,把这1000次状态转移中网络中的各个状态出现的次数Si(i=1,2,…,16)记录
仿真1:首先把网络温度参数T固定在100,按工作规则共进行1000次状态更新,把这1000次状态转移中网络中的各个状态出现的次数Si(i=1,2,…,16)记录下来 按下式计算各个状态出现的实际频率: Pi=Si/∑i=1,NSi=Si/M 同时按照Bo1tzmann分布计算网络各个状态出现概率的理论值: Q(Ei)=(1/Z)exp(-Ei/T) 仿真2:实施降温方案,重新计算 采用快速降温方案:T(t)= T0/(1+t) T从1000降到0.01,按工作规则更新网络状态 当T=0.01时结束降温,再让T保持在0.01进行1000次状态转移,比较两种概率-a simulation : First of all network parameters temperature T fixed at 100 and, according to the rules for a total of 1000 to update the state, this state of the 1000 network transfer of all states for the number of Si (i = 1, 2, ..., 16) all recorded determined by the formula state-of the actual frequency : Pi = Si/i = 1, NSi = Si/M in accordance with Bo1tzmann distributed computing network of states all probability the theoretical value : Q (Ei) = (1/Z) exp (- Ei/T) Simulation 2 : implementation of cooling, re-using rapid cooling programs : T (t) = T0/(1 t) T dropped to 0.01 from 1000 and, according to the rules updated network state when T = 0.01 at the end of cooling, let T at 0.01 for the 1000 state tran
- 2022-08-06 06:55:50下载
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此文档是遗传算法原理加源代码。
生物的进化是一个奇妙的优化过程,它通过选择淘汰,突然变异,基因遗传等规律产生适应环境变化的优良物种。遗传算法是根据生物进化思...
此文档是遗传算法原理加源代码。
生物的进化是一个奇妙的优化过程,它通过选择淘汰,突然变异,基因遗传等规律产生适应环境变化的优良物种。遗传算法是根据生物进化思想而启发得出的一种全局优化算法。 -This document is the principle of genetic algorithm source code increases. Biological evolution is a wonderful optimization process, it eliminated by choosing a sudden variation of genetic and other changes in the law to adapt to the environment arising from the fine species. Genetic algorithm is based on ideas of biological evolution and the inspiration derived from a global optimization algorithm.
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里面有两个源码,人机博弈的,一个是五子棋,一个是中国象棋!...
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自适应共振神经网络可以实现自动学习与分类具有自适应的功能...
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