Description: 模拟退火算法来源于固体退火原理，将固体加温至充分高，再让其徐徐冷却，加温时，固体内部粒子随温升变为无序状，内能增大，而徐徐冷却时粒子渐趋有序，在每个温度都达到平衡态，最后在常温时达到基态，内能减为最小。根据Metropolis准则，粒子在温度T时趋于平衡的概率为e-ΔE/(kT)，其中E为温度T时的内能，ΔE为其改变量，k为Boltzmann常数。用固体退火模拟组合优化问题，将内能E模拟为目标函数值f，温度T演化成控制参数t，即得到解组合优化问题的模拟退火算法：由初始解i和控制参数初值t开始，对当前解重复“产生新解→计算目标函数差→接受或舍弃”的迭代，并逐步衰减t值，算法终止时的当前解即为所得近似最优解，这是基于蒙特卡罗迭代求解法的一种启发式随机搜索过程。退火过程由冷却进度表(Cooling Schedule)控制，包括控制参数的初值t及其衰减因子Δt、每个t值时的迭代次数L和停止条件S。
-simulated annealing algorithm derived from solid annealing method, the heating to the full solid, let its slowly cooling, heating, solid particles with internal temperature rise-into disorder, which can increase, and slowly cooling gradual and orderly particles in each temperature has reached equilibrium, in the end when the temperature reached to ground state, which can be reduced to the minimum. According to the Metropolis criteria particles at a temperature T leveling the probability of e- E / (kT), in which the E-T when the temperature within, E capacity for change, for the Boltzmann constant k. Solid simulated annealing combinatorial optimization problems, will be able to target E simulation function f, T evolved temperature control parameters t, that is to be solving combinatorial o Platform: |
Size: 9122 |
Author:刘明 |
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Description: 模拟退火算法来源于固体退火原理，将固体加温至充分高，再让其徐徐冷却，加温时，固体内部粒子随温升变为无序状，内能增大，而徐徐冷却时粒子渐趋有序，在每个温度都达到平衡态，最后在常温时达到基态，内能减为最小。根据Metropolis准则，粒子在温度T时趋于平衡的概率为e-ΔE/(kT)，其中E为温度T时的内能，ΔE为其改变量，k为Boltzmann常数。用固体退火模拟组合优化问题，将内能E模拟为目标函数值f，温度T演化成控制参数t，即得到解组合优化问题的模拟退火算法：由初始解i和控制参数初值t开始，对当前解重复“产生新解→计算目标函数差→接受或舍弃”的迭代，并逐步衰减t值，算法终止时的当前解即为所得近似最优解，这是基于蒙特卡罗迭代求解法的一种启发式随机搜索过程。退火过程由冷却进度表(Cooling Schedule)控制，包括控制参数的初值t及其衰减因子Δt、每个t值时的迭代次数L和停止条件S。
-simulated annealing algorithm derived from solid annealing method, the heating to the full solid, let its slowly cooling, heating, solid particles with internal temperature rise-into disorder, which can increase, and slowly cooling gradual and orderly particles in each temperature has reached equilibrium, in the end when the temperature reached to ground state, which can be reduced to the minimum. According to the Metropolis criteria particles at a temperature T leveling the probability of e- E / (kT), in which the E-T when the temperature within, E capacity for change, for the Boltzmann constant k. Solid simulated annealing combinatorial optimization problems, will be able to target E simulation function f, T evolved temperature control parameters t, that is to be solving combinatorial o Platform: |
Size: 11082 |
Author:刘明 |
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Description: 模拟退火算法来源于固体退火原理，将固体加温至充分高，再让其徐徐冷却，加温时，固体内部粒子随温升变为无序状，内能增大，而徐徐冷却时粒子渐趋有序，在每个温度都达到平衡态，最后在常温时达到基态，内能减为最小。根据Metropolis准则，粒子在温度T时趋于平衡的概率为e-ΔE/(kT)，其中E为温度T时的内能，ΔE为其改变量，k为Boltzmann常数。用固体退火模拟组合优化问题，将内能E模拟为目标函数值f，温度T演化成控制参数t，即得到解组合优化问题的模拟退火算法：由初始解i和控制参数初值t开始，对当前解重复“产生新解→计算目标函数差→接受或舍弃”的迭代，并逐步衰减t值，算法终止时的当前解即为所得近似最优解，这是基于蒙特卡罗迭代求解法的一种启发式随机搜索过程。退火过程由冷却进度表(Cooling Schedule)控制，包括控制参数的初值t及其衰减因子Δt、每个t值时的迭代次数L和停止条件S。
-simulated annealing algorithm derived from solid annealing method, the heating to the full solid, let its slowly cooling, heating, solid particles with internal temperature rise-into disorder, which can increase, and slowly cooling gradual and orderly particles in each temperature has reached equilibrium, in the end when the temperature reached to ground state, which can be reduced to the minimum. According to the Metropolis criteria particles at a temperature T leveling the probability of e- E / (kT), in which the E-T when the temperature within, E capacity for change, for the Boltzmann constant k. Solid simulated annealing combinatorial optimization problems, will be able to target E simulation function f, T evolved temperature control parameters t, that is to be solving combinatorial o Platform: |
Size: 6055 |
Author:刘明 |
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Description: vb的波尔兹曼机（含模拟退火算法），有需要的可参考一下
-vb the Boltzmann machine (with simulated annealing algorithm), the need of a reference Platform: |
Size: 1412 |
Author:张耀天 |
Hits:
Description: 模拟退火算法来源于固体退火原理，将固体加温至充分高，再让其徐徐冷却，加温时，固体内部粒子随温升变为无序状，内能增大，而徐徐冷却时粒子渐趋有序，在每个温度都达到平衡态，最后在常温时达到基态，内能减为最小。根据Metropolis准则，粒子在温度T时趋于平衡的概率为e-ΔE/(kT)，其中E为温度T时的内能，ΔE为其改变量，k为Boltzmann常数。用固体退火模拟组合优化问题，将内能E模拟为目标函数值f，温度T演化成控制参数t，即得到解组合优化问题的模拟退火算法：由初始解i和控制参数初值t开始，对当前解重复“产生新解→计算目标函数差→接受或舍弃”的迭代，并逐步衰减t值，算法终止时的当前解即为所得近似最优解，这是基于蒙特卡罗迭代求解法的一种启发式随机搜索过程。退火过程由冷却进度表(Cooling Schedule)控制，包括控制参数的初值t及其衰减因子Δt、每个t值时的迭代次数L和停止条件S。
-simulated annealing algorithm derived from solid annealing method, the heating to the full solid, let its slowly cooling, heating, solid particles with internal temperature rise-into disorder, which can increase, and slowly cooling gradual and orderly particles in each temperature has reached equilibrium, in the end when the temperature reached to ground state, which can be reduced to the minimum. According to the Metropolis criteria particles at a temperature T leveling the probability of e- E/(kT), in which the E-T when the temperature within, E capacity for change, for the Boltzmann constant k. Solid simulated annealing combinatorial optimization problems, will be able to target E simulation function f, T evolved temperature control parameters t, that is to be solving combinatorial o Platform: |
Size: 9216 |
Author:刘明 |
Hits:
Description: 模拟退火算法来源于固体退火原理，将固体加温至充分高，再让其徐徐冷却，加温时，固体内部粒子随温升变为无序状，内能增大，而徐徐冷却时粒子渐趋有序，在每个温度都达到平衡态，最后在常温时达到基态，内能减为最小。根据Metropolis准则，粒子在温度T时趋于平衡的概率为e-ΔE/(kT)，其中E为温度T时的内能，ΔE为其改变量，k为Boltzmann常数。用固体退火模拟组合优化问题，将内能E模拟为目标函数值f，温度T演化成控制参数t，即得到解组合优化问题的模拟退火算法：由初始解i和控制参数初值t开始，对当前解重复“产生新解→计算目标函数差→接受或舍弃”的迭代，并逐步衰减t值，算法终止时的当前解即为所得近似最优解，这是基于蒙特卡罗迭代求解法的一种启发式随机搜索过程。退火过程由冷却进度表(Cooling Schedule)控制，包括控制参数的初值t及其衰减因子Δt、每个t值时的迭代次数L和停止条件S。
-simulated annealing algorithm derived from solid annealing method, the heating to the full solid, let its slowly cooling, heating, solid particles with internal temperature rise-into disorder, which can increase, and slowly cooling gradual and orderly particles in each temperature has reached equilibrium, in the end when the temperature reached to ground state, which can be reduced to the minimum. According to the Metropolis criteria particles at a temperature T leveling the probability of e- E/(kT), in which the E-T when the temperature within, E capacity for change, for the Boltzmann constant k. Solid simulated annealing combinatorial optimization problems, will be able to target E simulation function f, T evolved temperature control parameters t, that is to be solving combinatorial o Platform: |
Size: 11264 |
Author:刘明 |
Hits:
Description: 模拟退火算法来源于固体退火原理，将固体加温至充分高，再让其徐徐冷却，加温时，固体内部粒子随温升变为无序状，内能增大，而徐徐冷却时粒子渐趋有序，在每个温度都达到平衡态，最后在常温时达到基态，内能减为最小。根据Metropolis准则，粒子在温度T时趋于平衡的概率为e-ΔE/(kT)，其中E为温度T时的内能，ΔE为其改变量，k为Boltzmann常数。用固体退火模拟组合优化问题，将内能E模拟为目标函数值f，温度T演化成控制参数t，即得到解组合优化问题的模拟退火算法：由初始解i和控制参数初值t开始，对当前解重复“产生新解→计算目标函数差→接受或舍弃”的迭代，并逐步衰减t值，算法终止时的当前解即为所得近似最优解，这是基于蒙特卡罗迭代求解法的一种启发式随机搜索过程。退火过程由冷却进度表(Cooling Schedule)控制，包括控制参数的初值t及其衰减因子Δt、每个t值时的迭代次数L和停止条件S。
-simulated annealing algorithm derived from solid annealing method, the heating to the full solid, let its slowly cooling, heating, solid particles with internal temperature rise-into disorder, which can increase, and slowly cooling gradual and orderly particles in each temperature has reached equilibrium, in the end when the temperature reached to ground state, which can be reduced to the minimum. According to the Metropolis criteria particles at a temperature T leveling the probability of e- E/(kT), in which the E-T when the temperature within, E capacity for change, for the Boltzmann constant k. Solid simulated annealing combinatorial optimization problems, will be able to target E simulation function f, T evolved temperature control parameters t, that is to be solving combinatorial o Platform: |
Size: 6144 |
Author:刘明 |
Hits:
Description: vb的波尔兹曼机（含模拟退火算法），有需要的可参考一下
-vb the Boltzmann machine (with simulated annealing algorithm), the need of a reference Platform: |
Size: 1024 |
Author:张耀天 |
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Description: Boltzmannn机网络是Hinton等人在1985年将模拟退火算法引入到神经网络中，提出的，简称BM网络。-Boltzmannn computer network is Hinton and others in 1985 will be simulated annealing primer access to the neural network, the network referred to BM. Platform: |
Size: 30720 |
Author:czyujian |
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Description: 这是一个C语言编写的模拟退火算法的玻尔兹曼机，它实现了Boltzmann玻尔兹曼机的学习训练。通过仿真神经网络，实现在多个输入输出神经元间，训练权重和阈值，从而收敛。-This is a C language, simulated annealing algorithm Boltzmann machine, it implements the Boltzmann Boltzmann machine training study. Through the neural network simulation, implementation in a number of input and output neurons, the training of weights and thresholds, thus convergence. Platform: |
Size: 30720 |
Author:郑杨 |
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Description: 模拟退火算法来源于固体退火原理，将固体加温至充分高，再让其徐徐冷却，加温时，固体内部粒子随温升变为无序状，内能增大，而徐徐冷却时粒子渐趋有序，在每个温度都达到平衡态，最后在常温时达到基态，内能减为最小。根据Metropolis准则，粒子在温度T时趋于平衡的概率为e-ΔE/(kT)，其中E为温度T时的内能，ΔE为其改变量，k为Boltzmann常数。用固体退火模拟组合优化问题，将内能E模拟为目标函数值f，温度T演化成控制参数t，即得到解组合优化问题的模拟退火算法：由初始解i和控制参数初值t开始，对当前解重复“产生新解→计算目标函数差→接受或舍弃”的迭代，并逐步衰减t值，算法终止时的当前解即为所得近似最优解，这是基于蒙特卡罗迭代求解法的一种启发式随机搜索过程。退火过程由冷却进度表(Cooling
Schedule)控制，包括控制参数的初值t及其衰减因子Δt、每个t值时的迭代次数L和停止条件S。 -Simulated annealing algorithm derived from the theory of solid annealing, the solid heat to full high and let it slowly cooling, heating, the temperature rise inside the solid particles with the shape into disorder, which can be increased gradually while slowly cooling particles increasingly ordered, the temperature has reached equilibrium in each state, and finally reached the ground state at room temperature, which can be reduced to minimum. According to Metropolis criterion, particles tend to equilibrium at a temperature T, the probability e-ΔE/(kT), where E is the temperature T, internal energy, ΔE change its volume, k the Boltzmann constant. Simulated annealing with a solid portfolio optimization problem, the internal energy E is modeled as the objective function value f, temperature T evolved into control parameter t, which are solutions of combinatorial optimization problems of the simulated annealing algorithm: the initial solution from the initial value of t i and the control Platform: |
Size: 5120 |
Author:leansmall |
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Description: 模拟退火算法来源于固体退火原理，将固体加温至充分高，再让其徐徐冷却，加温时，固体内部粒子随温升变为无序状，内能增大，而徐徐冷却时粒子渐趋有序，在每个温度都达到平衡态，最后在常温时达到基态，内能减为最小。根据Metropolis准则，粒子在温度T时趋于平衡的概率为e-ΔE/(kT)，其中E为温度T时的内能，ΔE为其改变量，k为Boltzmann常数。用固体退火模拟组合优化问题，将内能E模拟为目标函数值f，温度T演化成控制参数t，即得到解组合优化问题的模拟退火算法：由初始解i和控制参数初值t开始-Simulated annealing algorithm comes from solid annealing principle, will warm to fully solid high, then let it slowly cooling, heating, solid internal particles with a temperature rise of disorder, can increase, and gradually cooled gradually orderly particles, and in every temperature at the balance state, and the last in the normal temperature at the ground state, internal energy is reduced to the minimum standards according to the Metropolis, particle in temperature T tend to balance when the probability of e-Δ e/(kT), which for temperature T e the internal energy, Δ e for its change the volume, k as Boltzmann constant use solid annealing simulation combinatorial optimization problem, the internal energy e simulation for target function value f, temperature T evolution into control parameters T, namely get solution combinatorial optimization problem of simulated annealing algorithm: the initial solution I and control parameter optimization.finally T start
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Size: 5120 |
Author:黄 |
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Description: Adaptive Simulated Annealing (ASA) is a C-language code developed to statistically find the best global fit of a nonlinear constrained
non-convex cost-function over a D-dimensional space. This algorithm
permits an annealing schedule for "temperature" T decreasing exponentially in annealing-time k, T = T_0 exp(-c k^1/D). The introduction of re-annealing also permits adaptation to changing
sensitivities in the multi-dimensional parameter-space. This annealing schedule is faster than fast Cauchy annealing, where T =
T_0/k, and much faster than Boltzmann annealing, where T = T_0/ln k.
ASA has over 100 OPTIONS to provide robust tuning over many classes of
nonlinear stochastic systems.-Adaptive Simulated Annealing (ASA) is a C-language code developed to statistically find the best global fit of a nonlinear constrained
non-convex cost-function over a D-dimensional space. This algorithm
permits an annealing schedule for "temperature" T decreasing exponentially in annealing-time k, T = T_0 exp(-c k^1/D). The introduction of re-annealing also permits adaptation to changing
sensitivities in the multi-dimensional parameter-space. This annealing schedule is faster than fast Cauchy annealing, where T =
T_0/k, and much faster than Boltzmann annealing, where T = T_0/ln k.
ASA has over 100 OPTIONS to provide robust tuning over many classes of
nonlinear stochastic systems. Platform: |
Size: 653312 |
Author:ip |
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Description: 随机神经网络 主要有模拟退火算法和Boltzmann机及其应用-There are random neural network algorithms and simulated annealing Boltzmann Machine and Its Applications Platform: |
Size: 3072 |
Author: |
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Search - boltzmann simulated