Search - Boltzmann

Search - Boltzmann - List

[AI-NN-PR] BOLTZMAN DL : 0: Boltzmann Machine Optimization 人工智能人工神经网络源码-Boltzmann Machine Optimization of artificial neural network artificial intelligence source
Date : 2025-12-24 Size : 33kb User : 站长
[AI-NN-PR] BAM DL : 0: Bidirectional Associative Memory Heteroassociative Memory 人工智能人工神经网络源码;-Bidirectional Associative Memory Heteroassociative Memory artificial intelligence artificial neural network source
Date : 2025-12-24 Size : 19kb User :
[AI-NN-PR] Boltzmann Machin DL : 0: 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 transfer, the probability of two more
Date : 2025-12-24 Size : 5kb User : 韵子
[AI-NN-PR] 贝叶斯看病程序 DL : 1: 本程序是利用贝叶斯算法来实现的一个看病程序，主要是中风后遗症的诊断-this program is to use Bayesian algorithms to achieve a treatment process, mainly in the diagnosis of stroke sequelae
Date : 2025-12-24 Size : 104kb User : luxiangzz
[AI-NN-PR] 模拟退火例子1 DL : 0: 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
Date : 2025-12-24 Size : 9kb User : 刘明
[AI-NN-PR] 模拟退火例子2 DL : 0: 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
Date : 2025-12-24 Size : 11kb User : 刘明
[AI-NN-PR] 模拟退火例子3 DL : 0: 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
Date : 2025-12-24 Size : 6kb User : 刘明
[AI-NN-PR] Boltzmann DL : 0: 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
Date : 2025-12-24 Size : 1kb User : 张耀天
[AI-NN-PR] boltzman_VC DL : 0: 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.
Date : 2025-12-24 Size : 30kb User : czyujian
[AI-NN-PR] TSP DL : 0: 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。-TSP
Date : 2025-12-24 Size : 114kb User : IT农夫
[AI-NN-PR] EDA_Tutorial DL : 0: 分布估计算法（EDA）讲义，29页PPT 1. 从GA到 EDA 2. 链接学习与模型 3. 连续EDA 4. EDA 与玻耳兹曼分布 5. 资源 -estimation of distribution algorithm（EDA）tutorial 1. from the GA to the EDA 2. Linking learning and models 3. Continuous EDA 4. EDA with Boltzmann distribution 5. other Resources
Date : 2025-12-24 Size : 195kb User : 陈雷
[AI-NN-PR] libann.1.4.tar DL : 0: 该源码实现了人工神经网络算法,包括hopfield和boltzmann等网络，并附有例程！-The source implementation of artificial neural network algorithms, including hopfield and boltzmann-peer networks, together with routine!
Date : 2025-12-24 Size : 1.15mb User : 王晓军
[AI-NN-PR] BOLTZMAN DL : 0: 人工智能，玻尔兹曼机的模拟退火，可以应用与优化旅行商问题-Artificial Intelligence, Boltzmann machine, simulated annealing, traveling salesman problem and optimization can be applied
Date : 2025-12-24 Size : 8kb User : 李杰
[AI-NN-PR] mnth DL : 0: 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
Date : 2025-12-24 Size : 5kb User : leansmall
[AI-NN-PR] D1Q5 DL : 0: 1D lattice boltzmann code in C
Date : 2025-12-24 Size : 2kb User : Ehsan
[AI-NN-PR] BOLTZMAN DL : 0: BOLTZMAN模拟退火算法的玻尔兹曼机-BOLTZMAN Boltzmann simulated annealing machine
Date : 2025-12-24 Size : 31kb User : mj
[AI-NN-PR] cylinder DL : 0: 格子boltzmann方法模拟2D圆柱绕流程序 matlab程序d2Q9模型-Lattice boltzmann simulation of flow around 2D cylinder model program matlab program d2Q9
Date : 2025-12-24 Size : 2kb User : x.f.shi
[AI-NN-PR] lbm2d.cpp DL : 1: 何雅玲教授的二维方腔流格子Boltzmann代码-Lattice Boltzmann code for 2D lid-driven cavity flow by He Yaling
Date : 2025-12-24 Size : 2kb User : hello
[AI-NN-PR] LibDriven_LBM DL : 0: 用c++实现顶盖驱动流，采用格子Boltzmann方法-This file is part of the book named "格子Boltzmann方法的理论及应用 ".Details can be found in Page 216～222 in Appendix D.You are free to use, copy and modify the sources under certain umstances, provided this copyright notice remains intact.
Date : 2025-12-24 Size : 2kb User : 李飞
[AI-NN-PR] Boltzmann DL : 0: 对一个三节点的玻尔兹曼机，在初始温度和初态确定的情况下，采用步长为-0.1的线性降温方式训练，通过编写程序确定在T 0时的状态-For a three-node Boltzmann machine, in the case of initial temperature and initial state determination, the use of a step-by-step linear cooling mode training, through the preparation of procedures to determine the state in the T 0
Date : 2025-12-24 Size : 1kb User : tuantaun1234

« 12 3 »