BP神经网络实现回归程序，神经网络可以从reunificat学习…

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

研究基于遗传编程的可持续性进化算法的理论与方法，并建立了该算法的数学模型及运算流程.HFC算法实现源代码.-Research based on genetic programming evolutionary algorithm for the sustainability of the theory and method, and set up a mathematical model of the algorithm and the computation flow. HFC algorithm source code implementation.

关于鱼群算法的一个程序，希望对学习智能算法的朋友有所帮助。-Fish-swarm algorithm on a procedure, and they hope to learn from intelligent algorithm friend help.

一个对初学者非常实际非常方便的矢量量化的C语言实现 ,值得-one pair of very real beginners very convenient vector quantization C language, it is worth a look at

this is a program about TSP -this is a program about TSP

FaceRec 简单易懂的神经网络面部特征识别例子 [VB源码]-FaceRec simple neural network to identify examples of facial features [VB code]

双峰和多峰函数最大值的遗传算法求解 采用前向网络-Bimodal and multimodal function maximum use of the genetic algorithm to the network

用人工神经网络实现格林码的编码，通过对网络的训练实现，输入一串0/1码得到其格林码-using artificial neural networks Green codes, the network through training, input string of 0/1 yards code to be its Green

改进遗传算法－郭涛算法做最优化问题很管用,算法的基本思想是 先任意产生n个随机数，然后从n个数里随机选择m个数，再有这m个 数合成一个新数，将这个新数同n个数中间适应值函数值的最差的比较， 如果好的话就取代最差的那个，如果它比最好的还要好的话，则把最好的 也取代。如果比最差的坏，则重新合成一个新数。依次循环下去。 程序的奇妙之处是GA_crossover()函数，产生的新数确实比较好，看看 那位大侠能改进一下，产生比这跟好的数。-improved genetic algorithm- Guo Tao done optimization algorithm is very effective, and the algorithm is the first basic idea arbitrary n generated random number and then the number li n m randomly selected number, and this number m of a new synthesis of the new middle with n number fitness function of the worst by comparison, if a good case to replace the worst, um, if it even than the best, then ruled the best have replaced. If worse than the worst, a de novo synthesis of new. Followed by the cycle continues. The magic of procedures is GA_crossover () function, the new really good to see heroes who can be improved upon, it generated more than a few with a good.