neural network in Java

西安电子科技大学模式识别原理与应用课件1-Xi" an University of Electronic Science and Technology Pattern Recognition Theory and Application of a courseware

netlogo实现的二元货物的模拟仿真模型-netlogo to achieve the dual cargo simulation model

实现了fisher线性分类器的功能,源码使用matlab编程,通俗易懂-Realize the fisher linear classifier functions, source code using matlab programming, user-friendly

adaptive resonance theory 2 人工神经网络-two adaptive resonance theory of artificial neural networks

利用最大匹配法进行汉语句子的分词 最大匹配算法是最常用的分词算法，简单实用正确率可达到80%以上-the maximum matching method for the Chinese Sentence Word maximum matching algorithm is the most commonly used word segmentation algorithm, simple and practical accuracy rate can reach more than 80%

模糊pid仿真，先运行模糊推理系统设计程序a.m，并调入内存，然后运行模糊控制程序b.m,对pid控制参数进行整定。-Pid fuzzy simulation, Fuzzy Inference System to run the design process am, and transferred to memory, and then run the procedure of fuzzy control bm, of pid control tuning parameters.

Matlab遗传算法工具箱函数及应用实例 Matlab遗传算法工具箱函数及应用实例-Matlab genetic algorithm toolbox function and application of genetic algorithm Matlab toolbox function and application example

对模式识别中的字符串模板匹配问题进行的研究，设计了对任意字符串进行匹配和字符串相似度，即列文斯顿距离进行计算的算法-right pattern recognition template matching string for the research and design of the arbitrary strings and string matching similarity, Ken Livingstone distance calculation algorithm

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

模式识别――原理、方法及应用-pattern recognition-- principles, methods and applications