constract-decring

pso algorithm to solve job shop scheduling written on matlab

针对灰色预测的良好算法，这里边对灰色预测的整个过程进行了matlab的求解，全面可靠。(For the good gray prediction algorithm, where the edge of the whole process of the gray forecast matlab solving, comprehensive and reliable.)

音乐和的设计，硬件语言描叙，书上的源代码哦(Music and design, hardware language describes, the book s source code Oh)

含有台湾大学某老师提出的果蝇优化算法文档及程序，读者可下载进行学习(Containing a fruit fly optimization algorithm document and program put forward by a teacher of National Taiwan University, readers can download and learn.)

cpm连续相位调制系统的matlab仿真程序，全面。(cpm continuous phase modulation system matlab simulation program, comprehensive.)

分析电力系统风电出力数据的状态转移概率。在得到两三年的风电场发电出力数据后，可用该程序分析风电出力的具体变化，以及风电出力在相邻时刻不同状态之间的转移概率。(Analysis of power system wind power output data state transition probabilities. In two or three years to get the wind farm power output data, the program can analyze specific changes in wind power output, as well as wind power output in the adjacent time transition probabilities between different states.)

JArbitrager 我改进的一款套利软件 可以用在IB上(I improved a JArbitrager arbitrage software can be used on the IB)

implementation of TSP problem in matlab

说明：  实验一：基于LMS算法的“苹果-橘子”分类问题 实验二：基于LMS算法的T-G-F字符识别问题 通过以上两个实验，了解ADALINE网络的结构及计算机制，掌握LMS算法，并会利用该算法来解决简单的模式分类问题。1， 学习ADALINE网络的基本结构及其学习机制，并在Matlab中实现 2， 分别对上述两类分类问题进行实现，并根据实验结果完成实验报告 (Experiment 1: Based on LMS Algorithm Apple- Orange classification issues Experiment two: TGF-based character recognition problem LMS algorithm Through the above two experiments to understand the structure and computer network system ADALINE, master LMS algorithm, and will use the algorithms to solve simple pattern classification problem. 1, learn the basic structure and learning mechanism ADALINE network and to achieve in Matlab 2, respectively, for these two types of classification problems were implemented and completed in accordance with the experimental results of test report)

待辨识对象参数a=[1 -1.5 0.7] b=[1 0.5] 输入采用长度L=400的白噪声序列，输出 ，输入和输出数据均含不相关随机噪声，ρ（k）=1/k。利用上述递推公式，辨识系统参数。(To identify the object parameters a = [1-1.5 0.7] B = [1 0.5] Input the length L = 400 white noise sequence, the output, the input and output data are not related with random noise, ρ (k) = 1/k. Using the recursion formula, identification system parameters.)