Advanced-PID-control-

genetic algorithm example in matlab

This code is for quadtree partitioning of image...

灰色神经网络的预测算法，订单需求预测，matlab的经典算法(Gray neural network prediction algorithm, order demand forecasting, matlab classical algorithm)

BOundaries for CISPR 13

本书包含MATLAB使用和数学知识的丰富层次。编著本书有四个主要目的：（1） 帮助初学者顺利跨入MATLAB大门；（2） 全面、多层次、细致而深入地叙述MATLAB中数值、字符串、胞元、构架、逻辑、函数句柄六种重要数据类型的应用规则、相互配用和编程规范；（3） 由浅入深地阐述MATLAB三种建模、分析、仿真环境——数值计算、符号计算和Simulink环境的特征和使用要领；（4） 以实例讲述MATLAB代表的现代计算能力对传统算法和思维的影响。(Book contains rich levels of using MATLAB and mathematics knowledge. Compiled the book has four main purposes: (1) help beginners MATLAB entered the door smoothly (2) comprehensive, multi-level, meticulous and thorough account Numbers, strings, cell yuan, in the MATLAB framework, logic and function handles six important application rules, with each other and programming of the data type specifications (3) describes the MATLAB distinguished three kinds of modeling, analysis, simulation environment, numerical and symbolic computation and Simulink environment characteristics and use of the essentials (4) with examples about MATLAB on behalf of the modern computing power s influence on the traditional algorithm and thinking. )

毕业设计中，用Matlab实现了机器人最优路径规划中的全局路径规划，算法采用的是基于方向权的 tBgkUG 二叉树算法，同时结合了哈夫曼编码二叉树的 KNrtdZj综合。(Graduation design, using Matlab to achieve the optimal path planning of robot global path planning algorithm uses a binary tree algorithm based on the direction of the right tBgkUG , combined with the binary tree Huffman coding KNrtdZj comprehensive.)

蛙跳算法（SFLA）是一种全新的启发式群体进化算法，具有高效的计算性能和优良的全局搜索能力。(Leapfrog algorithm (SFLA) is a new heuristic population evolutionary algorithm, with high computational performance and excellent global search ability.)

low pass filter code

inverse dynamic of robot.nt to upload source code, you can

用MCE算法训练分类器，用Train.txt里的数据进行训练，用Test.txt的数据进行性能检测(Use MCE training algorithm of MCE to train the classifier, use the training data in Train.txt to train the classifier and use the data in the Test.txt to test the performance of the classifier )