36331-b40

用遗传算法求解TSP问题，matlab源程序，付P1.m与数据以供试验(solve TSP with GA)

说明：  做实验的一些心得体会，愿大家有所受益很好，欢迎大家学习，交流！(verygood！let we see！verygood！let we see！verygood！let we see！verygood！let we see！verygood！let we see！)

FactorTutorial with some improvements to help in the examples. There is no answer here

使用卡尔曼滤波对加了噪声的GPS定位数据进行滤波。(Calman filtering is used to filter the GPS location data with noise.)

说明：  详细介绍了宽带无线通信中OFDM的基本原理和关键技术。(The book describs the fundamental theory and key technology of OFDM in detail.)

说明：  利用matlab实现彩色图像的分割。算法主要是利用聚类算法(Use of matlab color image segmentation. Algorithm is the main clustering algorithm)

主要介绍数据拟合的方法，是自己学习后整理出来的程序，欢迎下载！(The method focuses on data fitting, after sorting out their own learning program, welcome to download!)

机器人栅格地图创建，机器人栅格地图的路径规划问题(Robot raster maps to create the path planning, robot grid map)

说明：  BP神经网络的仿真，能够拟合各种函数，支持梯度下降法和LM两种训练算法。所有代码自己编写，没有使用matlab自带的函数，是学习的最佳范本。(The simulation of BP neural network can fit various functions and supports two training algorithms, gradient descent method and LM. All the code is written by myself, without using the functions that come with matlab, it is the best model for learning.)

优秀论文及配套源码。首先阐述了负荷预测的应用研究现状，概括了负荷预测的特点及其影响因素，归纳了短期负荷预测的常用方法，并分析了各种方法的优劣；接着介绍了作为支持向量机（SVM）理论基础的统计学习理论和SVM的原理，推导了SVM回归模型；本文采用最小二乘支持向量机（LSSVM）模型，根据浙江台州某地区的历史负荷数据和气象数据，分析影响预测的各种因素，总结了负荷变化的规律性，对历史负荷数据中的“异常数据”进行修正，对负荷预测中要考虑的相关因素进行了归一化处理。LSSVM中的两个参数对模型有很大影响，而目前依然是基于经验的办法解决。对此，本文采用粒子群优化算法对模型参数进行寻优，以测试集误差作为判决依据，实现模型参数的优化选择，使得预测精度有所提高。实际算例表明，本文的预测方法收敛性好、有较高的预测精度和较快的训练速度。(first expounds the recent application research of load forecasting, summarized the characteristics of load forecasting and influencing factors, summed up common methods of short-term load forecasting, and analyzed the advantages and disadvantages of each method then introduced statistical learning theory and the principle of SVM as the basis of support vector machine (SVM ) theory, SVM regression model is derived this paper adopted least squares support vector machine (LSSVM) model, according to the historical load data and meteorological data of a certain area of Zhejiang Taizhou, Analysised the various factors affecting the forecast, summed up the regularity of load change , amended "outliers" in the historical load data,the load forecasting factors to be considered were normalized. The two parameters of LSSVM have a significant impact on the model, but it is still soluted based on the experience currently. So, this paper adopted particle swarm optimization algorithm to optimized )