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Particle Swarm Optimization

说明：  利用matlab中随机森林工具包数据进行预测(RF toolkit data in matlab was used for prediction)

This matlab code demonstrates independent component analysis.

说明：  K3销售订单合同号和摘要更新娄据库金蝶K3老单二次开发,源代码,案例, 源代码,案例-Kingdee K3 old single secondary development, source code, case(K3 Sales Contract No. and the update database)

ldpc的编码和译码方法，译码我验证过了，都是对的，包括wbf和bf译码方法，编码方法包括四种不同的编码方法(LDPC encoding and decoding method, decoding I verified, all is right, including any WBF and bf decoding method, the encoding methods include four different coding methods )

研究上课所讲谱分析方法，利用实验验证书中的结论，掌握各种谱分析方法，学会实验设计和实验结果分析。 所应用到的谱分析方法，包括： 1） 非参数化方法：周期图（直接法）、BT法（间接法），Welch平均周期图法 2） 参数化方法： RELAX、Capon 3） 空间谱估计：常见的DOA方法（Capon） (Experimental Objective: To study methods of spectral analysis class talking about the use of experimental verification of the conclusions of the book, to master a variety of spectral analysis methods, learn experimental design and experimental results. Experiment content: applied to the spectral analysis methods, including: 1) non-parametric methods: periodogram (direct method), BT method (indirect method), Welch average periodogram 2) parameterization: RELAX, Capon 3) spatial spectrum estimation: common of DOA method (Capon))

说明：  主成分析在matlab中应用有源程序,关于主要因素的提取(Principal Component Analysis in matlab application has source code, the main factors on the extraction)

神经网络的 语音识别 初学者可以看看 希望对大家有帮助(Speech recognition neural network can look at beginners)

粒子滤波算法在非线性系统中得到了广泛的应用，其精度取决于目标概率函数和重要性函数是否相近，并且样本退化问题也影响了算法的性能。针对粒子滤波算法中样本退化的问题，本文提出一种基于奇异值分解的粒子滤波算法。该算法通过使用奇异值分解方法得到的重要性概率密度函数更接近于目标概率分布，降低样本退化的影响，提高了滤波器的精度，然后在列车组合定位系统的数学模型中应用该算法进行仿真实验。 (Particle filter algorithm for nonlinear systems is widely used, its accuracy depends on whether the target probability function and importance of the functions are similar, and sample degradation also affects the performance of the algorithm. For particle filter algorithm sample degradation problem, this paper proposes a particle filter algorithm based on singular value decomposition. The algorithm by using singular value decomposition of the importance of the probability density function method to get closer to the target probability distribution, reduce sample degradation, improve the accuracy of the filter, and then apply the algorithm in combination mathematical model train positioning system simulation experiments. The simulation )

A Guide to MATLAB Object-Oriented Programming - Andy H. Register