状态空间方法建立的线性二自由度的车辆模型

matlab的优化算法教程matlab的优化算法教程(asdfdsgfdagdsagdsafgsdgdsgds)

这个工具是同伴的图书软件的复杂性管理的模糊系统 ，斯普林格，2010。 一个新的规则为基础的系统类型，称为规则为基础的网络，已与行动建议操作规则库的互联网络，形成一个等效的单规则库。这些行动最近已实现为基于Matlab工具箱，但仅限于个人使用的节点对。此工具自动执行正确网络中的所有节点的基本操作。该工具会让你更容易实现用尽可能少的用户交互的各种规模的规则为基础的网络 (This tool is companion software for the book Complexity Management in Fuzzy Systems , Springer, 2010. A novel type of rule based system, called a rule based network, has been suggested with operations to manipulate networks of interconnected rule bases to form an equivalent single rule base. These operations have recently been implemented as a Matlab based toolbox, but are limited to individual use on pairs of nodes. This tool automatically performs the basic operations correctly on all nodes in a network. The tool will make it much simpler to implement rule based networks of all sizes with as little user interaction as possible. )

在Matlab中画一个给定参数的T分布的图像。(plot a figure of a data for T distribution.)

使用matlab中的三次样条差值函数实现曲线拟合(Using cubic spline matlab difference in function and curve fitting)

无限冲激响应数字滤波器的设计中的各个例子的程序，希望对大家有用(infinite impulse response filter design examples of the various procedures in the hope that it may be useful to)

实现了 SVM 在医学图像分类中的应用，通过医学图像的实验结果验证了该方法的有效性。 对 SVM 算法进行了深入研究，结合量子行为的粒子群算法的优点，提出了一种量子行为的粒子群算法和 SVM 结合的混合分类模型，应用于医学图像的分类中。 深入研究了 K-means 算法，结合了无监督聚类和监督分类方法的优点，提出了一种 K-means 和 SVM 结合的混合分类模型，并应用于医学图像的分类中。 利用粒子群和量子行为的粒子群算法对 K-means 算法进行了改进，并将改进的K-means 算法与 SVM 算法结合形成全自动分类模型，并且把它应用于医学图像的分类中。 (SVM in the classification of medical image applications, medical images through the experimental results verify the effectiveness of the method. SVM algorithm conducted in-depth research, combined with the quantum behavior of the advantages of particle swarm optimization, PSO and SVM combined behavior of a quantum mixed classification model, applied to medical image classification. In-depth study of the K-means algorithm combines the advantages of unsupervised clustering and supervised classification method, propose a hybrid combination of a K-means and SVM classification model, and applied to the classification of medical images. Improved particle swarm algorithm using particle swarm and quantum behavior of the K-means algorithm, and improved K-means algorithm and SVM algorithm combined with the formation of a fully automatic classification model, and apply it to medical image classification.)

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pso解决两目标函数极值问题，适合初学多目标的同学(Pso solve two goals for beginners, the function extreme problem multi-objective classmates)

用于——正态性检验：正态概率纸法的matlab源程序(For- test of normality: normal probability paper method matlab source)

particle filtering RBPF in matlab