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MATLAB多项式数值分析教程MATLAB Numerical Analysis polynomials tutorial(MATLAB Numerical Analysis polynomial MATLAB Numerical Analysis polynomials tutorial tutorial)

一本关于遗传算法在MATLAB上经典教程(study)

此为BP感知神经分类器,自己写的.有须要的请下载,很不错的.(this perception of BP neural classifier himself wrote it. The need to please download, very good.)

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说明：  该程序是使用matlab编写的支持向量机程序，它可用于分类/预测等方面。(it is very useful!)

本论文的主要内容包含以下几个方面: 首先，论述了图像融合技术的基本理论、现状和方法，并对多聚焦图像融合 算法及融合结果的评价方法做了分类。 其次，论述了小波理论及其图像的小波分解和重构，研究了小波分解层数及 小波基函数的选择对多聚焦图像融合效果的影响，并给出了在多聚焦图像融合算 法中最佳小波分解层数和小波基函数的选取 再次，研究了基十小波变换的多聚焦图像融合方法，并提出了改进后的基十 小波变换区域对比度的多聚焦图像融合方法，并利用Matlab工具进行算法进行了 仿真实验 最后，总结本论文的研究成果和不足，并指出发展方向。(The following main parts are included. Firstly, this paper discussed the basic theory, present state and means of image fusion technology, classified multi-focus image fusion algorithm and evaluation means of image fusion consequences. Secondly, this paper discussed the wavelet theory and the wavelet decomposition and reconstruction of image, studied the effects on selection of wavelet basis function and wavelet decomposition level to multi-focus image fusion, at last the best choices are proposed. Thirdly, a new method: regional contras of wavelet transformation are proposed for multi-focus image fusion based on wavelet transformation and Matlab is employed to simulation algorithm Finally, this paper summarized the research achievement and insufficient of this paper and pointed out development direction. )

这个Matlab程序实现最速下降算法。最速下降法是一种最基本的算法，它在最优化方法中占有重要地位.最速下降法的优点是工作量小，存储储变量较少,初始点要求不高；缺点是收敛慢，最速下降法适用于寻优过程的前期迭代或作为间插步骤，当接近极值点时，宜选用别种收敛快的算法. (The Matlab program to achieve the steepest descent algorithm. Steepest descent method is a basic algorithm, it occupies an important position in the optimization method. Advantage of the steepest descent method is the small amount of work, less storage reservoir variables, the initial point less demanding drawback is slow convergence, the steepest descent method for the optimization process of the early iterations, or as inserted steps are advised to use other kinds of fast convergence of the algorithm when close to the extreme point.)

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