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5下载：
遗传算法改进的模糊C均值聚类的matlab源程序 可以运行 格式需要更改,Improved genetic algorithm fuzzy C-means clustering can run the matlab source code need to change format
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遗传算法改进的模糊C-均值聚类MATLAB源码
模糊C-均值算法容易收敛于局部极小点，为了克服该缺点，将遗传算法应用于模糊C-均值算法(FCM)的优化计算中，由遗传算法得到初始聚类中心，再使用标准的模糊C-均值聚类算法得到最优分类结果。
-Value algorithm (FCM) of the optimization calculations, by the genetic algorithm is the initial cluster centers, and the
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遗传算法改进的模糊C均值聚类的matlab源程序,可以运行,格式需要更改-Improved genetic algorithm fuzzy C means clustering matlab source code, you can run, you need to change the format
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本算法是实现基于KNN的基因遗传算法，是对KNN算法的改进，具有更好的分类效果。-gaKnn[Genetic Algorithm Optimized K Nearest Neighbor Classification framework] is a frameowork for KNN optimization with a genetic algorithm.
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用Ｋ均值和遗传算法实现了半监督聚类算法，这是个一个已经发表的论文的源程序-Using K-means and genetic algorithm to achieve a semi-supervised clustering algorithm, this is a paper published source
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遗传算法解决TSP问题matlab代码
详细说明代码含义即实现步骤-Genetic algorithm to solve TSP problem matlab code detailed descr iption of the code means that the implementation steps
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模糊C-均值算法容易收敛于局部极小点，为了克服该缺点，将遗传算法应用于模糊C-均值算法(FCM)的优化计算中，由遗传算法得到初始聚类中心，再使用标准的模糊C-均值聚类算法得到最优分类结果。-Fuzzy C-means algorithm converges to local minimum points easily, in order to overcome the shortcomings of genetic algorithm is applied to fuzzy C-means al
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基于遗传算法的k-means聚类分析代码 欢迎下载-Based on Genetic Algorithm k-means cluster analysis of the code are welcome to download
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遗传算法是弥补了EM算法的缺点，是现在比较流行的一种利用迭代计算全局最优值的一种算法。遗传算法是借鉴了自然遗传学，因此它们的基本思想是想通的。-Genetic algorithm is made up for the shortcomings of EM algorithm, is now more popular means of a global optimum value of iterative calculation algorithm. Genetic algorithm is bo
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基于遗传算法的K-means算法研究，有相关程序和算法说明。-Genetic Algorithm Based K-means algorithm, related program and algorithm descr iption.
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数据挖掘，聚类，遗传算法，k-means算法，基于遗传算法的k-means聚类方法。-Data mining, clustering, genetic algorithm, k-means algorithm, based on genetic algorithm k-means clustering method.
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遗传算法具有全局搜索的功能，能够克服K-means算法的缺点。-Genetic algorithm global search function to overcome the shortcomings of K-means algorithm.
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k-menas using genetic algorithm for centroid initialization
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遗传算法 优化K均值算法 实现两种算法的优势互补 -Genetic Algorithm K-means algorithm to achieve complementary advantages of the two algorithms
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遗传算法优化B P神经网络的目的是通过遗传算法得到更好的网络初始权值和阈值, 其
基本思想就是用个体代表网络的初始权值和阈值、 个体值初始化的B P神经网络的预测误差作为该个体的适应度值, 通过选择、 交叉、 变异操作寻找最优个体, 即最优的B P神经网络初始权值。除了遗传算法之外, 还可以采用粒子群算法、 蚁群算法等优化B P神经网络初始权值。-Genetic algorithm to optimize BP neural network is designed by means of g
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Real world problems often present multiple, frequently conflicting, objectives.
The research for optimal solutions of multi-objective problems
can be achieved through means of genetic algorithms, which are inspired
by the natural process of evo
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遗传算法用在聚类方式时，其中的一个重要应用就是k-means算法，本文给出了源代码。-When the genetic algorithm used in the clustering mode, which is an important application of k-means algorithm, this paper presents the source code.
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遗传算法改进的C模糊聚类算法源程序.已运行成功，可以作适当修改。-Improved genetic algorithm fuzzy clustering algorithm C source. Has run successfully, you can make appropriate changes.
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该算法引入遗传算法对模糊c均值算法进行改进，并在iris数据集中进行实验验证，得到很高的正确率。-The algorithm genetic algorithm fuzzy c-means algorithm is improved, and focus on experiments in the iris data to obtain a high accuracy.
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使用k-means算法对图像进行分割，并利用遗传算法对k-means算法加以改进(The k-means algorithm is used for the segment of images, and the genetic algorithm is used to improve the k-means algorithm)
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