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首页 » AI-NN-PR » 为遗传算法提供基类,该基类将评价函数值直接作为适合度,采用新个体直接替换老个体的整体再生法...

为遗传算法提供基类,该基类将评价函数值直接作为适合度,采用新个体直接替换老个体的整体再生法...

于 2022-03-19 发布 文件大小:17.24 kB
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为遗传算法提供基类,该基类将评价函数值直接作为适合度,采用新个体直接替换老个体的整体再生法 -genetic algorithm to provide the base class, the base class will be evaluated as a direct function of fitness, Individual direct new replacing the old individual"s overall regeneration

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    The code on this diskette has been organized by chapter. Each subdirectory containing listing has a readme.txt describing program usage and any relevent file formats. The readme will also describe how to compile the target programs under IBM OS/2 (TM) using the using C Set++ compiler (TM). All programs on this diskette have been compiled and tested in this environment. The majority of programs on this diskette should port to other environments with only minor adjustments. The exception to this are those programs which utilize presentation manager for graphical display of program data. In particular, the grid1 programs in chapt4ackprop and chapt3 fall into this latter category.
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  • 本人编写的incremental 随机神经元网络算法,该算法最大的特点是可以保证approximation特性,而且速度快效果不错,可以作为学术上的比较和分析。
    本人编写的incremental 随机神经元网络算法,该算法最大的特点是可以保证approximation特性,而且速度快效果不错,可以作为学术上的比较和分析。目前只适合benchmark的regression问题。 具体效果可参考 G.-B. Huang, L. Chen and C.-K. Siew, “Universal Approximation Using Incremental Constructive Feedforward Networks with Random Hidden Nodes”, IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 879-892, 2006. -I prepared by incremental random neural network algorithm, which is characterized by the largest approximation properties can be guaranteed, and fast good results can be used as an academic comparison and analysis. The current benchmark is only suitable for the regression problem. Specific effects may refer G.-B. Huang, L. Chen and C.-K. Siew,
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    积分:1
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    内附两个源代码,其中一个源代码是识别程序的,另一个是一个矩阵类库的,所以一并附上。 ~..~因为已经保存了训练好的网络权值,所以第一次使用识别程序时,可以直接运行Release目录下的可执行文件,然后对图片目录中的测试图片进行读入、识别。 当然读者也可以自行用训练样本训练网络,不过要特别注意训练样本的选择,否则可能造成识别率很低。训练样本选择的原则是,尽可能的有代表性,在训练时间不至于太长的情况下,训练样本数目尽量多。 -containing 2 source code, which is a source identification procedures, and the other is a matrix class library. So accompanied. ~ ~ .. Has been preserved because of the trained network weights, the first use of the identification procedures, can be run directly Release directory executable file, Then the catalog of tests conducted read into the picture, recognition. Certainly readers can use their own training samples training network, but special attention should be paid to the choice of training samples, Otherwise, it could cause a very low recognition rate. Training sample selection principle is that as far as possible be representative, in the training of the time not too long under the circumstances, the number of training samples as much as possible.
    2022-08-11 21:14:16下载
    积分:1
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