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Briefly introduce the genetic algorithm is suitable for beginners Tutorial
Date : 2025-06-09 Size : 635kb User : ludelai

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Bp simulation bowl to the public!! And good training and then to simulate the contents of a simple welcome to download
Date : 2025-06-09 Size : 1kb User : rico

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The RBF neural network for any research to help the Friends are welcome to download, there are notes
Date : 2025-06-09 Size : 1kb User : rico

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Stabilization of wheeled mobile robot methods, from the everyday experience of the methods
Date : 2025-06-09 Size : 335kb User : 华胜

Optimize the use of genetic algorithms to solve 100 cities TSP problem, which was to select a departure from the resident, after each city once again, and finally returned to the resident s line, bringing the total minim
Date : 2025-06-09 Size : 1.18mb User : 李佳鹤

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The use of artificial neural networks and genetic algorithm procedures robot can automatically rule out the possibility of obstructions map.
Date : 2025-06-09 Size : 1.46mb User : 李佳鹤

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SVM classification Please read them carefully document your package and then write their specific functions
Date : 2025-06-09 Size : 2.43mb User : ladeng

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Classic example of the algorithm, the algorithm in detail, hoping that everyone likes
Date : 2025-06-09 Size : 965kb User : 高蕊

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This procedure adopted by the heuristic function, I can not prove that it can resolve any of the eight digital dilemma, if the more difficult the problem will have a lot of nodes, there may cause the lack of space, so tr
Date : 2025-06-09 Size : 2.87mb User : chenyan

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Produces a matrix of derivatives of network output wrt each network weight for use in the functions NNPRUNE and NNFPE.
Date : 2025-06-09 Size : 3kb User : 张镇

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Train a two layer neural network with the Levenberg-Marquardt method. If desired, it is possible to use regularization by weight decay. Also pruned (ie. not fully connected) networks can be trained. Given a set of corre
Date : 2025-06-09 Size : 3kb User : 张镇

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This function calculates Akaike s final prediction error estimate of the average generalization error for network models generated by NNARX, NNOE, NNARMAX1+ 2, or their recursive counterparts. [FPE, deff, varest, H] = nn
Date : 2025-06-09 Size : 2kb User : 张镇
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