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Controller of PID
Update : 2025-03-04 Size : 43kb Publisher : 涂畅辉

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1) the distribution pattern of the original data requirements 2) known model types should be more comprehensive 3) suitable for multi-objective pattern recognition 4) extrapolation ability is limited 5) the qualitative d
Update : 2025-03-04 Size : 17.13mb Publisher : 三苏

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Loop feedback network (Hopfield) features: 1) the qualitative data of the pattern recognition 2) rely on attractor for pattern recognition 3) its function can be made of BP network to realize, but fast.
Update : 2025-03-04 Size : 58kb Publisher : 三苏

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1) is suitable for large sample of unsupervised classification 2) the results often need to use with statistical analysis to explain the classification results 3) to identify new types, but poor function.
Update : 2025-03-04 Size : 45kb Publisher : 三苏

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Radial basis function (RBF) network features: 1) can be used in any dimensional space interpolation 2) interpolation and the training speed slower 3) once the training is successful, as long as the right to store coeffic
Update : 2025-03-04 Size : 78kb Publisher : 三苏

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Use simulink simulation, realization flow calculation. ʱ ?? ʱ ?? Very detailed, can help to study the trend of computing and simulink.
Update : 2025-03-04 Size : 39kb Publisher : Aries_zh

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Use PSO reactive power dispatch, considering the economy, with relatively good results. You can learn
Update : 2025-03-04 Size : 63kb Publisher : Aries_zh

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Artificial neural network matlab realize, there are specific codes and a plurality of M files
Update : 2025-03-04 Size : 15kb Publisher : 李辉

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Artificial neural network stochastic neural network to achieve, there are M-file, you can modify
Update : 2025-03-04 Size : 3kb Publisher : 李辉

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Linear neural network implementations, including 13 M-file can be used to learn, very practical
Update : 2025-03-04 Size : 8kb Publisher : 李辉

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AI feedback neural network matlab programming, the program contains a wealth of resources
Update : 2025-03-04 Size : 17kb Publisher : 李辉

Support Vector Machine (SVM) was first proposed in 1995 by Cortes and Vapnik [15] for solving classification and regression problems. The solving strategy of SVM on the multiple classification problems is commonly “one t
Update : 2025-03-04 Size : 28kb Publisher : guolei
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