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- W2382143368 abstract "Aiming to solve the problems of long training time,low precision in processing complex problem,and a local minimum in single neural networks,a multi-module cooperative neural network(MMCNN) was proposed.Its structure has hierarchical character.Sample data was first detached by the fuzzy clustering method,and then the neural network was partitioned into several sub-nets based on the clustering results.The linking weights were elicited by solving equations.For a given input data,some multi-modules were selected to deal with it.The approximating performance was improved by combining divide-and-conquer and learning ensemble strategies.A sub-net selection method was designed based on distance measurements.Simulation results demonstrate that a multi-module cooperative neural network can heighten approximating ability effectively for complicated problems,and the training time is faster than in a single back-propagation neural network." @default.
- W2382143368 created "2016-06-24" @default.
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- W2382143368 date "2011-01-01" @default.
- W2382143368 modified "2023-09-23" @default.
- W2382143368 title "A multi-module cooperative neural network" @default.
- W2382143368 hasPublicationYear "2011" @default.
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