doi:

DOI: 10.3724/SP.J.1219.2013.00693

Information and Control (信息与控制) 2013/42:6 PP.693-699

Multiple Neural Network Model Based on Data Partition Using Feature Clustering


Abstract:
For the shortage of poor robustness in the description of nonlinear systems with single data model, a multiple neural network modeling method based on data partition using feature clustering is proposed. By structuring feature function, the data set is clustered and divided into multiple data subsets. The partitioned data subsets are used to build sub-models by RBF (radial bases function) neural network. Then the sub-models are adaptively weighted by minimizing mean square error in order to improve the model accuracy and model robustness. Finally, the proposed modeling method is applied to nonlinear system and moisture regain soft sensing in textile slashing process, and is compared with the single neural network model and the equivalently weighted multiple neural network model. The experimental results show that the root mean square error and the max absolute error of the proposed multiple neural network model are less than the single neural network model and the equivalently weighted multiple neural network model.

Key words:nonlinear system,multiple neural network,data partition,feature clustering,adaptive weight

ReleaseDate:2015-04-15 18:52:39



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