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- W2378879504 abstract "To overcome the two main flaws of three-layer artificial neural network,i.e.tendency to overfitting and difficulty to determine the optimal number of hidden layer nodes,a modified errors back propagation algorithm containing ridge regression(BP-RR) is proposed.BP-RR determines the number of the hidden layer nodes according to the number of observed vectors in training sample.Firstly,BP is applied to learn from the training sample to the best of its abilities.Secondly,considering that there exists the outstanding correlation or multicollinearity among the hidden-layer-node output data,ridge regression is employed to obtain the optimal weights associated the hidden layer nodes with output layer nodes and the optimal thresholds of the output layer nodes instead of the original values obtained by BP;and then the model with the good predict ability is developed.The ridge regression uses an evolution algorithm to optimize ridge parameter according to the predict accuracy of the model.Further,BP-RR is employed to develop the naphtha dry point soft sensor.The results show that the good predict ability model with the robust character is obtained,and that the predict accuracy of BP-RR is superior to that of ANN." @default.
- W2378879504 created "2016-06-24" @default.
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- W2378879504 date "2007-01-01" @default.
- W2378879504 modified "2023-09-24" @default.
- W2378879504 title "Modified Errors Back Propagation Algorithm Based on Ridge Regression and Its Application to Soft Sensor" @default.
- W2378879504 hasPublicationYear "2007" @default.
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