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Development of a variable selection method for soft sensor using artificial neural network and nonnegative garrote
Affiliation:1. Department of Chemical Engineering, National Tsing Hua University, Hsinchu 30043, Taiwan;2. Department of Chemical and Materials Engineering, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan;3. Tsing Hua Intelligent Chemical Engineering Ltd. Co., Hsinchu 30043, Taiwan;4. Center for Energy and Environmental Research, National Tsing Hua University, Hsinchu 30043, Taiwan;1. Department of Chemical and Material Engineering, National Yunlin University of Science and Technology, Yunlin, 64002, Taiwan;2. Department of Chemical Engineering, National Tsing Hua University, Hsinchu, 30013, Taiwan
Abstract:This paper developed a new variable selection method for soft sensor applications using the nonnegative garrote (NNG) and artificial neural network (ANN). The proposed method employs the ANN to generate a well-trained network, and then uses the NNG to conduct the accurate shrinkage of input weights of the ANN. This paper took Bayesian information criterion as the model evaluation criterion, and the optimal garrote parameter s was determined by v-fold cross-validation. The performance of the proposed algorithm was compared to existing state-of-art variable selection methods. Two artificial dataset examples and a real industrial application for air separation process were applied to demonstrate the performance of the methods. The experimental results showed that the proposed method presented better model accuracy with fewer variables selected, compared to other state-of-art methods.
Keywords:Variable selection  Soft sensor  Nonnegative garrote  Artificial neural network
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