Positive and negative correlation input attributes oriented subnets based double parallel extreme learning machine (PNIAOS-DPELM) and its application to monitoring chemical processes in steady state.

Extreme learning machine (ELM) is an effective learning algorithm for single-hidden-layer feed-forward neural networks (SLFNNs). Due to its easiness in theory and implementation, ELM has been widely used in many fields. In order to further enhance the generalization performance of ELM, a positive and negative correlation input attributes oriented subnets based double parallel extreme learning machine (PCNCIAOS-DPELM) is proposed in this paper. A salient feature in the PNIAOS-DPELM is that there are two special subnets. In one of the two subnets, the input attributes have a positive correlation to the outputs. In another subnet, the input attributes have a negative correlation to the outputs. The two kinds of input attributes can be obtained by separating the input attributes into two categories using the correlation coefficient analysis. Then according to the categories, the two subnets can be established. The two subnets are based on well-trained auto-associative neural networks (AANNs), which can extract the nonlinear information of the input attributes and remove the redundant information. An advantage in PNIAOS-DPELM is that the proper number of the nodes in the hidden layer can be determined. To test the validity of PNIAOS-DPELM, it is applied to monitoring three chemical processes in steady state. Meanwhile, ELM, double parallel ELM (DP-ELM), and ELM with kernel (ELMK) were developed for comparisons. Experimental results demonstrated that PNIAOS-DPELM could achieve better regression precision and have better stable ability than ELM, DP-ELM, and ELMK did during the generalization phase. [ABSTRACT FROM AUTHOR]