An improved pattern-based prediction model for a class of industrial processes

Abstract

For the problem of simplifying pattern-based modeling procedures, an improved pattern-based modeling method is put forward via pattern classification for a class of complex processes. It is a pure data-driven modeling method using statistical attributes of the processes. At the beginning of the paper, a method of system dynamics description based on pattern moving is introduced. Then, an improved method of pattern-moving-based prediction modeling is put forward, and it simplifies the pattern-moving-based modeling method by integrating an initial model and a classification mapping. It consists of two parts: system pattern construction and pattern classification. And a constructive classification neural network (CCNN) is designed to describe system dynamics by classifying the system pattern, and its generalization is discussed. Finally, simulations using data of an actual production process demonstrate the feasibility of the proposed modeling method, and the effectiveness of the CCNN is verified using comparison experiments.