Robust soft sensors based on integration of genetic programming, analytical neural networks, and support vector machines

Abstract

A novel approach for development of inferential sensors based on integration of three key computational intelligence approaches (genetic programming, analytical neural networks, and support vector machines) is proposed. The advantages of this type of soft sensors are their good generalization capabilities, increased robustness, explicit input/output relationships, self-assessment capabilities, and low implementation and maintenance cost.