ABSTRACT
This chapter discusses the way in which fuzzy systems and neural net technology can be applied to process modeling and control and to fault detection and diagnosis in chemical process engineering. Neural networks and fuzzy systems share the common ability to deal with difficulties arising from uncertainty, impression, and noise in this natural environment. Fuzzy logic evolved from the need to model the type of vague or ill-defined systems that are difficult to handle using conventional binary value logic, but the methodology itself is grounded in mathematical theory. The chapter explores the mathematical notation required to describe fuzzy systems and reviews existing application and research area. It outlines why neural nets are so advantageous for representation of information in chemical process engineering and explains the characteristics of neural nets focusing on the neuron, the configurations and the learning algorithm. Neural nets mimic learning processes and are named after the network of neurons formed in the human brain.
