ABSTRACT
This chapter describes the basic principles of fuzzy logic and neural networks. It summarizes the applications of fuzzy logic and neural networks to food process automation. The computerized automation of food processes is more challenging than that of chemical or pharmaceutical processes. Food processes largely rely on operator's rules of thumb and are not fully automated. Defining membership functions for fuzzy sets is subjective and context dependent. Fuzzy logic can represent the imprecise meaning of natural language, and it is able to perform the imprecise reasoning that plays an important role in human decision making under uncertainty and imprecise information. A linguistic variable is a variable whose values are words or sentences in natural language. A linguistic hedge is an operator used to modify a term of a fuzzy set to generate a new term. In dispositional reasoning, the premises contain explicitly or implicitly the fuzzy quantifier "usually".
