Inference engine fuzzy logic

A fuzzy logic system maps crisp inputs into crisp outputs using the theory of fuzzy sets. In a fuzzy logic system, an inference engine works with fuzzy rules. 

The engine takes inputs, some of which may be fuzzy, and generates outputs, some of which may be fuzzy. The fuzzy core of the inference engine is bracketed by one step that can convert crisp data into fuzzy data, and another step that does the reverse. Figure 2.10 shows the general procedures involved in a fuzzy logic system as follows. 

Fuzzy logic controllers are governed by a set of if-then rule known as a knowledge base or rule base. The fuzzy rule base drives the inference engine to produce the output in response to one or a set of inputs. The knowledge base established on a heuristic approach is given as follows. 

Fuzzy logic control (FLC) is a simulation of logical reasoning of human brain it maps an input space to a corresponding output space based on fuzzy rules specified in if-then format knovm as knowledge base. Fuzzy logic-based control includes a fuzzification interface, an inference engine and a defuzzification interface. For details... 

Both classical and symbolic logic systans do not care for uncertainty, and their inferences are all certain, which gives the impression to engineers that they are also correct and valid universally. However, since four decades Loth Asker Zadeh [12] proposed a logic with uncertainty ingredients that is the fuzzy logic, which is bound to be used more frequently in future. 

At the beginning, the basics of the fuzzy sets and fuzzy logic rules will be explained. After this introduction, different types of mathematical treatments of If-Then rules are presented, as well as the use of such in the fuzzy controllers. At the end, fuzzy linear systems and their extension to intuitionistic linear systems as an inference engine are discussed. 

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