Publisher's Synopsis
Encyclopaedia of Fuzzy Logic: Controls, Concepts, Theories and Applications introduces new concepts and theo¬ries of Fuzzy Logic Control for the application and development of robotics and intelligent machines. Fuzzy control (FC) using linguistic information possesses several advantages such as robustness, model-free, universal approximation theorem and rules-based algorithm. The inaccuracy and uncertainty are two aspects that may be part of the information. There are two theories used to deal with inaccuracy and uncertainty: classic sets (crisp) theory and probabilities theory, respectively. However, these theories do not always capture the information content provided by humans in natural language. The classic sets theory cannot deal with the fuzzy aspect of information while the probabilities theory is more suited to handle frequency information than those provided by humans. These theories have been used in systems that use human-provided information. These theories are closely linked with each other. When the fuzzy sets theory is used in a logic context, as knowledge-based systems, it is known as fuzzy logic (term used in this chapter). The fuzzy logic is currently one of the most successful technologies for the development of process control systems, due to low implementation cost, easy maintenance and the fact that complex requirements may be implemented in simple controllers. Encyclopaedia of Fuzzy Logic: Controls, Concepts, Theories and Applications introduces new concepts and theo¬ries of Fuzzy Logic Control for the application and development of robotics and intelligent machines. Fuzzy control (FC) using linguistic information possesses several advantages such as robustness, model-free, universal approximation theorem and rules-based algorithm. The inaccuracy and uncertainty are two aspects that may be part of the information. There are two theories used to deal with inaccuracy and uncertainty: classic sets (crisp) theory and probabilities theory, respectively. However, these theories do not always capture the information content provided by humans in natural language. The classic sets theory cannot deal with the fuzzy aspect of information while the probabilities theory is more suited to handle frequency information than those provided by humans. These theories have been used in systems that use human-provided information. These theories are closely linked with each other. When the fuzzy sets theory is used in a logic context, as knowledge-based systems, it is known as fuzzy logic (term used in this chapter). The fuzzy logic is currently one of the most successful technologies for the development of process control systems, due to low implementation cost, easy maintenance and the fact that complex requirements may be implemented in simple controllers.