During the twentieth century, many attempts were made for augmenting the intelligence of computer software with further capabilities. Adaptive learning algorithms were developed, making possible the initial developments in neural networks (NNs) in the 1950s. A very innovative learning approach was birthed by L. Zadeh in 1965 with the publication of his paper “Fuzzy Sets.” In that paper, the idea of a membership function based on multivalued logic, allowed a solid theory where technology bundled together thinking, vagueness, and imprecision. An engineering design starts from the process of thinking, i.e., a mental creation, and designers will use their linguistic formulation, with their own analysis and logical statements about their ideas. Then, vagueness and imprecision are considered as empirical knowledge to be incorporate in the model implementation of the system. Scientists and engineers try to remove most of the vagueness and imprecision of the world by making precise mathematical formulation of laws of physics, chemistry, and the nature in general. Sometimes, it is possible to have precise mathematical models, with strong constraints on nonidealities, parameter variation, and nonlinear behavior. However, if the system becomes more complex, the lack of ability to measure or to evaluate features, with a lack of definition of precise modeling, in addition to many other uncertainties and incorporation of human expertise, makes it almost impossible to explore such a very precise model for a complex real-life system. Fuzzy logic (FL) and NNs became the foundation for the newly advanced twenty-first century of smart control, smart modeling, intelligent behavior, and artificial intelligence (AI). This chapter discusses the basics and foundations of FL and NNs, with some applications in the area of energy systems, power electronics, power systems, and power quality.

Chapter Contents:

• 3.1 What is an intelligent system
• 3.2 Fuzzy reasoning
• 3.3 Introduction to fuzzy sets
• 3.4 Introduction to fuzzy logic
• 3.4.1 Defining fuzzy sets in practical applications
• 3.5 Fuzzy sets kernel

Inspec keywords: artificial intelligence; neural nets; fuzzy logic; power engineering computing; fuzzy set theory

Other keywords: smart control; power quality; intelligent behavior; smart modeling; power systems; neural networks; multivalued logic; energy systems; power electronics; fuzzy logic; artificial intelligence; fuzzy sets kernel; fuzzy reasoning; membership function

Subjects: Neural nets; Formal logic; Power engineering computing; Combinatorial mathematics