ABSTRACT
This chapter discusses the foundation of neuro-fuzzy systems. It introduces Takagi, Sugeno, and Kang (TSK) fuzzy model and its difference from the Mamdani model. Under the idea of TSK fuzzy model, the chapter also discusses a neuro-fuzzy system architecture: Adaptive Network-based Fuzzy Inference System (ANFIS) that is developed by Jang. This model allows the fuzzy systems to learn the parameters adaptively. By using a hybrid learning algorithm, the ANFIS can construct an input-output mapping based on both human knowledge and numerical data. The adaptation of fuzzy inference system provides more physical insights for engineers to understand the relationship between the parameters. Neuro-fuzzy systems are multi-layer feedforward adaptive networks that realize the basic elements and functions of traditional fuzzy logic systems. The backpropagation neural network (BPNN) has been used extensively and proven useful in several engineering fields. In the BPNN, the link of nodes between two layers is characterized by a weighting value.
