This chapter discusses the foundation of neuro-fuzzy systems. First, we introduce Takagi, Sugeno, and Kang (TSK) fuzzy model [l,2] and its difference from the Mamdani model. Under the idea of TSK fuzzy model, we discuss a neuro-fuzzy system architecture: Adaptive Network-based Fuzzy Inference System (ANFIS) that is developed by Jang [3]. This model allows the fuzzy systems to learn the paramete...

Fuzzy Neural Networks (FNNs) techniques have been effectively used in applications that range from medical to mechanical engineering, to business and economics. Despite of attracting researchers in recent years and outperforming other fuzzy systems, Adaptive Neuro-Fuzzy Inference System (ANFIS) still needs effective parameter training and rulebase optimization methods to perform efficiently whe...

BOD is a parameter frequently used to evaluate the water quality on different rivers. The aim of the present study is to investigate applicability of artificial intelligence techniques such as ANFIS (Adapti ve Neuro-Fuzzy Inference System) in water quality BOD prediction for the case study, Mahi river at Khanpur in Thasara Taluka of Kheda District in Gujarat State, India. The proposed technique...

Fuzzy rule-based systems are nowadays one of the most successful applications of fuzzy logic, but in complex applications with a large set of variables, the number of rules increases exponentially and the obtained fuzzy system is scarcely interpretable. Hierarchical fuzzy systems are one of the alternatives presented in the literature to overcome this problem. This paper presents a multilevel f...

Conventional controller’s usually required a prior knowledge of mathematical modelling of the process. The inaccuracy of mathematical modelling degrades the performance of the process, especially for non-linear and complex control problem. The process used is Water-Bath system, which is most widely used and nonlinear to some extent. For Water-Bath system, it is necessary to attain desired tempe...

Conventional controller’s usually required a prior knowledge of mathematical modelling of the process. The inaccuracy of mathematical modelling degrades the performance of the process, especially for non-linear and complex control problem. The process used is Water-Bath system, which is most widely used and nonlinear to some extent. For Water-Bath system, it is necessary to attain desired tempe...

The main goal of this research is to develop a novel optimum neuro-fuzzy system for diagnosis the complex and dynamic systems. .It has used the Particle Swarm Optimization (PSO) technique for training the Adaptive Neuro Fuzzy Inference System (ANFIS) off-line. The proposed system has applied for diagnosis the faults of two complex Photovoltaic (PV) systems. They are used to feed the power for l...

This paper reviews the use of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for vector-controlled induction motor drives. While conventional schemes do not deal well with the highly nonlinear nature of motor control, fuzzy logic with its adjustability and neural networks with their adaptability have been shown to be excellent alternatives. ANFIS combines the advantages of fuzzy logic and neura...

This paper illustrates a Comparative study of highly non-linear, complex and multivariable Inverted Pendulum (IP) system on Cart using different soft computing techniques. Firstly, a Fuzzy logic controller was designed using triangular and trapezoidal shape Membership functions (MF's). The trapezoidal fuzzy controller shows better results in comparison to triangular fuzzy controller. Secondly, ...