In this paper, a neuro-fuzzy algorithm has been implemented to improve the path planning of a mobile robot based on modification of vector field histogram (VFH) approach using neuro-fuzzy algorithm. A back propagation neural network has been used for detection of unknown obstacle to avoid collision. A neural network model is used to learn (off line) many critical status of obstacle topology. Fu...

In this paper, a gradient-based back propagation dynamical iterative learning algorithm is proposed for structure optimization and parameter tuning of the neuro-fuzzy system. Premise and consequent parameters of the neuro-fuzzy model are initialized randomly and then tuned by the proposed iterative algorithm. The learning algorithm is based on the first order partial derivative of the output wi...

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...

There has been a growing interest in combining both neural network and fuzzy system, and as a result, neuro-fuzzy computing techniques have been evolved. ANFIS (adaptive network-based fuzzy inference system) model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. In this paper, a novel structure of unsupervised ANFIS is presented to solve differential e...

Fuzzy systems are currently being used in a wide field of industrial and scientific applications. Since the design and especially the optimization process of fuzzy systems can be very time consuming, it is convenient to have algorithms which construct and optimize them automatically. One popular approach is to combine fuzzy systems with learning techniques derived from neural networks. Such app...

Several adaptation techniques have been investigated to optimize fuzzy inference systems. Neural network learning algorithms have been used to determine the parameters of fuzzy inference system. Such models are often called as integrated neuro-fuzzy models. In an integrated neuro-fuzzy model there is no guarantee that the neural network learning algorithm converges and the tuning of fuzzy infer...

This paper investigates the development of the Adaptive Neuro-Fuzzy Systems with Local Recurrent Structure (ANFS-LRS) and their application to Fault Detection and Isolation (FDI). Hybrid learning, based on a fuzzy clustering algorithm and a gradientlike method, is used to train the ANFS-LRS. The experimental case study refers to an application of fault diagnosis of an electro-pneumatic actuator...

In this work, an adaptive critic-based neuro-fuzzy is presented for an unmanned bicycle. The only information available for the critic agent is the system feedback which is interpreted as the last action the controller has performed in the previous state. The signal produced by the critic agent is used alongside the back propagation of error algorithm to tune online conclusion parts of the fuzz...