This paper proposes a TSK-type Neuro-Fuzzy system tuned with a novel learning algorithm. The proposed algorithm used an improved version of the standard Particle Swarm Optimization algorithm, it employs several sub-swarms to explore the search space more efficiently. Each particle in a sub-swarm correct her position based on the best other positions, and the useful information is exchanged amon...

The growth in amount of data available today has encouraged the development of effective data analysis methods to support human decision-making. Neuro-fuzzy computation is a soft computing hybridisation combining the learning capabilities of the neural networks with the linguistic representation of data provided by the fuzzy models. In this paper, a framework to build temporally local neuro-fuz...

Handwritten character recognition is an area with many applications. Over the last decade much research has gone into algorithms to develop systems, which accurately convert images of handwriting to text. At the same time, neuro-fuzzy classification models have been researched and proven to solve complex problems. In this paper, two popular models, Adaptive Neuro-Fuzzy Inference System (ANFIS) ...

This paper proposes a neuro-fuzzy approach for optimizing injection molding parameter settings. The approach consists of design of experiments and neuro-fuzzy systems. Experimental data shows that the proposed approach performs better than the traditional trial and error practices usually involved in the injection molding process.

this paper presents the approach of neuro fuzzy systems to design autonomous vehicle control system. The purposed intelligent controller deliberates obstacles avoidance, unstructured environment adaptation and speed scheduling of autonomous vehicle based on neuro-fuzzy with reinforcement learning mechanism. The purposed system provides the autonomous vehicle navigation and speed control in unst...

A neuro-fuzzy system is the combined the advance feature of fuzzy logic and neural network, it is simply a fuzzy inference system that is trained by the learning concept of neural network. In NFS learning mechanism fine-tunes the underlying fuzzy inference system. This paper presents fundamental concepts and parameterized comparison in the aspects of fuzzy logic, neural network and neuro-fuzzy ...

Abstract. In this paper an algorithm for neuro-fuzzy identification of multivariable discrete-time nonlinear dynamical systems is proposed based on a decomposed form as a set of coupled multiple input and single output (MISO) Takagi-Sugeno (TS) neuro-fuzzy networks. An on-line scheme is formulated for modeling a nonlinear autoregressive with exogenous input (NARX) neuro-fuzzy structure from sam...

As a result from the demanding of process safety, reliability and environmental constraints, a called of fault detection and diagnosis system become more and more important. In this article some basic aspects of TSK (Takigi Sugeno Kang) neuro-fuzzy techniques for the prognosis and diagnosis of manufacturing systems are presented. In particular, a neuro-fuzzy model that can be used for the ident...

This work is an attempt to illustrate the usage and effectiveness of soft computing techniques in the estimation and control of multi input and multi output systems. This paper focuses on neuro-fuzzy system ANFIS (Adaptive Neuro Fuzzy Inference system). An Adaptive Network based Fuzzy Interference System architecture extended to cope with multivariable systems has been used. The performance of ...

The design and optimization process of fuzzy controllers can be supported by learning techniques derived from neural networks. Such approaches are usually called neuro-fuzzy systems. In this paper, we describe the application of an updated version of the neuro-fuzzy model NEFCON to a real plant. The NEFCON model is able to learn and optimize the rulebase of a Mamdani-type fuzzy controller onlin...