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...

the paper analyses issues leading to errors in graphic object classifiers. thedistance measures suggested in literature and used as a basis in traditional, fuzzy, andneuro-fuzzy classifiers are found to be not suitable for classification of non-stylized orfuzzy objects in which the features of classes are much more difficult to recognize becauseof significant uncertainties in their location and...

Original scientific paper This paper proposes a Neuro-fuzzy system for quantitative assessment of the effects of intelligent transportation systems and technologies on road fatalities. The basic idea in developing Neuro-fuzzy system is the fact that intelligent transportation systems and technologies activate some safety mechanisms and in turn the activation of safety mechanisms will have a pos...

Article Info Abstract This paper presents a Hybrid Adaptive Neuro Fuzzy Control technique for speed control of BLDC motor drives. The proposed controller is an integration of adaptive neuro fuzzy, fuzzy PID and PD controllers. The objective is to utilize the best attribute of fuzzy PID and PD controllers, which exhibits a better response than the neuro fuzzy controllers. The error back propagat...

In this paper, the problem of minimum-time trajectory planning is studied for a three degrees-offreedom (3-DOF) planar manipulator using a hierarchical hybrid neuro-fuzzy system. A first preprocessing step involves two components. The first component is NeFIK (for Neuro-Fuzzy Inverse Kinematics), a neuro-fuzzy network designed to learn and solve the inverse kinematics problem. The second one is...

The neural network's performance can be measured by efficiency and accuracy. The major disadvantages of neural network approach are that the generalization capability of neural networks is often significantly low, and it may take a very long time to tune the weights in the net to generate an accurate model for a highly complex and nonlinear systems. This paper presents a novel Neuro-fuzzy archi...

Abstract: Recently, supervised artificial neural networks have obtained success to reveal and provide quantitative information concerning defects in TNDE (Thermographic NonDestructive Evaluation). Supervised neural networks may converge to local minimum and their training procedure are usually long. In this study, a neuro-fuzzy approach is applied to characterize subsurface defects in TNDE. Sim...

The present article is a novel attempt in providing an exhaustive survey of neuro-fuzzy rule generation algorithms. Rule generation from artificial neural networks is gaining in popularity in recent times due to its capability of providing some insight to the user about the symbolic knowledge embedded within the network. Fuzzy sets are an aid in providing this information in a more human compre...

Neuro-fuzzy systems have recently gained a lot of interest in research and application. These are approaches that learn fuzzy systems from data. Many of them use rule weights for this task. In this paper we discuss the innuence of rule weights on the interpretability of fuzzy systems. We show how rule weights can be equivalently replaced by modiications in the membership functions of a fuzzy sy...