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...

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...

We show that prediction of travel time on a 28-km long highway section based on on-line travel time measurements with video is practicable by data mining and neuro-fuzzy methods. We introduce two new prediction models. The first one is a result of GUHA style data mining analysis and Total Fuzzy Similarity method, and the second one is a hierarchical model based on neuro-fuzzy modelling. Compari...

Internal cognitive models are useful methods for the implementation of motor control [1]. The approach can be applied more generally to any intelligent adaptive control problem where the dynamics of the system (plant) is unknown and/or changing. In particular, paired inverse-forward models have been shown successfully to control complex motor tasks using robotic manipulators [1]. One way this i...

This study presents a novel controller of magnetic levitation system by using new neuro-fuzzy structures which called flexible neuro-fuzzy systems. In this type of controller we use sliding mode control with neuro-fuzzy to eliminate the Jacobian of plant. At first, we control magnetic levitation system with Mamdanitype neuro-fuzzy systems and logical-type neuro-fuzzy systems separately and then...

background: noise prediction is considered to be the best method for evaluating cost-preventative noise controls in industrial workrooms. one of the most important issues is the development of accurate models for analysis of the complex relationships among acoustic features affecting noise level in workrooms. in this study, advanced fuzzy approaches were employed to develop relatively accurate ...

The aim of the paper is to demonstrate the neuro-fuzzy support of knowledge management in social regulation. Knowledge, defined as human capability of making data and information useful for decision making processes, could be understood for social regulation purposes as explicit and tacit. Explicit knowledge relates to the community culture indicating how things work in the community based on s...

To use fuzzy controllers for automization tasks appropriate fuzzy sets and fuzzy rules have to be deened. This can be diicult in some domains, and the resulting controller has to be tuned. Neuro{fuzzy models can help in this tuning process by adapting fuzzy sets and creating fuzzy rules. Combinations of neural networks and fuzzy controllers are suitable if there is only partial knowledge in the...

Hybrid neuro-fuzzy systems have been in evidence during the past few years, due to its attractive combination of the learning capacity of arti2cial neural networks with the interpretability of the fuzzy systems. This article proposes a new hybrid neuro-fuzzy model, named hierarchical neuro-fuzzy quadtree (HNFQ), which is based on a recursive partitioning method of the input space named quadtree...

OBJECTIVES To develop a neuro-fuzzy system to predict the presence of prostate cancer. Neuro-fuzzy systems harness the power of two paradigms: fuzzy logic and artificial neural networks. We compared the predictive accuracy of our neuro-fuzzy system with that obtained by total prostate-specific antigen (tPSA) and percent free PSA (%fPSA). METHODS The data from 1030 men (both outpatients and ho...