In this paper we present NEFCLASS, a neuro{fuzzy system for the classiication of data. This approach is based on our generic model of a fuzzy perceptron which can be used to derive fuzzy neural networks or neural fuzzy systems for spe-ciic domains. The presented model derives fuzzy rules from data to classify patterns into a number of (crisp) classes. NEFCLASS uses a supervised learning algorit...

314 Abstract— Neuro-Fuzzy systems are hybrid intelligent systems which combine features of both paradigmsfuzzy logic and artificial neural networks. Adaptive Neuro Fuzzy Inference System (ANFIS) is one of such architecture which is widely used as solution for various real world problems. This paper describes development of an ANFIS model for FPGA implementation. Model can be realized with hardw...

The article presents possibilities of using different artificial neural networks and neuro-fuzzy systems to solve certain engineering geodesy tasks. Special attention is paid to tasks connected with the construction of a numerical terrain model, transformation of coordinates from the “1965” system into the “2000” system, and prediction of a time series on the basis of results of GPS measurement...

Computer assisted diagnosis/surgery systems need improved solutions for visualization and interactions, as the virtual probe tools. This work presents such an interaction tool and its applications in medical imaging. The novelty is represented by the use of neuro-fuzzy systems in 3D virtual probe positioning; two such configurations are presented and compared to the crisp positioning system. We...

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...

The techniques in artificial intelligence are used in almost all the fields where human reasoning and uncertainties can be effectively modeled. The popular techniques in AI are fuzzy logic and neural networks which can be used either separately or applied together. When they are used in combined way, they are called Neuro-Fuzzy Systems. The reasons to combine these two paradigms come out of the...

This paper presents two new intelligent methods to linearize the Multi-Carrier Power Amplifiers (MCPA). One of the them is based on the Neuro-Fuzzy controller while the other uses two small neural networks as a polar predistorter. Neuro-Fuzzy controllers are not model based, and hence, have ability to control the nonlinear systems with undetermined parameters. Both methods are adaptive, low com...

In this paper, a novel neuro-fuzzy system has been introduced for the use of approximation and prediction of complex plants. The proposed system uses a parameter estimation network to provide coefficients of a linear combination, dynamically and then identifies a plant by approximating its target signal. So we have called it PENTA which stands for Parameter Estimation Network for Target Approxi...

A neuro-fuzzy adaptive control approach for nonlinear dynamical systems, coupled with unknown dynamics, modeling errors, and various sorts of disturbances, is proposed and used to design a wheel slip regulating controller. The implemented control structure consists of a conventional controller and a neuro-fuzzy network-based feedback controller. The former is provided both to guarantee global a...

The paper presents a new approach to fuzzy classification in the case of missing data. Rough-fuzzy sets are incorporated into logical type neuro-fuzzy structures and a rough-neuro-fuzzy classifier is derived. Theorems which allow determining the structure of the rough-neuro-fuzzy classifier are given. Several experiments illustrating the performance of the roughneuro-fuzzy classifier working in...