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...

This work presents a Neuro-Fuzzy network implemented in 0.8μm CMOS analog technology. The architecture and the circuit configurations adopted are discussed. The circuit has been designed taking into account mismatch effects and Spectre simulations have been reported. An integrated circuit has been realized and is now under test. Key-Words: Neuro-Fuzzy, Analog, Mixed Signal, VLSI, Embedded Appli...

This paper presents a new general purpose neuro-fuzzy controller to realize adaptive-network-based fuzzy inference system (ANFIS) architecture. ANFIS which tunes the fuzzy inference system with a back propagation algorithm based on collection of input-output data makes fuzzy system to learn. To implementing this idea we propose several improved CMOS analog circuits, including Gaussian-like memb...

Jǐŕı Anděl, Sergej Čelikovský, Marie Demlová, Jan Flusser, Petr Hájek, Vladimı́r Havlena, Didier Henrion, Yiguang Hong, Zdeněk Hurák, Martin Janžura, Jan Ježek, George Klir, Ivan Kramosil, Tomáš Kroupa, Petr Lachout, Friedrich Liese, Jean-Jacques Loiseau, Frantǐsek Matúš, Radko Mesiar, Karol Mikula, Jǐŕı Outrata, Jan Seidler, Karel Sladký Jan Štecha, Olga Štěpánková, Frantǐsek Turnovec, Igor Vaj...

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 an indirect adaptive system based on neuro-fuzzy approximators for the speed control of induction motors. the uncertainty including parametric variations, the external load disturbance and unmodeled dynamics is estimated and compensated by designing neuro-fuzzy systems. the contribution of this paper is presenting a stability analysis for neuro-fuzzy speed control of inducti...

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...

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...

The interest in neuro{fuzzy systems has grown tremendously over the last few years. First approaches concentrated mainly on neuro{fuzzy controllers, whereas newer approaches can also be found in the domain of data analysis. After successful applications in Japan neuro{fuzzy concepts also nd their way into the European industries, though mainly simple models, like FAMs, still prevail. This paper...

Load sensor is developed using fuzzy logic as well as neuro-fuzzy method. It is two inputs and one output sensor. Both fuzzy logic and neuro-fuzzy algorithms are simulated using MATLAB fuzzy logic toolbox. This paper outlines the basic difference between the results of fuzzy logic and neuro-fuzzy algorithms and provides the better algorithm for load sensor. Index Terms —fuzzy logic, load sensor...