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...

in this research, pomegranate arils are dehydrated by osmotic dehydration in 40, 50, and 60 % sucrose ‎solutions and at 45, 55 and 65 degrees c‎‏ ‏‎ and weight reduction, solids grain and water loss of the products ‎were measured at 60, 120 and 180 minutes of process. osmotic dehydration processes was modeled by ‎combination of neural network and fuzzy logic techniques (neuro-fuzzy) and respons...

This paper presents an adaptive neuro-fuzzy inference system (ANFIS) for USD/JPY exchange rates forecasting. Previous work often used time series techniques and neural networks (NN). ANFIS can be used to better explain solutions to users than completely black-box models, such as NN. The proposed neurofuzzy rule based system applies some technical and fundamental indexes as input variables. In o...

Navigation of multiple mobile robots using neuro-fuzzy controller has been discussed in this paper. In neuro-fuzzy controller the output from the neural network is fed as an input to fuzzy controller and the final outputs from the fuzzy controller are used for motion control of robots. The inputs to the neural network are obtained from the robot sensors (such as left, front, right obstacle dist...

Performance of a fuzzy system identifier is investigated against a fossil fuel boiler data. A multi-layer neuro-ftrzzy system presents identification of a drum type boiler. This identification techruque provides a rule-based approach to express the boiler dynamics in fuzzy rules that are generated from the experimental boiler data. The interconnections of neuro-fuzzy layers furnish these fuzzy ...

In this paper, the robust neuro-fuzzy networks (RNFNs) are proposed to improve the problems of neuro-fuzzy networks (NFNs) for modeling with outliers. Firstly, the support vector regression (SVR) approach is applied to obtain the initial structure of RNFNs. Because of the SVR approach is equivalent to solving a linear constrained quadratic programming problem under the fixed structure of SVR, t...

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...