This paper introduces a new classifier, that is based on fuzzy-integration schemes controlled by a genetic optimisation procedure. Two different types of integration are proposed here, and are validated by experiments on real data sets of biological cells. The performance of our classifier is tested against a feed-forward neural network and a Support Vector Machine. Results show the good perfor...

This study presents a new approach for evaluation of oil-film pressure in hydrodynamic bearing. Oil-film pressure is one of the critical parameter which determines the performance of hydrodynamic bearing. In this research parameters of rotational speed, bearing load and oil-feed pressure are considered as model independent variables. Modelling and simulations are done in Matlab. The research sh...

In this contribution we present investigations about the use of computational intelligence for toxicity prediction of pesticides. Therefore different molecular descriptors are computed and the correlation behavior of the different descriptors in the descriptor space is studied. In a first step 164 pesticides are considered and 175 descriptors are taken into account. From this set of data prelim...

This paper presents a time control liquids mixing system in the tanks as an application of fuzzy time control discrete model. The system is designed for a wide range of industrial applications. The simulation design of control system has three inputs: volume, viscosity, and selection of product, along with the three external control adjustments for the system calibration or to take over the con...

A novel technique of designing application specific defuzzification strategies with neural learning is presented. The proposed neural architecture considered as a universal defuzzification approximator is validated by showing the convergence when approximating several existing defuzzification strategies. The method is successfully tested with fuzzy controlled reverse driving of a model truck. T...

Several methods of fuzzy rule based classiication are applied to medical data. Features from patient data, collected in a clinic, are pre-processed before being fed into fuzzy neural networks, where fuzzy rule based classiication systems are generated. Additionally, results obtained from feed-forward architectures such as standard-backpropagation networks, radial basis function nets (RBF) and D...

A fuzzy set theoretic methodology is described that serves as a classification preprocessing strategy for supervised feed-forward neural networks. This methodology, fuzzy interquartile encoding, determines the respective degrees to which a feature belongs to a collection of fuzzy sets that overlap at the respective quartile boundaries of the feature. These membership values are subsequently use...

The Knowledge Acquisition (KA) process consists on extracting and representing knowledge of a domain expert. In this work, one of the goals is to minimize the intrinsic difficulties of the KA process. We have obtained all possible rules from the domain expert in a short time and also a set of examples. Other goal, we are proposed a Hybrid Expert System (HES) to minimize the problems of the KA t...

In medical image analysis, classification plays an imperative role and the accessible corporeal classification plunge to congregate the requisites. Automatic image classification is emergent thriving research area and contribution diagnostician for malady to ascertain the echelon of malignant cells of brain tumor using artifical intellect Since manual classification is time devastating and best...