Selecting a set of features which is optimal for a given classiication task is one of the central problems in machine learning. We address the problem using the exible and robust lter technique EUBAFES. EUBAFES is based on a feature weighting approach which computes binary feature weights and therefore a solution in the feature selection sense and also gives detailed information about feature r...

For estimation of monthly precipitation, considering the intricacy and lack of accurate knowledge about the physical relationships, black box models usually are used because they produce more accurate values. In this article, a hybrid black box model, namely ANN-RBF, is proposed to estimate spatiotemporal value of monthly precipitation. In the first step a Multi Layer Perceptron (MLP) network i...

A novel approach for the classification of both balanced and imbalanced dataset is developed in this paper by integrating the best attributes of radial basis function networks and differential evolution. In addition, a special attention is given to handle the problem of inconsistency and removal of irrelevant features. Removing data inconsistency and inputting optimal and relevant set of featur...

In this chapter we present a 3-D visual object recognition system for an autonomous mobile robot. This object recognition system performs the following three tasks: Object localisation in the camera images, feature extraction, and classification of the extracted feature vectors with hierarchical radial basis function (RBF) networks.

Joachim Feist , Ste en Gutjahr Neurotec Hochtechnologie GmbH Germany Email: [email protected] University of Karlsruhe Institute of Logic, Complexity and Deduction Systems Germany Email: [email protected] Abstract. In this paper we compare variants of elliptical basis function networks for classication tasks. The networks are introduced as density estimators and then modi ed towards RBF networks. ...

This paper introduces a novel method for human face recognition that employs a set of different kind of features from the face images with Radial Basis Function (RBF) neural network called the Hybrid N-Feature Neural Network (HNFNN) human face recognition system. The face image is projected in each appropriately selected transform methods in parallel. The output of the RBF classifiers are fused...

Oil spills represent one of the most destructing environmental disasters. Predicting the possibility of finding oil slicks in a certain area after an oil spill can be crucial in order to reduce the environmental risks. The system presented here forecasts the presence or not of oil slicks in a certain area of the open sea after an oil spill using Case-Based Reasoning methodology. CBR is a comput...

A novel method based on the local nonlinear mapping is presented in this research. The method is called Locally Linear Discriminate Embedding LLDE . LLDE preserves a local linear structure of a high-dimensional space and obtains a compact data representation as accurately as possible in embedding space low dimensional before recognition. For computational simplicity and fast processing, Radial ...

This paper presents a novel control method based on radial basis function networks (RBFNs) for chaotic dynamical systems. The proposed method first identifies the nonlinear part of the chaotic system off-line and then constructs a model-following controller using only the estimated system parameters. Simulation results show the effectiveness of the proposed control scheme. Keywords—Chaos, nonli...

Forecasting road condition after maintenance can help in better road maintenance planning. As road administrations annually collect and store road-related data, data-driven methods can be used in determining forecasting models that result in improved accuracy. In this paper, we compare the prediction models identified by experts and currently used in road administration with simple data-driven ...