A new approach is proposed for the data-based identification of transparent fuzzy rule-based classifiers. It is observed that fuzzy rule-based classifiers work in a similar manner as kernel function-based support vector machines (SVMs) since both model the input space by nonlinearly maps into a feature space where the decision can be easily made. Accordingly, trained SVM can be used for the con...

A method for the construction of open approximate models from vector time series Preface PhD research is a largely open proces in which a stimulating environment plays a crucial role. I am indebted to several people and institutions that shaped such an environment for me during the years since April 1990. First and for all, I would like to thank my supervisor, Christiaan Heij. He set me on the ...

We formulate the problem of least squares temporal difference learning (LSTD) in the framework of least squares SVM (LS-SVM). To cope with the large amount (and possible sequential nature) of training data arising in reinforcement learning we employ a subspace based variant of LS-SVM that sequentially processes the data and is hence especially suited for online learning. This approach is adapte...

This paper investigates the long-term prediction of MPEG video traffic. Predicting such traffic over a long horizon is important for today’s fast networks and internet multimedia services. In comparison with short-term prediction, long-term prediction of video traffic is yet to be explored especially for MPEG-4 coded videos despite its effectiveness in a number of important network-edge applica...

In this paper we present the recently proposed approach InterCriteria Analysis (ICA) for the Mesta River pollution modelling in Bulgaria. The approach is based on the apparatus of the index matrices and the intuitionistic fuzzy sets. We have applied the ICA to establish the basic pollution relations (the model structure) based on different criteria involved in the Mesta River. The results have ...

This paper tackles the problem of incremental and decremental learning of an evolving and customizable fuzzy inference system for classi cation. We explain the interest of integrating a forgetting capacity in such an evolving system to improve its performances in changing environment. In this paper, we describe two decremental learning strategies, to introduce a forgetting capacity in evolving ...

The prediction of an adequate amount of claim reserves is of the greatest importance to face the responsibilities assumed by an insurance company. Although many different deterministic and stochastic methods based on statistical analyses are used for claims analysis, presence of many internal and external factors that increase the uncertainty in insurance environment may lead to considerable lo...

Predicting the probabilities that a mobile user will be active in other cells at future moments poses a significant technical challenge to network resource management in multimedia wireless communications. The probability information can be used to assist base stations to maintain a balance between guaranteeing quality of service (QoS) to mobile users and achieving maximum resource utilization....

Fuzzy linear regression models are used to obtain an appropriate linear relation between a dependent variable and several independent variables in a fuzzy environment. Several methods for evaluating fuzzy coefficients in linear regression models have been proposed. The first attempts at estimating the parameters of a fuzzy regression model used mathematical programming methods. In this the...

The main problem when dealing with fuzzy data variables is that it cannot be formed by a model represents the through method of Fuzzy Least Squares Estimator (FLSE) which gives false estimates invalidity in case existence multicollinearity. To overcome this problem, Bridge Regression (FBRE) Method was relied upon to estimate linear regression triangular numbers. Moreover, detection multicolline...