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Increasing demands on effluent quality and loads call for an improved control, monitoring, and fault detection of waste-water treatment plants (WWTPs). Improved control and optimization of WWTP lead to increased pollutant removal, a reduced need for chemicals as well as energy savings. An important step toward the optimal functioning of a WWTP is to minimize the influence of sensor faults on the control...
The paper deals with identification of a cloud based evolving system. The antecedent part of a fuzzy rule-based system is defined by clouds and density distribution as proposed by Angelov and Yager [1], [2]. But in the current paper Mahalanobis distance is used rather than the Euclidean one when calculating the density. The idea behind is that the shape of the clouds should be reflected in the density...
In this paper an attempt of designing the direct self- learning/evolving fuzzy controller is presented. The controller is comprised of several local-controllers each valid in a certain part of input-output space. The input-output space is partitioned by on-line clustering and evolving mechanisms taken from eFuMo method. The controller gains are adapted using the fuzzy model reference adaptive control...
In this paper we present the design of a two degree-of-freedom (2 DOF) control algorithm, developed for a class of single-input multiple-output non-linear systems. The feedforward and feedback control loops are developed based on the known Takagi-Sugeno fuzzy model of the system. The fuzzy model is part of the control law and it is obtained by evolving fuzzy modelling. Although only a single system...
In this paper a new approach to data stream evolving fuzzy model identification is given. The structure of the model is given in the form of Takagi-Sugeno and the partitioning of the input-output space is obtained using a fuzzy c-regression clustering method and the approach also involves the evolving properties. The method is given in a recursive form. The proposed approach is shown with two simple...
This paper presents the solving of the petrol sales volume estimation problem given by the Task Force on Competitions, Fuzzy Systems Technical Committee IEEE Computational Intelligence Society. The solution using eTS, SAFIS and eFuMo method is presented. The results are compared to linear ARX model identified using RLS method. Results using static and prediction model are given. The paper also presents...
This paper presents the solving of the petrol sales volume estimation problem given by the Task Force on Competitions, Fuzzy Systems Technical Committee IEEE Computational Intelligence Society. The solution using eTS, SAFIS and eFuMo method is presented. The results are compared to linear ARX model identified using RLS method. Results using static and prediction model are given. The paper also presents...
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