Łukasz Bartczuk

A New Algorithm for Identification of Significant Operating Points Using Swarm Intelligence

The paper presents a novel algorithm for identification of significant operating points from non-invasive identification of nonlinear dynamic objects. In the proposed algorithm to identify the unknown parameters of nonlinear dynamic objects in different significant operating points, swarm intelligence supported by a genetic algorithm is used for optimization in continuous domain. Moreover, we propose a new weighted approximation error measure which eliminates the problem of the measurements obtained from non-significant areas. This measure significantly accelerates the process of the parameters identification in comparison with the same algorithm without weights. Performed simulations prove efficiency of the novel algorithm.

New Method for Nonlinear Fuzzy Correction Modelling of Dynamic Objects

In the paper a method to use the equivalent linearization technique of the nonlinear state equation with the coefficients generated by the fuzzy rules for current operating point is proposed. On the basis of the evolutionary strategy and properly defined identification procedure, the fuzzy rules are automatically designed to maximize the accuracy of the resulting linear model.

Medical Diagnosis with Type-2 Fuzzy Decision Trees

In this paper, we propose type-2 fuzzy decision trees in application to medical diagnosis. This means that attribute values employed in the tree structures may be characterized by type-2 fuzzy sets. Three medical benchmark data sets, available on the Internet, have been used to illustrate results of diagnosis obtained by this method.

Hybrid State Variables - Fuzzy Logic Modelling of Nonlinear Objects

In this paper a new hybrid method for modelling of nonlinear dynamic systems is proposed. It uses fuzzy logic system together with state variables technique to obtain the local linear approximation performed continuously for successive operating points. This approach provides good accuracy and allows the use of very convenient and well-known method from linear control theory to analyse the obtained model.

A new method for dealing with unbalanced linguistic term set

In this paper, a new method for dealing with an unbalanced linguistic term set is introduced. The proposed method is a modification of the 2-tuple linguistic model, in which we use a set of extended linguistic terms. The extended linguistic term is a pair that consists a linguistic label and a value of correction factor which describes the term shift relative to its position in an equidistant term set. This modification allows us to obtain the method that is computationally less expensive and give simpler semantics than method based on linguistic hierarchies.

Fully controllable ant colony system for text data clustering

The paper presents a new Fully Controllable Ant Colony Algorithm (FCACA) for the clustering of the text documents in vector space. The proposed new FCACA is a modified version of the Lumer and Faieta Ant Colony Algorithm (LF-ACA). The algorithm introduced new version of the basic heuristic decision function significantly improves the convergence and greater control over the process of the grouping data. The proposed solution was shown in a text example proving efficiency of the proposed solution in comparison with other grouping algorithms.

Learning methods for type-2 FLS based on FCM

This paper presents a new two-phase learning method for interval type-2 fuzzy logic systems. The method combines traditional learning approaches to type-1 fuzzy systems with fitting of interval memberships using FCM memberships. Two improving modifications of the proposed method are supplied additionally.

Type-2 Fuzzy Decision Trees

This paper presents type-2 fuzzy decision trees (T2FDTs) that employ type-2 fuzzy sets as values of attributes. A modified fuzzy double clustering algorithm is proposed as a method for generating type-2 fuzzy sets. This method allows to create T2FDTs that are easy to interpret and understand. To illustrate performace of the proposed T2FDTs and in order to compare them with results obtained for type-1 fuzzy decision trees (T1FDTs), two benchmark data sets, available on the internet, have been used.

Gene Expression Programming in Correction Modelling of Nonlinear Dynamic Objects

In this paper we shown the applying of gene expression programming algorithm to correction modelling of non-linear dynamic objects. The correction modelling is the non-linear modelling method based on equivalent linearization technique that allows to incorporate in modelling process the known linear model of the same or similar object or phenomenon. The usefulness of the proposed method will be shown on a practical example of the continuous stirred tank reactor modelling.

A new version of the Fuzzy-ID3 algorithm

In this paper, a new version of the Fuzzy-ID3 algorithm is presented. The new algorithm allows to construct decision trees with smaller number of nodes. This is because of the modification that many different attributes and their values can be assigned to single leaves of the tree. The performance of the algorithm was checked on three typical benchmarks data available on the Internet.