Piotr Dziwiński

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.

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.

A New Approach to Nonlinear Modeling Based on Significant Operating Points Detection

The paper presents a new approach to nonlinear modeling based on significant operating points detection from non-invasive identification of nonlinear dynamic system. The swarm intelligence supported by the genetic algorithm is used in the proposed approach to identify the unknown parameters of the nonlinear dynamic system in different significant operating points. The parameters of the membership functions of the fuzzy rules and the parameters of the linear models are simultaneously identified. The new approach was tested on the nonlinear electrical circuit, which was replaced by the approximate linear model. The obtained results prove efficiency of the new approach based on the significant operating points detection.

Ant Focused Crawling Algorithm

This paper presents a new algorithm for hypertext graph crawling. Using an ant as an agent in a hypertext graph significantly limits amount of irrelevant hypertext documents which must be downloaded in order to download a given number of relevant documents. Moreover, during all time of the crawling, artificial ants do not need a queue to central control crawling process. The proposed algorithm, called the Focused Ant Crawling Algorithm, for hypertext graph crawling, is better than the Shark-Search crawling algorithm and the algorithm with best-first search strategy utilizing a queue for the central control of the crawling process.

New method for generation type-2 fuzzy partition for FDT

One of the most important tasks during application of fuzzy decision tree algorithms is to generate a fuzzy partition. In this paper, we introduce a new method to perform this task. The proposed method is a two stage process. The second stage is based on the classical Fuzzy C-means (FCM) clustering.

New linguistic hedges in construction of interval type-2 FLS

The paper presents a methodology for application of an interval type-2 codebook to computing with words. The crucial problem in this task is to formulate new hedge operators for fuzzy sets. The proposed hybrid system is demonstrated in a numerical example.

A New Method of the Intelligent Modeling of the Nonlinear Dynamic Objects with Fuzzy Detection of the Operating Points

The paper presents a new method of the intelligent modeling of the nonlinear dynamic objects with online detection of significant operating points from non-invasive measurements of the nonlinear dynamic object. The PSO-GA algorithm is used to identify the unknown values of the system matrix describing the nonlinear dynamic object in the detected operating points. The Takagi-Sugeno fuzzy system determines the values of the system matrix in the detected operating points. The new method was tested on the nonlinear electrical circuit with the three operating points. The obtained results prove efficiency of the new method of the intelligent modeling of the nonlinear dynamic objects with fuzzy detection of the operating points.