Peter Vassilev

Parallel arrays of microtubles formed in electric and magnetic fields

The influence of electric and magnetic fields on microtubule assembly in vitro was studied. Both types of field caused alignment of microtubules in parallel arrays, as demonstrated by electron micrographs. These Iindings suggest a possible role of microtubules in the biological effects of exogenous as well as endogenous

InterCriteria Analysis Approach to Parameter Identification of a Fermentation Process Model

In this investigation recently developed InterCriteria Analysis (ICA) is applied aiming at examination of the influence of a genetic algorithm (GA) parameter in the procedure of a parameter identification of a fermentation process model. Proven as the most sensitive GA parameter, generation gap is in the focus of this investigation. The apparatuses of index matrices and intuitionistic fuzzy sets, laid in the ICA core, are implemented to establish the relations between investigated here generation gap, from one side, and model parameters of fed-batch fermentation process of Saccharomyces cerevisiae, from the other side. The obtained results after ICA application are analysed towards convergence time and model accuracy and some conclusions about observed interactions are derived.

InterCriteria Analysis of Generation Gap Influence on Genetic Algorithms Performance

In this investigation InterCriteria Analysis (ICA) is applied to examine the influences of one of the genetic algorithms parameters—the generation gap (ggap). The investigation is carried out during the model parameter identification of E. coli MC4110 cultivation process. The apparatuses of index matrices and intuitionistic fuzzy sets, which are the core of ICA, are used to establish the relations between ggap and GAs outcomes (computational time and decision accuracy), on one hand, and cultivation process model parameters on the other hand. The obtained results after ICA application are analyzed in terms of convergence time and model accuracy and some conclusions about derived interactions are reported.

InterCriteria Analysis of a model parameters identification using genetic algorithm

In this paper we apply an approach based on the apparatus of the Index Matrices and the Intuitionistic Fuzzy Sets - namely InterCriteria Analysis. The main idea is to use the InterCriteria Analysis to establish the existing relations and dependencies of defined parameters in non-linear model of an E. coli fed-batch cultivation process. Moreover, based on results of series of identification procedures we observe the mutual relations between model parameters and considered optimization techniques outcomes, such as execution time and objective function value. Based on InterCriteria Analysis we examine the obtained identification results and discuss the conclusions about existing relations and dependencies between defined, in terms of InterCriteria Analysis, criteria.

Comparison of different algorithms for InterCriteria relations calculation

In this investigation different algorithms for InterCriteria relations calculation are proposed. The algorithms are investigated by exploring the influence of genetic parameters on algorithm performance during the model parameter identification of E. coli fermentation process. Four different algorithms performing InterCriteria Analysis (ICrA), namely μ-biased, balanced, ν-biased and unbiased, are applied. Proposed ICrA algorithms are compared based on real experimental data set of an E. coli MC4110 fed-batch fermentation process. The obtained results show that for considered here case study the most reliable algorithm is the μ-biased one.

InterCriteria Analysis of Genetic Algorithms Performance

In this paper we apply InterCriteria Analysis (ICrA) approach based on the apparatus of Index Matrices and Intuitionistic Fuzzy Sets. The main idea is to use ICrA to establish the existing relations and dependencies of defined parameters in a non-linear model of an E. coli fed-batch cultivation process. We perform a series of model identification procedures applying Genetic Algorithms (GAs). We proposed a schema of ICrA of ICrA results to examine the obtained model identification results. The discussion about existing relations and dependencies is performed according to criteria defined in terms of ICrA. We consider as ICrA criteria model parameters and GAs outcomes on the one hand, and 14 differently tuned GAs on the other. Based on the results, we observe the mutual relations between model parameters and GAs outcomes, such as computation time and objective function value. Moreover, some conclusions about the preferred tuned GAs for the considered model parameter identification in terms of achieved accuracy for given computation time are presented.

Using Phi Coefficient to Interpret Results Obtained by InterCriteria Analysis

The authors propose an algorithm for assessment of the estimates of “correspondence” and “opposition” obtained by InterCriteria Analysis (ICA) in the form of intuitionistic fuzzy vector pairs. For this aim the modified Pearson coefficient of Karl Pearson, called \(\varphi \) coefficient (“mean square contingency coefficient”). The algorithm is applied on real data from neurosurgery. The statistical significance of the relations between the considered criteria is verified by data found in literature. The authors believe this approach for data exploration may prove useful in many areas.

InterCriteria Analysis of Parameters Relations in Fermentation Processes Models

In this paper the application of InterCriteria Analysis (ICA) is presented. The approach is based on the apparatuses of index matrices and intuitionistic fuzzy sets. ICA is applied to establish the relations and dependencies of defined parameters in non-linear models of Escherichia coli MC4110 and Saccharomyces cerevisiae fermentation processes. Parameter identification of both fed-batch process models has been done using three kinds of genetic algorithms (GA) – standard single population GA (SGA) and two SGA modifications. The obtained results are discussed in the lights of ICA and some conclusions about existing relations and dependencies between model parameters are derived.

On the Intuitionistic Fuzzy Sets of n -th Type

A survey and new results, related to the intuitionistic fuzzy sets of n-th type are given. Some open problems are formulated.

InterCriteria Analysis by Pairs and Triples of Genetic Algorithms Application for Models Identification

In this investigation the InterCriteria Analysis (ICrA) approach is applied. The apparatuses of index matrices and intuitionistic fuzzy sets are at the core of ICrA. They are used to examine the influences of two main genetic algorithms (GA) parameters—the rates of crossover (xovr) and mutation (mutr). A series of parameter identification procedures for S. cerevisiae and E. coli fermentation process models is fulfilled. Twenty GA with different xovr and mutr values are applied. Relations between ICrA criteria—GA parameters and outcomes, on the one hand, and fermentation process model parameters, on the other hand, are investigated. The ICrA approach is applied by pairs, as well as by triples. The obtained results are thoroughly analysed towards computation time and model accuracy and some conclusions about the derived criteria interactions are reported.