Interval type-2 intuitionistic fuzzy logic system for time series and identification problems - a comparative study

This paper proposes a sliding mode control-based learning of interval type-2 intuitionistic fuzzy logic system for time series and identification problems. Until now, derivative-based algorithms such as gradient descent back propagation, extended Kalman filter, decoupled extended Kalman filter and hybrid method of decoupled extended Kalman filter and gradient descent methods have been utilized for the optimization of the parameters of interval type-2 intuitionistic fuzzy logic systems. The proposed model is based on a Takagi-Sugeno-Kang inference system. The evaluations of the model are conducted using both real world and artificially generated datasets. Analysis of results reveals that the proposed interval type-2 intuitionistic fuzzy logic system trained with sliding mode control learning algorithm (derivative-free) do outperforms some existing models in terms of the test root mean squared error while competing favourable with other models in the literature. Moreover, the proposed model may stand as a good choice for real time applications where running time is paramount compared to the derivative-based models

Ranking Causes of Road Accident Occurrence Using Extended Interval Type-2 Fuzzy TOPSIS

Over the past century there has been a dramatic increase in the number of road accidents in Malaysia. Hence, it is necessary to create a decision making method which can consider various preferences and criteria in order to identify the main causes of the accidents. This paper proposes an Interval Type-2 Fuzzy Technique for Order Preference by Similarity to Ideal Solution (IT2FTOPSIS) method which provides a comprehensive valuation from experts. This method is developed based on the aggregation of experts’ opinions on preferred causes of road accidents. The extended IT2FTOPSIS employs a linguistic scales of positive and negative Interval Type-2 Trapezoidal Fuzzy Number (IT2TrFN) and hybrid averaging approach (from an ambiguity and type-reduction methods) to formulate a collective decision environment. Three authorised personnel from three Malaysian Government agencies were interviewed where they were asked to rank the causes. The analysis shows that the linguistic scales of positive and negative Interval Type-2 Trapezoidal Fuzzy Number (IT2TrFN) and hybrid averaging approach are effective in measuring the uncertainties in the interviewees’ responses. Thus this paper concludes that the extended IT2FTOPSIS is more aligned with the users’ decisions compared to the earlier IT2FTOPSIS. Keywords: Multiple criteria decision-making; interval type-2 fuzzy set; IT2FTOPSIS; road accident

A type 2-hesitation fuzzy logic based multi-criteria group decision making system for intelligent shared environments

Intelligent environments aim to maximize the user comfort and safety while achieving other objectives such as energy minimization. Intelligent shared spaces (such as homes, classrooms, offices, libraries, etc.) need to consider the preferences of users from diverse backgrounds. However, there are high levels of uncertainties faced in intelligent shared spaces. Hence, there is a need to employ intelligent decision making systems which can consider the various users preferences and criteria in order to achieve the convenience of the various users while handling the faced uncertainties. Therefore, we propose a Fuzzy Logic-Multi-Criteria Group Decision Making (FL-MCGDM) system which provides a comprehensive valuation from a group of users/decision makers based on the aggregation of users' opinions and preferences. The proposed FL-MCGDM system employs an interval type-2 fuzzy logic and hesitation index [from Intuitionistic Fuzzy Sets (IFSs)]. We have carried out experiments in the intelligent apartment (iSpace) located in the University of Essex to evaluate various approaches employing group decision making techniques for illumination selection in an intelligent shared environment. It was found that the Footprint of Uncertainty (of interval type-2 fuzzy sets) and hesitation index (of intuitionistic fuzzy sets (IFSs)) are able to provide a measure of the uncertainties present among the various decision makers. The proposed Type 2-Hesitation FL-MCGDM system better agrees with the users' decision compared to existing fuzzy MCDM including the Fuzzy Logic based TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), type-1 FL-MCGDM and interval type-2 in FL-MCGDM. © 2013 Springer-Verlag Berlin Heidelberg

Fuzzy Logic in Decision Support: Methods, Applications and Future Trends

During the last decades, the art and science of fuzzy logic have witnessed significant developments and have found applications in many active areas, such as pattern recognition, classification, control systems, etc. A lot of research has demonstrated the ability of fuzzy logic in dealing with vague and uncertain linguistic information. For the purpose of representing human perception, fuzzy logic has been employed as an effective tool in intelligent decision making. Due to the emergence of various studies on fuzzy logic-based decision-making methods, it is necessary to make a comprehensive overview of published papers in this field and their applications. This paper covers a wide range of both theoretical and practical applications of fuzzy logic in decision making. It has been grouped into five parts: to explain the role of fuzzy logic in decision making, we first present some basic ideas underlying different types of fuzzy logic and the structure of the fuzzy logic system. Then, we make a review of evaluation methods, prediction methods, decision support algorithms, group decision-making methods based on fuzzy logic. Applications of these methods are further reviewed. Finally, some challenges and future trends are given from different perspectives. This paper illustrates that the combination of fuzzy logic and decision making method has an extensive research prospect. It can help researchers to identify the frontiers of fuzzy logic in the field of decision making

Evaluation of organisation employee knowledge synergy

Disertacijoje nagrinėjama organizacijos žinių sinergijos vertinimo, taikant sisteminį požiūrį, problema. Sisteminis žinių sinergijos vertinimas svarbus organizacijoms siekiant koreguoti, keisti arba integruoti naujas žinių dalijimosi bei bendradarbiavimo skatinimo priemones, siekiant geresnių veiklos rezultatų ir taip didinant organizacijos potencialą. Tyrimų objektas – darbuotojų žinių sinergijos vertinimas organizacijoje. Darbo tikslas – sukurti organizacijos darbuotojų žinių sinergijos vertinimo metodų rinkinį, kurio taikymas leistų kiekybiškai įvertinti organizacijos darbuotojų žinių sinergiją ir jos komponentus bei sudarytų prielaidas teikti pagrįstus darbuotojų žinių ir ryšių tarp darbuotojų valdymo tobulinimo siūlymus. Disertaciją sudaro įvadas, keturi skyriai, bendrosios išvados ir dešimt priedų. Pirmame disertacijos skyriuje išanalizuota žinių svarba ir probleminės sritys žinių visuomenėje, apžvelgta žinių sklaida, atskleistos žinių sinergijos formavimosi prielaidos, suformuluotas žinių sinergijos apibrėžimas bei pagrįstas šios sąvokos naudojimas, atlikta išsami mokslinių šaltinių analizė sinergijos, žinių sinergijos sampratos tema. Antrame disertacijos skyriuje išanalizuota žinių sinergijos vertinimo metodinė bazė, išgryninti žinių sinergijos komponentai, o atlikus įvairių šaltinių kritinę analizę pagrįstas žinių sinergijos vertinimo metodų rinkinio kūrimo tikslingumas. Trečiame disertacijos skyriuje aprašytas parengtas žinių sinergijos vertinimo metodų rinkinys, išskirti žinių sinergijos tipai, atsižvelgiant į ryšių susiformavimą organizacijoje. Metodų rinkinio esmė – komponentai vertinami kiekybiškai taikant grafų teoriją, kombinatoriką ir daugiakriterinius vertinimo metodus. Kiekybinio organizacijos žinių sinergijos vertinimo rezultatai sudaro prielaidas išgryninti pažangius bei probleminius veiklos procesus, t. y. atlikti išsamų ir tikslų įvertinimą, ir priimti sprendimus veikloms efektyvinti. Ketvirtame skyriuje pateikta žinių sinergijos vertinimo schema bei aprašyta žinių sinergijos vertinimo metodų rinkinio taikymo metodika. Šiame skyriuje taip pat pateikti žinių sinergijos vertinimo metodų rinkinio eksperimentinio taikymo organizacijose rezultatai, kurie pagrindžia žinių sinergijos vertinimo metodų rinkinio naudą organizacijos veiklos procesų analizei