University of Sistan and BaluchestanIranian Journal of Fuzzy Systems1735-065412520151001Cover vol. 12, no.5, October 20150264210.22111/ijfs.2015.2642ENJournal Article20161002https://ijfs.usb.ac.ir/article_2642_e38b90e7cce4505fe17c4f2f1a961d07.pdfUniversity of Sistan and BaluchestanIranian Journal of Fuzzy Systems1735-065412520151030Functorial semantics of topological theories143211010.22111/ijfs.2015.2110ENSergey A.SolovyovInstitute of Mathematics, Faculty of Mechanical Engineering,
Brno University of Technology, Technicka 2896/2, 616 69 Brno, Czech RepublicJournal Article20120720Following the categorical approach to universal algebra through algebraic theories, proposed by F.~W.~Lawvere in his PhD thesis, this paper aims at introducing a similar setting for general topology. The cornerstone of the new framework is the notion of emph{categorically-algebraic} (emph{catalg}) emph{topological theory}, whose models induce a category of topological structures. We introduce the quasicategory of catalg topological theories and consider its functorial relationships with the quasicategory of the categories of models, in order to provide convenient means for studying topological structures via the properties of their corresponding theories.https://ijfs.usb.ac.ir/article_2110_e9eb0f65766fb9dfb4d98bd6fea53fbc.pdfUniversity of Sistan and BaluchestanIranian Journal of Fuzzy Systems1735-065412520151030CVaR Reduced Fuzzy Variables and Their Second Order Moments4575211110.22111/ijfs.2015.2111ENXue-JieBaiCollege of Management, Hebei University, Baoding 071002, Hebei, China and College of Science, Agricultural University of Hebei, Baoding 071001, Hebei, ChinaYan-KuiLiuCollege of Management, Hebei University, Baoding 071002, Hebei,
ChinaJournal Article20130620Based on credibilistic value-at-risk (CVaR) of regular<br />fuzzy variable, we introduce a new CVaR reduction method for<br />type-2 fuzzy variables. The reduced fuzzy variables are<br />characterized by parametric possibility distributions. We establish<br />some useful analytical expressions for mean values and second<br />order moments of common reduced fuzzy variables. The convex properties of second order moments with respect to parameters are also discussed. Finally, we take second order moment as a new risk measure, and develop a mean-moment model to optimize fuzzy portfolio selection problems. According to the analytical formulas of second order moments, the mean-moment optimization model is equivalent to parametric<br />quadratic convex programming problems, which can be solved by general-purpose optimization software. The solution results reported in the numerical experiments demonstrate the credibility of the proposed optimization method.https://ijfs.usb.ac.ir/article_2111_ae9c29c1bd509c95fc8928b9778558da.pdfUniversity of Sistan and BaluchestanIranian Journal of Fuzzy Systems1735-065412520151030Linear matrix inequality approach for synchronization of chaotic fuzzy cellular neural networks with discrete and unbounded distributed delays based on\ sampled-data control7798211210.22111/ijfs.2015.2112ENP.Balasubramaniam-pourDepartment of Mathematics, Gandhigram Rural Institute - Deemed University, Gandhigram - 624 302, Tamilnadu, IndiaK.RatnaveluInstitute of Mathematical Sciences, Faculty of Science, University
of Malaya - 50603, Kuala Lumpur, MalaysiaM.KalpanaInstitute of Mathematical Sciences, Faculty of Science, University of
Malaya - 50603, Kuala Lumpur, MalaysiaJournal Article20130120In this paper, linear matrix inequality (LMI) approach for synchronization of chaotic fuzzy cellular neural networks (FCNNs) with discrete and unbounded distributed delays based on sampled-data control<br />is investigated. Lyapunov-Krasovskii functional combining with the input delay approach as well as the free-weighting matrix approach are employed to derive several sufficient criteria in terms of LMIs ensuring the delayed FCNNs to be asymptotically synchronous. The restriction such as the time-varying delay required to be differentiable or even its time-derivative assumed to be smaller than one, are removed. Instead, the time-varying delay is only assumed to be bounded. Finally, numerical examples and its simulations are provided to demonstrate the effectiveness of the derived results.https://ijfs.usb.ac.ir/article_2112_42c61297c52cf75dfbd00eb52c889040.pdfUniversity of Sistan and BaluchestanIranian Journal of Fuzzy Systems1735-065412520151030Hierarchical Functional Concepts for Knowledge Transfer among Reinforcement Learning Agents99116211310.22111/ijfs.2015.2113ENA.MousaviControl and Intelligent Processing Center of Excellence, School of
Electrical and Computer Engineering, University of Tehran, Tehran, IranM.Nili AhmadabadiControl and Intelligent Processing Center of Excellence,
School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
and School of Cognitive Science, Institute for Research in Fundamental Sciences
(IPM), Tehran, IranH.VosoughpourControl and Intelligent Processing Center of Excellence, School
of Electrical and Computer Engineering, University of Tehran, Tehran, IranB. N.AraabiControl and Intelligent Processing Center of Excellence, School of
Electrical and Computer Engineering, University of Tehran, Tehran, Iran and School
of Cognitive Science, Institute for Research in Fundamental Sciences (IPM), Tehran,
IranN.ZaareControl and Intelligent Processing Center of Excellence, School of
Electrical and Computer Engineering, University of Tehran, Tehran, IranJournal Article20131220This article introduces the notions of functional space and concept as a way of knowledge representation and abstraction for Reinforcement Learning agents. These definitions are used as a tool of knowledge transfer among agents. The agents are assumed to be heterogeneous; they have different state spaces but share a same dynamic, reward and action space. In other words, the agents are assumed to have different representations of an environment while having similar actions. The learning framework is $Q$-learning. Each dimension of the functional space is the normalized expected value of an action. An unsupervised<br />clustering approach is used to form the functional concepts as some fuzzy areas in the functional space. The functional concepts are abstracted further in a hierarchy using the clustering approach. The hierarchical concepts are employed for knowledge transfer among agents. Properties of the proposed approach are tested in a set of case studies. The results show that the approach is very effective in transfer learning among heterogeneous agents especially in the beginning episodes of the learning.https://ijfs.usb.ac.ir/article_2113_282ba69af4a0a581fecc56675062be1d.pdfUniversity of Sistan and BaluchestanIranian Journal of Fuzzy Systems1735-065412520151030Non-Newtonian Fuzzy numbers and \related applications117137211410.22111/ijfs.2015.2114ENUgurKadakDepartment of Mathematics, Bozok University, Yozgat, TurkeyJournal Article20141020Although there are many excellent ways presenting the principle of the classical calculus, the novel presentations probably leads most naturally to the development of the non-Newtonian calculus. The important point to note is that the non-Newtonian calculus is a self-contained system independent of any other system of calculus. Since this self-contained work is intended for a wide audience, including engineers, scientists and mathematicians. The main purpose of the present paper is to construct of fuzzy numbers with respect to the non-Newtonian calculus and is to give the necessary and sufficient conditions according to the generalization of the notion of fuzzy numbers by using the generating functions. Also we introduce the concept of non-Newtonian fuzzy distance and give some properties regarding convergence of sequences and series of fuzzy numbers with some illustrative examples.https://ijfs.usb.ac.ir/article_2114_aafefe9d564507d2db1e4749261bbf48.pdfUniversity of Sistan and BaluchestanIranian Journal of Fuzzy Systems1735-065412520151030Order intervals in the metric space\ of fuzzy numbers139147211510.22111/ijfs.2015.2115ENS.AytarFaculty of Arts and Sciences, Department of Mathematics, Suleyman
Demirel University, Isparta, TurkeyJournal Article20130320In this paper, we introduce a function in order to measure the distance<br />between two order intervals of fuzzy numbers, and show that this function is<br />a metric. We investigate some properties of this metric, and finally present<br />an application. We think that this study could provide a more general<br />framework for researchers studying on interval analysis, fuzzy analysis and<br />fuzzy decision making.https://ijfs.usb.ac.ir/article_2115_28bfba3b526dd2943ba3639b4db69957.pdfUniversity of Sistan and BaluchestanIranian Journal of Fuzzy Systems1735-065412520151030A note on soft topological spaces149155211610.22111/ijfs.2015.2116ENFu-GuiShiSchool of Mathematics and Statistics, Beijing Institute of Technology,
5 South Zhongguancun Street, Haidian District, 100081 Beijing, P.R. ChinaBinPangSchool of Mathematics and Statistics, Beijing Institute of Technology, 5
South Zhongguancun Street, Haidian District, 100081 Beijing, P. R. ChinaJournal Article20130720This paper demonstrates the redundancies concerning the increasing popular ``soft set" approaches to general topologies. It is shown that there is a complement preserving isomorphism (preserving arbitrary $widetilde{bigcup}$ and arbitrary $widetilde{bigcap}$) between the lattice ($mathcal{ST}_E(X,E),widetilde{subset}$) of all soft sets on $X$ with the whole parameter set $E$ as domains and the powerset lattice ($mathcal{P}(Xtimes E),subseteq$) of all subsets of $Xtimes E$. It therefore follows that soft topologies are redundant and unnecessarily complicated in theoretical sense.https://ijfs.usb.ac.ir/article_2116_72c56938f64cab0a8312fcbb598fa53d.pdfUniversity of Sistan and BaluchestanIranian Journal of Fuzzy Systems1735-065412520151029Persian-translation vol. 12, no.5, October 2015159165264310.22111/ijfs.2015.2643ENJournal Article20161002https://ijfs.usb.ac.ir/article_2643_0921b835f0d81e0b0abec67977f149f7.pdf