2018-12-16T14:49:34Z
http://ijfs.usb.ac.ir/?_action=export&rf=summon&issue=340
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2015
12
5
Cover vol. 12, no.5, October 2015
2015
10
01
0
http://ijfs.usb.ac.ir/article_2642_e38b90e7cce4505fe17c4f2f1a961d07.pdf
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2015
12
5
Functorial semantics of topological theories
Sergey A.
Solovyov
Following 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.
Algebra
Algebraic theory
Comma category
Categorically-algebraic topology
Poslat topology
Powerset theory
Topological system
Topological theory
Variety
2015
10
30
1
43
http://ijfs.usb.ac.ir/article_2110_e9eb0f65766fb9dfb4d98bd6fea53fbc.pdf
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2015
12
5
CVaR Reduced Fuzzy Variables and Their Second Order Moments
Xue-Jie
Bai
Yan-Kui
Liu
Based on credibilistic value-at-risk (CVaR) of regularfuzzy variable, we introduce a new CVaR reduction method fortype-2 fuzzy variables. The reduced fuzzy variables arecharacterized by parametric possibility distributions. We establishsome useful analytical expressions for mean values and secondorder 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 parametricquadratic 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.
Credibilistic value-at-risk
Reduced fuzzy variable
Parametric possibility distribution
Second order moment
2015
10
30
45
75
http://ijfs.usb.ac.ir/article_2111_ae9c29c1bd509c95fc8928b9778558da.pdf
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2015
12
5
Linear matrix inequality approach for synchronization of chaotic fuzzy cellular neural networks with discrete and unbounded distributed delays based on sampled-data control
P.
Balasubramaniam-pour
K.
Ratnavelu
M.
Kalpana
In 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 controlis 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.
Chaos
Fuzzy cellular neural networks
Linear matrix inequality
Sampled-data control
Synchronization
2015
10
30
77
98
http://ijfs.usb.ac.ir/article_2112_42c61297c52cf75dfbd00eb52c889040.pdf
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2015
12
5
Hierarchical Functional Concepts for Knowledge Transfer among Reinforcement Learning Agents
A.
Mousavi
M.
Nili Ahmadabadi
H.
Vosoughpour
B. N.
Araabi
N.
Zaare
This 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 unsupervisedclustering 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.
Reinforcement Learning
Transfer Learning
Heterogeneous Agents
Hierarchical Concepts
2015
10
30
99
116
http://ijfs.usb.ac.ir/article_2113_282ba69af4a0a581fecc56675062be1d.pdf
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2015
12
5
Non-Newtonian Fuzzy numbers and related applications
Ugur
Kadak
Although 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.
Non-Newtonian calculus
Fuzzy level sets
Trapezoidal fuzzy numbers
Convergence of fuzzy sequences and series
2015
10
30
117
137
http://ijfs.usb.ac.ir/article_2114_aafefe9d564507d2db1e4749261bbf48.pdf
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2015
12
5
Order intervals in the metric space of fuzzy numbers
S.
Aytar
In this paper, we introduce a function in order to measure the distancebetween two order intervals of fuzzy numbers, and show that this function isa metric. We investigate some properties of this metric, and finally presentan application. We think that this study could provide a more generalframework for researchers studying on interval analysis, fuzzy analysis andfuzzy decision making.
Fuzzy number
Order interval of fuzzy numbers
Decision making
2015
10
30
139
147
http://ijfs.usb.ac.ir/article_2115_28bfba3b526dd2943ba3639b4db69957.pdf
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2015
12
5
A note on soft topological spaces
Fu-Gui
Shi
Bin
Pang
This 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.
Soft set
Soft topology
2015
10
30
149
155
http://ijfs.usb.ac.ir/article_2116_72c56938f64cab0a8312fcbb598fa53d.pdf
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2015
12
5
Persian-translation vol. 12, no.5, October 2015
2015
10
29
159
165
http://ijfs.usb.ac.ir/article_2643_0921b835f0d81e0b0abec67977f149f7.pdf