Fuzzy Information and Engineering

Bisht, Kamlesh; Kumar, Sanjay

2018 Fuzzy Information and Engineering

doi: 10.1080/16168658.2019.1631557

Intuitionistic fuzzy sets (IFSs) have been proved to be more ideal than fuzzy sets to handle non-probabilistic uncertainty and non-determinism in the system. The present study proposes an IFS-based computational method to address the issue of non-determinism in financial time series forecasting. The proposed IFS-based forecasting method uses a simple computational algorithm to forecast without using complex computations using intuitionistic fuzzy logical relations. In order to see suitability of the proposed method in financial forecasting, it is implemented on three experimental data of the SBI share price, TAIEX and Dow Jones Industrial Average (DJIA). Root mean square error and statistical test are used in the study to confirm the out performance of the proposed IFS-based computational method of forecasting. Experimental results show that the proposed method outperforms various existing methods for forecasting SBI, TAIEX and DJIA.

Nasseri, S. H.; Bavandi, S.

2018 Fuzzy Information and Engineering

doi: 10.1080/16168658.2019.1612605

In this paper, we consider a Fuzzy Stochastic Linear Fractional Programming problem (FSLFP). In this problem, the coefficients and scalars in the objective function are the triangular fuzzy number and technological coefficients and the quantities on the right side of the constraints are fuzzy random variables with the specific distribution. Here we change an FSLFP problem to an equivalent deterministic Multi-objective Linear Fractional Programming (MOLFP) problem. Then by using Fuzzy Mathematical programming approach transformed MOLFP problem is reduced single objective Linear programming (LP) problem. A numerical example is presented to demonstrate the effectiveness of the proposed method.

Aggarwal, Abha; Mehra, Aparna; Chandra, Suresh; Khan, Imran

2018 Fuzzy Information and Engineering

doi: 10.1080/16168658.2019.1644032

Dubey et al. [40] have shown that solving an Atanassov's I-fuzzy Linear Programming Problem represented by Atanassov's I-fuzzy sets with linear membership and non-membership functions is equivalent to solving an appropriate fuzzy optimisation problem with piecewise linear S-shaped membership functions. The equivalence is established using Hurwicz optimism–pessimism criterion [38] and indeterminacy resolution in Atanassov's I-fuzzy sets. Moreover, in case of convex break points in the piecewise linear membership function, the crisp counterpart of the equivalent optimisation problem involves binary variables. Here, in this paper we first convert the resulting fuzzy optimisation problem having convex break points into an equivalent fuzzy optimisation problem having concave break points on the lines of Inuiguchi et al. [34], before formulating its crisp equivalent. The advantage of this strategy is that the resulting crisp equivalent problem has no binary variables. Further, we also make use of the indeterminacy factor resolution principle to establish a duality relation which can be interpreted as a Atanassov's I-fuzzy variant of the (crisp) weak duality theorem.

Bhamare, Devyani; Suryawanshi, Poonam

2018 Fuzzy Information and Engineering

doi: 10.1080/16168658.2019.1611030

The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have deserved increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples and performance evaluation. The general problem of recognising complex pattern with arbitrary patterns with arbitrary orientation, location and scale remains unsolved. New and emerging application, such as data mining, web searching, retrieval of multimedia data, face recognition and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarise and review some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field. In the literature, Pattern recognition frameworks have been drawn closer by different machine learning strategies. This part reviews 33 related examinations in the period between 2014 and 2017.

Prasannan, A. R.

2018 Fuzzy Information and Engineering

doi: 10.1080/16168658.2019.1655301

The notion of local starplus-compactness on an L-fuzzy topological space, which is an extension of the notion of local compactness in general topology, is introduced. It turns out that local starplus-compactness is finitely productive, closed hereditary and invariant under fuzzy continuous open surjections. Moreover, local starplus-compactness is a good extension of the notion of local compactness in general topology. Examples are included to show that local starplus-compactness is neither hereditary nor expansive, nor contractive.