Rule base identification in fuzzy networks by Boolean matrix equations

This paper introduces a novel extension of the Technique for Ordering of Preference by Similarity to Ideal Solution (TOPSIS) method. The method is based on aggregation of rules with different linguistic of the output of fuzzy networks to solve multi criteria decision-making problems whereby both benefit and cost criteria are presented as subsystems. Thus the decision maker evaluates the performance of each alternative for decision process and further observes the performance for both benefit and cost criteria. The aggregation sub-stage in a fuzzy system maps the fuzzy membership functions for all rules to an aggregated fuzzy membership function representing the overall output for the rules. This approach improves significantly the transparency of the TOPSIS methods, while ensuring high effectiveness in comparison to established approaches. To ensure practicality and effectiveness, the proposed method is further tested on portfolio selection problems. The ranking produced by the method is comparatively validated using Spearman rho rank correlation. The results show that the proposed method outperforms the existing TOPSIS approaches in term of ranking performance.

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“…the distance between the first equity E1 according to DM1 and the FPIS is calculated using Eq. (6) for and , as follows:…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Therefore FNs consider the interaction between subsystems. This ability brings considerable benefits to modelling complex processes, and although FNs have been introduced recently, a cohort of researchers are dedicated to the theoretical development and applications of FNs [3], [4], [6]- [8].…”

Section: Introductionmentioning

confidence: 99%

Selection of alternatives using fuzzy networks with rule base aggregation

Yaakob

Gegov

Rahman

2018

Fuzzy Sets and Systems

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“…In this case, the white-box presentation improves significantly the transparency of the model due to the explicit and adequate reflection of the internal structure of the modelled process. This ability brings considerable benefits to modelling complex processes, and although FNs have been introduced recently, a significant volume of work have been done and dedicated to the theoretical development and applications of FNs [36]- [37], [39]- [40].…”

Section: Fn-topsis: Fuzzy Network For Ranking Traded Equitiesmentioning

confidence: 99%

FN-TOPSIS: Fuzzy Networks for Ranking Traded Equities

Yaakob

Serguieva

Gegov

2017

IEEE Trans. Fuzzy Syst.

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Fuzzy systems consisting of networked rule bases, called fuzzy networks, capture various types of imprecision inherent in financial data and in the decision-making processes on them. This paper introduces a novel extension of the Technique for Ordering of Preference by Similarity to Ideal Solution (TOPSIS) method and uses fuzzy networks to solve multi criteria decision-making problems where both benefit and cost criteria are presented as subsystems. Thus, the decision maker evaluates the performance of each alternative for portfolio optimisation and further observes the performance for both benefit and cost criteria. This approach improves significantly the transparency of the TOPSIS methods, while ensuring high effectiveness in comparison to established approaches. The proposed method is further tested to solve the problem of selection/ranking of traded equity covering developed and emergent financial markets. The ranking produced by the method is validated using Spearman rho rank correlation. Based on the case study, the proposed method outperforms the existing TOPSIS approaches in terms of ranking performance.

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