A Novel Dual Factor Fuzzy Time Series Forecasting based on new Fuzzy sets and Interval Definition by Evolution Strategies

This paper proposes a new dual factor time-invariant fuzzy time series method that is capable of forecasting stock marketPrice Index. The proposed approach uses a new fuzzy logic relationship definition. According to the utilized membershipdegrees used to define the fuzzy relationships, each datum may belong to two distinct intervals rather than only one interval.This assumption, which has not been considered in the other studies, contributes to better forecasting results. In addition, anappropriate meta-heuristic algorithm for continuous solution schemes, namely evolution strategies (ES), is utilized to identifythe appropriate interval lengths. The proposed approach has been tested on TAIFEX index. The computational results showedthat the proposed approach outperforms the former studies

Locating Post Offices Using Fuzzy Goal Programming and Geographical Information System (GIS)

This paper deals with the problem of locating new post offices in a megacity. To do so, a combination of geographicalinformation system (GIS) and fuzzy goal programming (FGP) is used. In order to locate new offices, first six types of servicefacilities with high levels of interactions with post offices are defined. Then, aspiration level of proximity for each servicefacility is determined. Based on these values, a fuzzy goal programming model is constructed to find potential locations offacilities. In order to determine the optimal locations among potential facilities, a maximal covering location problem(MCLP) is solved and results are reported. Results show that although the current state is near-optimal, for future expansionsof the network, the government should spend money on central and southern parts of this megacity

An Ontological and epistemological perspective of fuzzy set theory/ Turksen

xxvii, 514 hal.: ill.; 24 cm