基于TOPSIS的属性关联直觉模糊多属性决策方法

针对属性值为直觉模糊数,已知部分属性偏好关系及属性交互类型的属性关联多属性决策问题给出决策方法.首先定义方案到正(负)理想方案的距离及各方案与正理想方案相对贴近度.然后以极大化各方案与相对贴近度为目标建立优化模型,确定出属性集的模糊测度.进而基于直觉模糊Choquet积分算子计算各方案的直觉模糊综合评价值,再根据直觉模糊数的得分值及精确度得到方案的排序.最后通过实例验证了方法的有效可行性.     

基于模糊理论的改进TOPSIS法在工程项目评标中的应用

评标过程会涉及到大量的定性分析,定性分析过程中受到专家的经验知识和主观判断的影响较大,因而所确定的指标权重和指标值带有较强的主观模糊性,易造成评标决策的失误.基于模糊数能量化主观模糊性并融合到指标权重与指标值中,采用模糊理论对TOPSIS法进行改进,评价指标权重和指标值均用梯形模糊数表示,建立基于模糊TOPSIS法的评标模型.结果表明,依据CC值不仅可以对投标人排序,而且还能客观的反应出投标人自身的优劣,避免了劣中选优的情况发生.最后通过示例对提议模型的应用过程及可行性进行了说明.     

Financial planning for the preventive maintenance of power distribution systems via fuzzy AHP

One of the most important objectives of electricity distribution companies is to improve the reliability of the distribution networks. To this end, the electricity distribution companies try to optimally use the existing financial resources in the planning of preventive maintenance (PM) programs to reduce the imposed costs on the system due to the failure of network components and to improve the network reliability. In fuzzy analytical hierarchical process (fuzzy AHP) method, the degree of network reliability and the effectiveness of PM budget in the improvement of network reliability are selected as decision criteria in the budget allocation procedure. The areas served by the power distribution network are prioritized relative to each other and are assigned weights based on these priorities. The PM budget is determined based on the obtained weights. The medium voltage distribution network of seven areas in the city of Tehran have been selected for the implementation of the proposed method and the analysis of the obtained results. © 2014 Wiley Periodicals, Inc. Complexity 21: 36–46, 2016     

Data fusion in the wavelet domain by means of fuzzy aggregation connectives

Dual-domain classification analysis is proposed to identify pigments used in works of art studied by Raman spectroscopy and X-ray fluorescence spectrometry. By means of this methodology, Raman and X-ray fluorescence data are jointly processed by a high-level fusion approach. The system proposed aims to avoid the pre-processing stage and directly process raw data obtained from the instrument. The system is tested with spectra contaminated with background components of different shapes and intensities and with those with the background removed by line segment correction. The benefits of the approach were well demonstrated in a study of an ochre pigment classification.The approach is based on the main advantage of wavelet transform, which is multiresolution. Each spectrum is split into blocks, according to a specific frequency, to form a wavelet prism. Partial least squares-discriminant analysis (PLS-DA) is then applied to those blocks which contain the deterministic part of the signal and are not influenced by noise and background signal components. At the end, to obtain the final classification assignment, high-level data fusion of the classifications results (decision levels) obtained from PLS-DA analysis is done by means of fuzzy aggregation connective operators. Our study showed that fuzzy aggregation may be suitable for performing high-level data fusion on dual-domain data. This method can be automated so that classification can be rapid. It can handle classifications with different levels of difficulty and requires no prior knowledge of sample composition.     

A Novel Fuzzy Document Based Information Retrieval Model for Forecasting

Information retrieval systems are generally used to find documents that are most appropriate according to some query that comes dynamically from users. In this paper a novel Fuzzy Document based Information Retrieval Model (FDIRM) is proposed for the purpose of Stock Market Index forecasting. The novelty of proposed approach is a modified tf-idf scoring scheme to predict the future trend of the stock market index. The contribution of this paper has two dimensions, 1) In the proposed system the simple time series is converted to an enriched fuzzy linguistic time series with a unique approach of incorporating market sentiment related information along with the price and 2) A unique approach is followed while modeling the information retrieval (IR) system which converts a simple IR system into a forecasting system. From the performance comparison of FDIRM with standard benchmark models it can be affirmed that the proposed model has a potential of becoming a good forecasting model. The stock market data provided by Standard & Poor’s CRISIL NSE Index 50 (CNX NIFTY-50 index) of National Stock Exchange of India (NSE) is used to experiment and validate the proposed model. The authentic data for validation and experimentation is obtained from http://www.nseindia.com which is the official website of NSE. A java program is under construction to implement the model in real-time with graphical users’ interface.     

Parametric (R,S)-norm Entropy on Intuitionistic Fuzzy Sets with a New Approach in Multiple Attribute Decision Making

The theory of intuitionistic fuzzy sets (IFSs) is well suitable to deal with vagueness and hesitancy. In the present communication, a new parametric $(R,S)$-norm intuitionistic fuzzy entropy is proposed with the proof of validity and some of its properties also discussed. The intuitionistic fuzzy entropy is useful to represent the decision information in decision making process since it is characterized by the degree of satisfiability, degree of non-satisfiability and hesitancy degree. Based on this proposed IF entropy, a new multiple attribute decision making (MADM) method is introduced and compared with an existing method. In case of attributes weight, two cases (one with completely unknown attributes weight and other with partially known attributes weight) are discussed with the help of examples. In the end, a case study of insurance companies on the basis of service qualitities is given.     

Multi Fuzzy Fractal Theorems in Fuzzy Metric Spaces

The Hutchinson-Barnsley (HB) operators are studied for multi iterated function system in a general setting. Some existence and uniqueness results are obtained in fuzzy fractal and multi fuzzy fractal spaces. From them, we obtain our results, which generalize some recent results in the literature.     

Preparation and characterization novel dioctyl terephthalate blended polyvinyl alcohol-composite films incorporated with the graphene oxide and silver nanoparticles

Dioctyl terephthalate is of great interest as a replacement for the phthalate plasticizers such as dioctyl phthalate and diisononyl phthalate due to its orthophthalate-free and non-carcinogenic properties. This study focused on the production, characterization and optimization of the quality characteristics of its film properties, such as the mechanical, hydrophilic and thermal properties of dioctyl terephthalate-blended polyvinyl alcohol composites modified with graphene oxide and silver nanoparticles using TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) based Taguchi Method. Dioctyl terephthalate has brought remarkable features, such as high elastic modulus, and hydrophilic and thermal stability to the polyvinyl alcohol matrix. The optimum Dioctyl terephthalate -blended polyvinyl alcohol films have a 2.26 times lower contact angle and a 13.41 times higher elastic modulus than the reference polyvinyl alcohol film. Dioctyl terephthalate should be preferentially used to manufacture more durable and hydrophilic composite films such as fibers, disposable underpad or industrial swab, instead of toxic phthalate plasticizers.     

Decision risk analysis for an interval TOPSIS method

TOPSIS is a multi-attribute decision making (MADM) technique for ranking and selection of a number of externally determined alternatives through distance measures. When the collected data for each criterion is interval and the risk attitude for a decision maker is unknown, we present a new TOPSIS method for normalizing the collected data and ranking the alternatives. The results show that the decision maker with different risk attitude ranks the different alternatives.     

Design of fuzzy radial basis function-based polynomial neural networks

In this study, we introduce a new design methodology of fuzzy radial basis function-based polynomial neural networks. In many cases, these models do not come with capabilities to deal with granular information. With this regard, fuzzy sets offer several interesting and useful opportunities. This study presents the development of fuzzy radial basis function-based neural networks augmented with virtual input variables. The performance of the proposed category of models is quantified through a series of experiments, in which we use two machine learning data sets and two publicly available software development effort data.