An overview of membership function generation techniques for pattern recognition

Medasani, S.; Kim, Jaeseok; Krishnapuram, Raghu

doi:10.1016/s0888-613x(98)10017-8

Cited by 285 publications

(155 citation statements)

References 17 publications

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“…2). For expert systems, the fuzzy partition should be based on automated methods and/or on the distribution and characteristics of the data itself (Medasania et al 1998).…”

Section: Fuzzy Logic Modellingmentioning

confidence: 99%

Using fuzzy logic modelling to simulate farmers’ decision-making on diversification and integration in the Mekong Delta, Vietnam

et al. 2010

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To reveal farmers' motives for on-farm diversification and integration of farming components in the Mekong Delta, Vietnam, we developed a fuzzy logic model (FLM) using a 10-step approach. Farmers' decision-making was mimicked in a three-layer hierarchical architecture of fuzzy inference systems, using data of 72 farms. The model includes three variables for family motives of diversification, six variables related to component integration, next to variables for the production factors and for farmers' appreciation of market prices and know-how on 10 components. To obtain a good classification rate of the less frequent activities, additional individual fine-tuning was necessary after general model calibration. To obtain the desired degree of sensitivity to each variable, it was necessary to use up to five linguistic values for some of the input and output variables in the intermediate hierarchical layers. Model's sensitivity to motivational variables determining diversification and integration was of the same magnitude as its sensitivity to market prices and farmers' know-how of the activities, but less than its sensitivity to labour, capital and land endowment. Modelling to support strategic decision-making seems too elaborate for individual farms, but FLM will be useful to integrate farmers' opinions in strategic decision-making at higher hierarchical levels.

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“…2). For expert systems, the fuzzy partition should be based on automated methods and/or on the distribution and characteristics of the data itself (Medasania et al 1998).…”

Section: Fuzzy Logic Modellingmentioning

confidence: 99%

Using fuzzy logic modelling to simulate farmers’ decision-making on diversification and integration in the Mekong Delta, Vietnam

et al. 2010

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“…It is also possible to consider parameterized functions in expert knowledge extraction, where the main task is the adjustment of the parameters by means of statistical methods. Some of them are described in (Medasani, Kim & Krishnapuram, 1998). …”

Section: Fuzzy Partition Designmentioning

confidence: 99%

Expert guided integration of induced knowledge into a fuzzy knowledge base

Guillaume¹,

Magdalena

2006

Soft Comput

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This paper proposes a method for building accurate and interpretable systems by integrating expert and induced knowledge into a single knowledge base. To favor the cooperation between expert knowledge and data, the induction process is run under severe constraints to ensure the fully control of the expert. The procedure is made up of two hierarchical steps. Firstly, a common fuzzy input space is designed according to both the data and expert knowledge. The compatibility of the two types of partitions, expert and induced, is checked according to three criteria: range, granularity and semantic interpretation. Secondly, expert rules and induced rules are generated according to the previous common fuzzy input space. Then, induced and expert rules have to be merged into a new rule base. Thanks to the common universe resulting from the first step, rule comparison can be made at the linguistic level only. The possible conflict situations are managed and the most important rule base features, consistency, redundancy and completeness, are studied. The first step is thoroughly described in this paper, while the second is only introduced.

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“…In literature, many methods have been found of membership function generation based on heuristics, histograms, neural networks, clustering, genetic algorithm [4,5,6]. The heuristic method uses predefined shape of membership functions [7].…”

Section: Introductionmentioning

confidence: 99%

“…Histograms of attribute provide information about the distribution of in put attribute values. Generating membership function using histogram analysis is simple and convenient [5].…”

Section: Introductionmentioning

confidence: 99%

Developing Membership Functions and Fuzzy Rules from Numerical Data for Decision Making

Yadav

2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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Nowadays, decision making using fuzzy logic is a major research area for scientists, researchers and project managers. Construction of membership functions and fuzzy rules from numerical data is very important in various applications of the fuzzy set theory. Therefore, in this paper a model is proposed for development of membership functions and fuzzy rules from numerical data for decision making. The main advantage of the proposed model is its simplicity. The proposed model is applied on Fisher's Iris data for decision making. The validation result shows that proposed model has a higher accuracy than existing models.

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An overview of membership function generation techniques for pattern recognition

Cited by 285 publications

References 17 publications

Using fuzzy logic modelling to simulate farmers’ decision-making on diversification and integration in the Mekong Delta, Vietnam

Using fuzzy logic modelling to simulate farmers’ decision-making on diversification and integration in the Mekong Delta, Vietnam

Expert guided integration of induced knowledge into a fuzzy knowledge base

Developing Membership Functions and Fuzzy Rules from Numerical Data for Decision Making

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