A New Design of Mamdani Complex Fuzzy Inference System for Multiattribute Decision Making Problems

Selvachandran, Ganeshsree; Quek, Shio Gai; Lan, Luong Thi Hong; Sơn, Lê Hoàng; Giang, Nguyễn Long; Ding, Weiping; Abdel‐Basset, Mohamed; Albuquerque, Victor Hugo C. de

doi:10.1109/tfuzz.2019.2961350

Cited by 60 publications

(46 citation statements)

References 27 publications

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“…Future works related to the plithogenic entropy include studying more examples of entropy measures for plithogenic sets with structures different from the one mentioned in Theorem 1, and to apply the different types of entropy measure for plithogenic sets onto real life datasets. We are also working on developing entropy measures for other types of plithogenic sets such as plithogenic intuitionistic fuzzy sets and plithogenic neutrosophic sets, and the study of the application of these measures in solving real world problems using real life datasets [36][37][38][39][40][41][42][43].…”

Section: Discussionmentioning

confidence: 99%

Entropy Measures for Plithogenic Sets and Applications in Multi-Attribute Decision Making

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Plithogenic set is an extension of the crisp set, fuzzy set, intuitionistic fuzzy set, and neutrosophic sets, whose elements are characterized by one or more attributes, and each attribute can assume many values. Each attribute has a corresponding degree of appurtenance of the element to the set with respect to the given criteria. In order to obtain a better accuracy and for a more exact exclusion (partial order), a contradiction or dissimilarity degree is defined between each attribute value and the dominant attribute value. In this paper, entropy measures for plithogenic sets have been introduced. The requirements for any function to be an entropy measure of plithogenic sets are outlined in the axiomatic definition of the plithogenic entropy using the axiomatic requirements of neutrosophic entropy. Several new formulae for the entropy measure of plithogenic sets are also introduced. The newly introduced entropy measures are then applied to a multi-attribute decision making problem related to the selection of locations.

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Section: Discussionmentioning

confidence: 99%

Entropy Measures for Plithogenic Sets and Applications in Multi-Attribute Decision Making

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“…For ANFIS-SC, as the second type of ANFIS model in this paper, two rules are considered. Therefore, Mamdani FIS structure [30] is generated for the ANFIS model, which contains one rule for each cluster. As can be seen, if the input data (in1) belongs to the first cluster (in1cluster1) and the second cluster (in1cluster2), then the output data belongs to output cluster 1 (output1cluster1) and output cluster 2 (output1cluster2), respectively.…”

Section:  mentioning

confidence: 99%

Optimal Day-Ahead Self-Scheduling and Operation of Prosumer Microgrids Using Hybrid Machine Learning-Based Weather and Load Forecasting

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Prosumer microgrids (PMGs) are considered as active users in smart grids. These units are able to generate and sell electricity to aggregators or neighbor consumers in the prosumer market. Although the optimal scheduling and operation of PMGs have received a great deal of attention in recent studies, the challenges of PMG's uncertainties such as stochastic behavior of load data and weather conditions (solar irradiance, ambient temperature, and wind speed) and corresponding solutions have not been thoroughly investigated. In this paper, a new energy management systems (EMS) based on weather and load forecasting is proposed for PMG's optimal scheduling and operation. Developing a novel hybrid machine learning-based method using adaptive neuro-fuzzy inference system (ANFIS), multilayer perceptron (MLP) artificial neural network (ANN), and radial basis function (RBF) ANN to precisely predict the load and weather data is one of the most important contributions of this article. The performance of the forecasting process is improved by using a hybrid machine learning-based forecasting method instead of conventional ones. The demand response (DR) program based on the forecasted data and considering the degradation cost of the battery storage system (BSS) are other contributions. The comparison of obtained test results with those of other existing approaches illustrates that more appropriate PMG's operation cost is achievable by applying the proposed DR-based EMS using a new hybrid machine learning forecasting method.

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“…Nonetheless, the CFIS [45] has limitations in the base set as well as the complex fuzzy operations. Thus, Selvachandran et al [49] have proposed the Mamdani Complex Fuzzy Inference System (M-CFIS) with specific inference mechanism according to the Mamdani type. CFIS and its extensions were successfully applied to function approximation, generating higher-order TSK models, diagnosis, etc.…”

Section: Introductionmentioning

confidence: 99%

“…A limitation in the M-CFIS [49] is the rule base, which may be residual and incongruous with the dataset. In order to cope with those issues, Tuan et al [55] introduced a new improvement of M-CFIS called M-CFIS-R (Mamdani Complex Fuzzy Inference System with Rule reduction Using Complex Fuzzy Measures in Granular Computing).…”

Section: Introductionmentioning

confidence: 99%

A New Complex Fuzzy Inference System With Fuzzy Knowledge Graph and Extensions in Decision Making

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Context and background: Complex fuzzy theory has a strong practical implication in many real-world applications. Complex Fuzzy Inference System (CFIS) is a powerful technique to overcome the challenges of uncertain, periodic data. However, a question is raised for CFIS: How can we deduce and predict the result in case there is little knowledge about data information and rule base? This is significance because many real applications do not have enough knowledge of rule base for inference so that the performance of systems may be low. Thus, it is necessary to have an approximate reasoning method to represent and derive final results. Motivation: Recently, the Mamdani Complex Fuzzy Inference System (M-CFIS) has been proposed with a specific inference mechanism according to the Mamdani type. A new improvement so-called the Mamdani Complex Fuzzy Inference System with Rule Reduction (M-CFIS-R) has been designed to utilize granular computing with complex similarity measures to reduce the rule base so as to gain better performance in decision-making problems. However in M-CFIS-R, testing data are checked by matching with each rule in the rule base, which leads to a high cost of computational time. Besides, if the testing data contain records that are not inferred by the rule base, the output cannot be generated. This happens in real commerce systems in which the rule base is small at the time of creation and needs to feed with new rules. Methodology: In order to handle those issues, this paper first time proposes the Fuzzy Knowledge Graph to represent the rule base in terms of linguistic labels and their relationships according to the rule set. An adjacent matrix of Fuzzy Knowledge Graph is generated for inference. When a record in the Testing dataset is given, it would be fuzzified and labelled. Each component in the record is checked with the Fuzzy Knowledge Graph by the inference mechanism in approximate reasoning called Fast Inference Search Algorithm. Then, we derive the label of the new record by the Max-Min operator. Besides, we also propose four extensions of

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A New Design of Mamdani Complex Fuzzy Inference System for Multiattribute Decision Making Problems

Cited by 60 publications

References 27 publications

Entropy Measures for Plithogenic Sets and Applications in Multi-Attribute Decision Making

Entropy Measures for Plithogenic Sets and Applications in Multi-Attribute Decision Making

Optimal Day-Ahead Self-Scheduling and Operation of Prosumer Microgrids Using Hybrid Machine Learning-Based Weather and Load Forecasting

A New Complex Fuzzy Inference System With Fuzzy Knowledge Graph and Extensions in Decision Making

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