Intuitionistic Trapezoidal Fuzzy MAGDM Problems with Sumudu Transform in Numerical Methods

Robinson, Philip J.; Jeeva, S.

doi:10.4018/ijfsa.2019070101

Cited by 4 publications

(2 citation statements)

References 81 publications

(35 reference statements)

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“…In (8) the method that evaluates a trained ANN is proposed. In (9) and (10) , some aggregation operators for Trapezoidal Intuitionistic Fuzzy numbers are presented. Aggregation operators for MAGDM problems and calculation of attribute weights were much developed and utilized by author in (11) .…”

Section: Introductionmentioning

confidence: 99%

Intuitionistic Fuzzy Gram-Schmidt Orthogonalized Artificial Neural Network for Solving MAGDM Problems

Robinson,

Saranraj

2024

IJST

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Objectives: To propose a suitable decision-making model based on Intuitionistic Fuzzy sets (IFSs) and Gram-Schmidt orthogonalization process for Artificial Neural Network (ANN). Methods: The IFS data sets appearing in the form of matrices are aggregated using the available aggregation operators in the literature and then the collective aggregated information is processed through Gram-Schmidt orthogonalization for the revised input vectors which is then fed into the ANN algorithm following Delta Learning Rule for the next phase. The weight updation is performed through the ANN and the output is improvised. Findings: The proposed Gram-Scmidt Orthogonalization process is utilized in Intuitionistic Fuzzy Artificial Neural Network model. The Delta learning rule is utilized in the process of the Neural Network, where the Intuitionistic Fuzzy nature of the input data is transformed into a fuzzy data and then the ranking of the alternatives is done based on the weights updation through the learning phase of the ANN. Once the vector is trained out of the learning phase, it is then processed through the activation function for the final selection of the best alternative required of the Multiple Attribute Group Decision Making (MAGDM) problem posed in this work. To demonstrate the usefulness and applicability of this new method with the Gram-Schmidt process, the numerical example also adds more insight to the proposed methodology of ANN with the application of some Linear Space techniques. Novelty: Most of the research done on Intuitionistic Fuzzy Artificial Neural Network model are based on learning rules or using some other calculations. The proposed Gram-Schmidt Orthogonalization process is used to find the orthogonal basis that are used as input training vectors in the Delta learning rule for ANN. Keywords: MAGDM, ANN, Aggregation operators, Learning Rules, Intuitionistic Fuzzy sets, Gram-Scmidt Orthogonalization

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Section: Introductionmentioning

confidence: 99%

Intuitionistic Fuzzy Gram-Schmidt Orthogonalized Artificial Neural Network for Solving MAGDM Problems

Robinson,

Saranraj

2024

IJST

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“…Intuitionistic Fuzzy Neural Networks with various applications were discussed in [1,2]. Some arithmetic aggregation operators for MAGDM problems were proposed in [14][15][16][17][18][19]21] and are utilized in various decision-making applications. The authors in [12] developed a system for predicting automated rainfall using adaptive membership enhanced fuzzy classifiers.…”

Section: Introductionmentioning

confidence: 99%

A Novel Learning Rule in Intuitionistic Fuzzy Artificial Neural Network and its Applications to MAGDM Support System

Robinson

2023

Preprint

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In this work, Artificial Neural Network (ANN)-based Multiple Attribute Group Decision Making (MAGDM) models have been provided. These models take into consideration pairs of justifications referred to as weighted geometric pairs, where one component is employed to induce an ordering over the second component, which are intuitionistic fuzzy values, and then aggregated. The recommended strategy of a novel Delta Learning rule named as Robin-Delta Learning rule-based ANN is proposed in this article, where the inputs take the form of intuitionistic fuzzy matrices and are resolved and comparisons are made with the traditional Delta Learning Rule with a numerical illustration. Because it removes the unimportant decision alternatives from the system, the Robin-Delta ANN, which is a groundbreaking contribution in the field of intuitionistic fuzzy ANN, proves to be more effective than the previous methods of MAGDM problems and hence provide a new decision support system in the field of decision making.

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Application of Varieties of Learning Rules in Intuitionistic Fuzzy Artificial Neural Network

Robinson,

Leonishiya

2024

Lecture Notes in Networks and Systems

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Intuitionistic Trapezoidal Fuzzy MAGDM Problems with Sumudu Transform in Numerical Methods

Cited by 4 publications

References 81 publications

Intuitionistic Fuzzy Gram-Schmidt Orthogonalized Artificial Neural Network for Solving MAGDM Problems

Intuitionistic Fuzzy Gram-Schmidt Orthogonalized Artificial Neural Network for Solving MAGDM Problems

A Novel Learning Rule in Intuitionistic Fuzzy Artificial Neural Network and its Applications to MAGDM Support System

Application of Varieties of Learning Rules in Intuitionistic Fuzzy Artificial Neural Network

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