Hamming Distance-Based Intuitionistic Fuzzy Artificial Neural Network with Novel Back Propagation Method
An artificial neural network (ANN)-based decision support system model, which aggregates intuitionistic fuzzy matrix data using a recently introduced operator, is developed in this work. Several desirable features related to distance measures of aggregation operators and artificial neural networks,...
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MDPI AG
2025-06-01
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| author | John Robinson Peter Dawson Wilson Arul Prakash Selvaraj |
| author_facet | John Robinson Peter Dawson Wilson Arul Prakash Selvaraj |
| author_sort | John Robinson Peter Dawson |
| collection | DOAJ |
| description | An artificial neural network (ANN)-based decision support system model, which aggregates intuitionistic fuzzy matrix data using a recently introduced operator, is developed in this work. Several desirable features related to distance measures of aggregation operators and artificial neural networks, including the backpropagation method, are investigated to support the application of the proposed methodologies to multiple attribute group decision-making (MAGDM) problems using intuitionistic fuzzy information. A novel and enhanced aggregation operator—the Hamming–Intuitionistic Fuzzy Power Generalized Weighted Averaging (H-IFPGWA) operator—is proposed for weight determination in MAGDM situations. Numerical examples are provided, and various ranking techniques are used to demonstrate the effectiveness of the suggested strategy. Subsequently, an identical numerical example is solved without bias using the ANN backpropagation approach. Additionally, a novel algorithm is created to address MAGDM problems using the proposed backpropagation model in an unbiased manner. Several defuzzification operators are applied to solve the numerical problems, and the efficacy of the solutions is compared. For MAGDM situations, the novel approach works better than the previous ANN approaches. |
| format | Article |
| id | doaj-art-c2e8abe2d5fa4b54a483e507a38a3c29 |
| institution | Kabale University |
| issn | 2673-4591 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-c2e8abe2d5fa4b54a483e507a38a3c292025-08-20T03:27:28ZengMDPI AGEngineering Proceedings2673-45912025-06-01951910.3390/engproc2025095009Hamming Distance-Based Intuitionistic Fuzzy Artificial Neural Network with Novel Back Propagation MethodJohn Robinson Peter Dawson0Wilson Arul Prakash Selvaraj1Department of Mathematics, Bishop Heber College, Affiliated to Bharathidasan University, Tiruchirappalli 620017, IndiaDepartment of Mathematics, Bishop Heber College, Affiliated to Bharathidasan University, Tiruchirappalli 620017, IndiaAn artificial neural network (ANN)-based decision support system model, which aggregates intuitionistic fuzzy matrix data using a recently introduced operator, is developed in this work. Several desirable features related to distance measures of aggregation operators and artificial neural networks, including the backpropagation method, are investigated to support the application of the proposed methodologies to multiple attribute group decision-making (MAGDM) problems using intuitionistic fuzzy information. A novel and enhanced aggregation operator—the Hamming–Intuitionistic Fuzzy Power Generalized Weighted Averaging (H-IFPGWA) operator—is proposed for weight determination in MAGDM situations. Numerical examples are provided, and various ranking techniques are used to demonstrate the effectiveness of the suggested strategy. Subsequently, an identical numerical example is solved without bias using the ANN backpropagation approach. Additionally, a novel algorithm is created to address MAGDM problems using the proposed backpropagation model in an unbiased manner. Several defuzzification operators are applied to solve the numerical problems, and the efficacy of the solutions is compared. For MAGDM situations, the novel approach works better than the previous ANN approaches.https://www.mdpi.com/2673-4591/95/1/9MAGDMANNaggregation operatorsbackpropagationintuitionistic fuzzy setsartificial neural network |
| spellingShingle | John Robinson Peter Dawson Wilson Arul Prakash Selvaraj Hamming Distance-Based Intuitionistic Fuzzy Artificial Neural Network with Novel Back Propagation Method Engineering Proceedings MAGDM ANN aggregation operators backpropagation intuitionistic fuzzy sets artificial neural network |
| title | Hamming Distance-Based Intuitionistic Fuzzy Artificial Neural Network with Novel Back Propagation Method |
| title_full | Hamming Distance-Based Intuitionistic Fuzzy Artificial Neural Network with Novel Back Propagation Method |
| title_fullStr | Hamming Distance-Based Intuitionistic Fuzzy Artificial Neural Network with Novel Back Propagation Method |
| title_full_unstemmed | Hamming Distance-Based Intuitionistic Fuzzy Artificial Neural Network with Novel Back Propagation Method |
| title_short | Hamming Distance-Based Intuitionistic Fuzzy Artificial Neural Network with Novel Back Propagation Method |
| title_sort | hamming distance based intuitionistic fuzzy artificial neural network with novel back propagation method |
| topic | MAGDM ANN aggregation operators backpropagation intuitionistic fuzzy sets artificial neural network |
| url | https://www.mdpi.com/2673-4591/95/1/9 |
| work_keys_str_mv | AT johnrobinsonpeterdawson hammingdistancebasedintuitionisticfuzzyartificialneuralnetworkwithnovelbackpropagationmethod AT wilsonarulprakashselvaraj hammingdistancebasedintuitionisticfuzzyartificialneuralnetworkwithnovelbackpropagationmethod |