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Aggregation in Ensemble Neural Models with Type-3 and Intuitionistic Fuzzy Logic

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New Directions on Hybrid Intelligent Systems Based on Neural Networks, Fuzzy Logic, and Optimization Algorithms

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1146))

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Abstract

Type-3 intuitionistic fuzzy aggregation in ensembles of neural networks is offered in this work. Aggregation is essential for combining the prediction of the networks that constitute the ensemble. The fuzzy aggregator is designed such that the combined prediction is better than the individual predictions. The type-3 intuitionistic fuzzy system estimates the weights assigned to the individual predictions in a weighted average approach. Prediction uncertainty based on data is modeled by type-3 and uncertainty of the experts is modeled by intuitionistic fuzzy sets. The proposal is tested with an illustrative example of prediction.

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Authors and Affiliations

  1. Tijuana Institute of Technology, TecNM, Tijuana, BC, Mexico

    Patricia Melin & Oscar Castillo

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  1. Patricia Melin
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  2. Oscar Castillo
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Correspondence to Patricia Melin .

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Editors and Affiliations

  1. Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  2. Division of Graduate Studies &Research, Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo

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Melin, P., Castillo, O. (2024). Aggregation in Ensemble Neural Models with Type-3 and Intuitionistic Fuzzy Logic. In: Melin, P., Castillo, O. (eds) New Directions on Hybrid Intelligent Systems Based on Neural Networks, Fuzzy Logic, and Optimization Algorithms. Studies in Computational Intelligence, vol 1146. Springer, Cham. https://doi.org/10.1007/978-3-031-53713-4_7

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