Abstract
This paper describes a proposed method for type-2 fuzzy integration that can be used in the fusion of responses for an ensemble neural network. We consider the case of the design of a type-2 fuzzy integrator for fusion of a neural network ensemble. The network structure of the ensemble may have a maximum of 5 modules. This integrator consists of 32 fuzzy rules, with 5 inputs depending on the number of modules of the neural network ensemble and one output. Each input and output linguistic variable of the fuzzy system uses Gaussian membership functions. The performance of type-2 fuzzy integrators is analyzed under different levels of uncertainty to find out the best design of the membership functions. In this case the proposed method is applied to time series prediction.
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Pulido, M., Melin, P. (2013). A New Method for Type-2 Fuzzy Integration in Ensemble Neural Networks Based on Genetic Algorithms. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Recent Advances on Hybrid Intelligent Systems. Studies in Computational Intelligence, vol 451. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33021-6_14
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DOI: https://doi.org/10.1007/978-3-642-33021-6_14
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