Optimization with genetic algorithm and particle swarm optimization of type-2 fuzzy integrator for ensemble neural network in time series
Fernando Gaxiola, Patricia Melín, Fevrier Valdez, Juan R. Castro · 2016
In this paper two bio-inspired methods are used to optimize the type-2 fuzzy inference system integrator in an ensemble of three neural networks with type-2 fuzzy weights. The genetic algorithm and particle swarm optimization are used to optimize the type-2 fuzzy system integrators that work in response integration of the ensemble neural network for obtaining the final output. In this work an optimized type-2 fuzzy inference system integrator to perform the integration for an ensemble of three neural networks and the results for the two bio-inspired methods are presented. The proposed approach is applied to a case of time series prediction, specifically for the Mackey-Glass time series.