Extreme Learning for Evolving Hybrid Neural Networks

Abstract

This paper addresses a structure and introduces an evolving learning approach to train uninorm-based hybrid neural networks using extreme learning concepts. Evolving systems are high level adaptive systems able to simultaneously modify their structures and parameters from a stream of data, online. Learning from data streams is a contemporary and challenging issue due to the increasing rate of the size and temporal availability of data, turning traditional learning methods impracticable. Uninorm-based neurons, rooted in triangular norms and co norms, generalize fuzzy neurons. Uninorms bring flexibility and generality to fuzzy neuron models as they can behave like triangular norms, triangular co norms, or in between by adjusting identity elements. This feature adds a form of plasticity in neural network modeling. An online clustering method is used to granulate the input space, and a scheme based on extreme learning is developed to train the neural network. Computational results show that the learning approach is competitive when compared with alternative evolving modeling methods.