Hybrid learning algorithm for interval type-2 fuzzy neural networks

In this paper, a class of Interval Type-2 Fuzzy Neural Networks (IT2FNN) is proposed, which is functionally equivalent to interval type-2 fuzzy inference systems. The computational process envisioned for a fuzzy-neural system is as follows: it starts with the development of an Interval Type-2 Fuzzy Neuron, which is based on biological neural morphologies, followed by learning mechanisms. We describe how to decompose the parameter set such that the hybrid learning rule of adaptive networks can be applied to the IT2FNW architecture.