PIE-RSPOP: A brain-inspired pseudo-incremental ensemble rough set pseudo-outer product fuzzy neural network

Abstract

A novel pseudo-incremental ensemble rough set pseudo-outer product (PIE-RSPOP) fuzzy neural network is proposed for learning and prediction of trends in complex temporal series. It incorporates four theories pertaining to the learning and memorizing mechanisms in human beings. BCM theory of synaptic metaplasticity replaces Hebbian learning of weights of fuzzy rules and allows for a more natural associative–dissociative learning of weights. Short term forgetting of weights of the fuzzy rules is integrated for rules that are recalled and rules that are not recalled by an instance of data through Ebbinghaus theory of forgetting due to decay and displacement. The long term forgetting of networks in the ensemble is also incorporated through exponential decay of weights based on the age and strengths of the networks. Lastly, a hippocampal mechanism for caching the memories for subsequent recall is proposed. Another contribution of PIE-RSPOP is its ability to deal with concept drift and less storage of historical data. Lastly, despite the usually uninterpretable nature of incremental ensembles of fuzzy networks, a scheme to derive simple interpretable single knowledge base is also proposed. Variety of numerical results on standard datasets are used to demonstrate the advantages of PIE-RSPOP over other incremental learning methods.