Population-based Automatic Fuzzy Neural Network for Online, Knowledge-based Learning

In this paper, a novel fuzzy connectionist system for incremental online learning and knowledge discovery called Population-based Automatic Fuzzy Neural Network (PAFuNN) is demonstrated in detail. PAFuNNs evolve out of incremental learning. New connections and neurons are created based on a population of samples while operating the system which has the advantage of controlling the number of neurons involved and leads to the low complexity of the network. Learning Automata is implemented in order to optimize the network parameters including sensitivity and error thresholds to enhance the performance of the entire system. Afterward, the proposed method is compared with Evolving Fuzzy Neural Network (EFuNN) as a general online learning machine on two case study datasets consisting of gas furnace and iris data for prediction and classification tasks leading to the thorough analysis of the effects of selecting appropriate automata. Less complex, more accurate and robust results are obtained for the proposed method in comparison with the EFuNN.