Data driven modelling based on Recurrent Interval-Valued Metacognitive Scaffolding Fuzzy Neural Network

The Metacognitive Scaffolding Learning Machine (McSLM), combining the concept of metacognition- what-to-learn, how-to-learn, and when-to-learn, and the Scaffolding theory-a tutoring theory for a learner to learn a complex task, has been successfully developed to enhance the capability of Evolving Intelligent Systems (EIS) in processing non-stationary data streams. Three issues, namely uncertainty, temporal behaviour, unknown system order, are however uncharted by any existing McSLMs and all McSLMs in the literature are designed for classification problems. This paper proposes a novel McSLM, called Recurrent Interval-Valued Metacognitive Scaffolding Fuzzy Neural Network (RIVMcSFNN) and used to solve regression and time-series modelling problems from data streams. RIVMcSFNN presents a novel recurrent network architecture as a cognitive constituent, which features double local recurrent connections at both the hidden layer and the consequent layer. The new recurrent network architecture is driven by the interval-valued multivariate Gaussian function in the hidden node and the nonlinear Wavelet function in the consequent node. As with its predecessors, the RIVMcSFNN characterises an open structure, where it can automatically grow, prune, adjust, merge, recall its hidden node and can select relevant data samples on the fly using an online active learning methodology. The RIVMcSFNN is also equipped with the online dimensionality reduction technique to cope with the curse of dimensionality. All learning mechanisms are carried out in the single-pass and local learning mode and actualise the plug-and-play learning principle, which aims to minimise the use of pre-and/or post-training steps. The efficacy of our algorithm was tested using numerous data-driven modelling problems and comprehensive comparisons with its counterparts. The RIVMcSFNN demonstrated substantial improvements in both accuracy and complexity against existing variants of the McSLMs and EISs. (C) 2017 Elsevier B.V. All rights reserved.