Judgement of Learning for Metacognitive Type-2 Fuzzy Inference System
This paper proposes the McIT2FIS-JOL algorithm for oceanic wave data prediction. The learning algorithm is preceded by a meta-memory layer for judgement of learning (JOL). The input is divided into subsets. Each subset is learnt by the learning algorithm, which in turn is regulated by the metacognitive layer. Each model begins with zero rules. The underlying learning algorithm then employs prediction error and novelty of sample to add rules to the network. The metacognitive component of the underlying learning algorithm determines whether each incoming sample is learnt, deleted, or reserved for future use. Once the models have learnt from their subsequent subsets, the learning of the input provided to each model is judged by employing JOL. The model whose JOL measure is the best is then utilised for processing the entire input. The performance of McIT2FIS-JOL is evaluated on a real world oceanic wave data prediction problem. It is compared with MLP, SAFIS, and its predecessor McIT2FIS. The results indicate that significant learning is possible utilising a smaller sample of incoming data, improving learning time.
KeywordsInterval Type-2 Fuzzy systems Meta-cognition Meta-memory Judgement of learning Oceanic wave data prediction
I deeply thank Professor Suresh Sundaram, Associate Professor at Nanyang Technological University, Singapore, at the Computational Intelligence Lab, for his valuable guidance throughout the research period.
- 1.Babu, G.S., Savitha, R., Suresh, S.: A projection based learning in meta-cognitive radial basis function network for classification problems. In: The 2012 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2012). https://doi.org/10.1109/IJCNN.2012.6252769
- 6.Das, A.K., Suresh, S., Srikanth, N.: Meta-cognitive interval type-2 neuro-fuzzy inference system for wind prediction. In: 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI), pp. 1–6 (2014)Google Scholar
- 11.Nelson, T.O., Narens, L.: Why investigate metacognition? In: Metacognition: Knowing About Knowing, pp. 1–25 (1994)Google Scholar
- 14.Suresh, S., Subramanian, K.: A sequential learning algorithm for meta-cognitive neuro-fuzzy inference system for classification problems. In: 2011 International Joint Conference on Neural Networks (IJCNN), pp. 2507–2512. IEEE (2011). https://doi.org/10.1109/IJCNN.2011.6033545