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Neuro-fuzzy systems are learning machines that employ algorithms derived from arti cial neural networks to nd the parameters of a fuzzy inference system. These hybridintelligent systems can learn fuzzy rules from the data, while preserving their semantic properties. The learning algorithms employed in these systems are inspired from the physiology and functioning of human brain. As the study on neuroscience and cognitive psychology is an ongoing process, it is essential that these machine learning algorithms be extended with developments from the ndings in human learning psychology. Studies in human learning psychology have shown that self-regulated learning in a meta-cognitive framework is the best learning strategy. Meta-cognition also provides a learner with self-monitoring, which is a step-by-step process of evaluation during the learning process. In this thesis, we develop a Meta-Cognitive sequential learning algorithm for Neuro-Fuzzy Inference System called McFIS which is based on a well-known model of meta-cognition proposed by Nelson and Narens. Similar to Nelson and Narens model of meta-cognition, McFIS consists of a cognitive and a meta-cognitive component. A Takagi-Sugeno-Kang type neuro-fuzzy inference system forms the cognitive component and a self-regulatory learning mechanism is its meta-cognitive component. The metacognitive component monitors the knowledge in the cognitive component and controls the learning by e ciently deciding on which sample to learn, when to learn it and how to learn it, e ciently. Such a learning strategy helps the network generalize the functional relationship between input and output. The performance evaluation of McFIS on a set of benchmark function approximation and time-series prediction problems indicate signi cant improvement over other state-of-the-art techniques. In addition, quantitative study on a set classi cation problems indicate motivating results.

Meta-cognitive learning algorithm for neuro-fuzzy inference systems