Meta-cognitive RBF Network and its Projection Based Learning algorithm for classification problems

► Meta-cognitive learning to emulate human learning components such as what-to-learn, when-to-learn and how-to-learn from sequence of training data. ► Sample learning, sample deletion and sample reserve strategy are proposed. Meta-cognitive component in PBL-McRBFN choose of the strategy based on information present in current sample and existing knowledge in RBF. ► PBL-McRBFN evolves the network architecture automatically and the strategies are also adapted to accommodate coarse knowledge first followed by fine tuning. ► Sequential learning algorithm uses computationally less intensive project based learning algorithm. ► Performance of proposed algorithm is compared with well-known fast learning neural networks reported in the literature using UCI data sets.