Neural Comput - Metacognitive learning in a fully complex-valued radial basis function neural network.

Tópicos

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Resumo

Recent studies on human learning reveal that self-regulated learning in a metacognitive framework is the best strategy for efficient learning. As the machine learning algorithms are inspired by the principles of human learning, one needs to incorporate the concept of metacognition to develop efficient machine learning algorithms. In this letter we present a metacognitive learning framework that controls the learning process of a fully complex-valued radial basis function network and is referred to as a metacognitive fully complex-valued radial basis function (Mc-FCRBF) network. Mc-FCRBF has two components: a cognitive component containing the FC-RBF network and a metacognitive component, which regulates the learning process of FC-RBF. In every epoch, when a sample is presented to Mc-FCRBF, the metacognitive component decides what to learn, when to learn, and how to learn based on the knowledge acquired by the FC-RBF network and the new information contained in the sample. The Mc-FCRBF learning algorithm is described in detail, and both its approximation and classification abilities are evaluated using a set of benchmark and practical problems. Performance results indicate the superior approximation and classification performance of Mc-FCRBF compared to existing methods in the literature.

Resumo Limpo

recent studi human learn reveal selfregul learn metacognit framework best strategi effici learn machin learn algorithm inspir principl human learn one need incorpor concept metacognit develop effici machin learn algorithm letter present metacognit learn framework control learn process fulli complexvalu radial basi function network refer metacognit fulli complexvalu radial basi function mcfcrbf network mcfcrbf two compon cognit compon contain fcrbf network metacognit compon regul learn process fcrbf everi epoch sampl present mcfcrbf metacognit compon decid learn learn learn base knowledg acquir fcrbf network new inform contain sampl mcfcrbf learn algorithm describ detail approxim classif abil evalu use set benchmark practic problem perform result indic superior approxim classif perform mcfcrbf compar exist method literatur

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