Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Fully Complex Multi-Layer Perceptron Network for Nonlinear Signal Processing
Journal of VLSI Signal Processing Systems
Approximation by fully complex multilayer perceptrons
Neural Computation
Orthogonality of decision boundaries in complex-valued neural networks
Neural Computation
Nonlinear Complex-Valued Extensions of Hebbian Learning: An Essay
Neural Computation
A Complex-Valued RTRL Algorithm for Recurrent Neural Networks
Neural Computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Intelligent beamforming by using a complex-valued neural network
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Engineering applications of Computational Intelligence
An augmented CRTRL for complex-valued recurrent neural networks
Neural Networks
Risk-sensitive loss functions for sparse multi-category classification problems
Information Sciences: an International Journal
No-reference image quality assessment using modified extreme learning machine classifier
Applied Soft Computing
Letters: Fully complex extreme learning machine
Neurocomputing
Channel equalization using adaptive complex radial basis function networks
IEEE Journal on Selected Areas in Communications
Symmetric Complex-Valued RBF Receiver for Multiple-Antenna-Aided Wireless Systems
IEEE Transactions on Neural Networks
A Sequential Learning Algorithm for Complex-Valued Self-Regulating Resource Allocation Network-CSRAN
IEEE Transactions on Neural Networks
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system
Applied Soft Computing
Expert Systems with Applications: An International Journal
Complex-Valued neuro-fuzzy inference system based classifier
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
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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.