title: A sequential learning algorithm for complex-valued self-regulating resource allocation network-CSRAN.
abstract: This paper presents a sequential learning algorithm for a complex-valued resource allocation network with a self-regulating scheme, referred to as complex-valued self-regulating resource allocation network (CSRAN). The self-regulating scheme in CSRAN decides what to learn, when to learn, and how to learn based on the information present in the training samples. CSRAN is a complex-valued radial basis function network with a sech activation function in the hidden layer. The network parameters are updated using a complex-valued extended Kalman filter algorithm. CSRAN starts with no hidden neuron and builds up an appropriate number of hidden neurons, resulting in a compact structure. Performance of the CSRAN is evaluated using a synthetic complex-valued function approximation problem, two real-world applications consisting of a complex quadrature amplitude modulation channel equalization, and an adaptive beam-forming problem. Since complex-valued neural networks are good decision makers, the decision-making ability of the CSRAN is compared with other complex-valued classifiers and the best performing real-valued classifier using two benchmark unbalanced classification problems from UCI machine learning repository. The approximation and classification results show that the CSRAN outperforms other existing complex-valued learning algorithms available in the literature.
authors.affiliation: School of Computer Engineering, Nanyang Technological University, Singapore. ssundaram@ntu.edu.sg
authors.forename: Sundaram
authors.initials: S
authors.lastname: Suresh
authors.forename: Ramasamy
authors.initials: R
authors.lastname: Savitha
authors.forename: Narasimhan
authors.initials: N
authors.lastname: Sundararajan
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D006801:Humans; D007858:Learning; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology; D012815:Signal Processing, Computer-Assisted