title: Projection-based fast learning fully complex-valued relaxation neural network.
abstract: This paper presents a fully complex-valued relaxation network (FCRN) with its projection-based learning algorithm. The FCRN is a single hidden layer network with a Gaussian-like sech activation function in the hidden layer and an exponential activation function in the output layer. For a given number of hidden neurons, the input weights are assigned randomly and the output weights are estimated by minimizing a nonlinear logarithmic function (called as an energy function) which explicitly contains both the magnitude and phase errors. A projection-based learning algorithm determines the optimal output weights corresponding to the minima of the energy function by converting the nonlinear programming problem into that of solving a set of simultaneous linear algebraic equations. The resultant FCRN approximates the desired output more accurately with a lower computational effort. The classification ability of FCRN is evaluated using a set of real-valued benchmark classification problems from the University of California, Irvine machine learning repository. Here, a circular transformation is used to transform the real-valued input features to the complex domain. Next, the FCRN is used to solve three practical problems: a quadrature amplitude modulation channel equalization, an adaptive beamforming, and a mammogram classification. Performance results from this paper clearly indicate the superior classification/approximation performance of the FCRN.
authors.forename: Ramasamy
authors.initials: R
authors.lastname: Savitha
authors.forename: Sundaram
authors.initials: S
authors.lastname: Suresh
authors.forename: Narasimhan
authors.initials: N
authors.lastname: Sundararajan
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence / Q000639:trends; D016571:Neural Networks, Computer; D013997:Time Factors