A FULLY COMPLEX-VALUED RADIAL BASIS FUNCTION NETWORK AND ITS LEARNING ALGORITHM

作者：Savitha R^*; Suresh S; Sundararajan N

来源：International Journal of Neural Systems, 2009, 19(4): 253-267.

DOI：10.1142/s0129065709002026

• Radial basis function
• complex-valued activation functions
• k-means clustering
• phase equalization
• adaptive beam-forming

In this paper, a fully complex-valued radial basis FC-RBF) network with a fully complex-valued activation function has been proposed, and its complex-valued gradient descent learning algorithm has been developed. The fully complex activation function, sech(.) of the proposed network, satisfies all the properties needed for a complex-valued activation function and has Gaussian-like characteristics. It maps C(n) -> C, unlike the existing activation functions of complex-valued RBF network that maps C(n) -> R. Since the performance of the complex-RBF network depends on the number of neurons and initialization of network parameters, we propose a K-means clustering based neuron selection and center initialization scheme. First, we present a study on convergence using complex XOR problem. Next, we present a synthetic function approximation problem and the two-spiral classification problem. Finally, we present the results for two practical applications, viz., a non-minimum phase equalization and an adaptive beam-forming problem. The performance of the network was compared with other well-known complex-valued RBF networks available in literature, viz., split-complex CRBF, CMRAN and the CELM. The results indicate that the proposed fully complex-valued network has better convergence, approximation and classification ability.

• 出版日期2009-8
• 单位南阳理工学院