Performance Study on Complex-valued Function Approximation Problems
In this chapter, we evaluate the approximation performances of the fully complex valued multi-layer perceptron network and the improved fully complex-valued multi-layer perceptron network described in Chapter 2, the fully complex-valued radial basis function network and the meta-cognitive fully complex-valued radial basis function network described in Chapter 3, and the fast learning fully complex valued relaxation network described in Chapter 4. The performances of these networks are studied in comparison with existing complex-valued learning algorithms like the complex-valued extreme learning machine and the complex-valued minimal resource allocation network using two synthetic, complex-valued function approximation problems and two real-world problems. The real world problems consist of a Quadrature Amplitude Modulation (QAM) channel equalization problem with circular signals and an adaptive beam-forming problem with non-circular signals.
KeywordsAntenna Array Hide Neuron Radial Basis Function Network Quadrature Amplitude Modulation Transmitted Symbol
Unable to display preview. Download preview PDF.
- 1.Proakis, J.G., Salehi, M.: Digital Communication. McGraw-Hill Higher Education, New York (2008)Google Scholar
- 18.Suksmono, A.B., Hirose, A.: Intelligent beamforming by using a complex-valued neural network. Journal of Intelligent and Fuzzy Systems 15(3-4), 139–147 (2004)Google Scholar
- 21.Song, X., Wang, J., Niu, X.: Robust adaptive beamforming algorithm based on neural network. In: IEEE International Conference on Automation and Logistics (ICAL 2008), pp. 1844–1849 (2008)Google Scholar
- 24.Savitha, R., Suresh, S., Sundararajan, N., Saratchandran, P.: Complex-valued function approximation using an improved BP learning algorithm for feed-forward networks. In: IEEE International Joint Conference on Neural Networks (IJCNN 2008), June 1-8, pp. 2251–2258 (2008)Google Scholar
- 25.Savitha, R., Suresh, S., Sundararajan, N.: Complex-valued function approximation using a fully complex-valued RBF (FC-RBF) learning algorithm. In: International Joint Conference on Neural Networks (IJCNN 2009), pp. 2819–2825 (2009)Google Scholar
- 27.Monzingo, R.A., Miller, T.W.: Introduction to Adaptive Arrays. SciTech. Publishing, Raleigh (2004)Google Scholar