Sequential Data Fusion via Vector Spaces: Fusion of Heterogeneous Data in the Complex Domain
Journal of VLSI Signal Processing Systems
A novel adaptive nonlinear filter-based pipelined feed-forward second-order Volterra architecture
IEEE Transactions on Signal Processing
A novel adaptive bilinear filter based on pipelined architecture
Digital Signal Processing
Complex-valued neural networks: the merits and their origins
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Complex valued recurrent neural networks for noncircular complex signals
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Recent progress in applications of complex-valued neural networks
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
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A complex-valued pipelined recurrent neural network (CPRNN) for nonlinear adaptive prediction of complex nonlinear and nonstationary signals is introduced. This architecture represents an extension of the recently proposed real-valued PRNN of Haykin and Li in 1995. To train the CPRNN, a complex-valued real time recurrent learning (CRTRL) algorithm is first derived for a single recurrent neural network (RNN). This algorithm is shown to be generic and applicable to general signals that have complex domain representations. The CRTRL is then extended to suit the modularity of the CPRNN architecture. Further, to cater to the possibly large dynamics of the input signals, a gradient adaptive amplitude of the nonlinearity within the neurons is introduced to give the adaptive amplitude CRTRL (AACRTRL). A comprehensive analysis of the architecture and associated learning algorithms is undertaken, including the role of the number of nested modules, number of neurons within the modules, and input memory of the CPRNN. Simulations on real-world and synthetic complex data support the proposed architecture and algorithms.