Multilayer feedforward networks are universal approximators
Neural Networks
Natural gradient works efficiently in learning
Neural Computation
Matrix analysis and applied linear algebra
Matrix analysis and applied linear algebra
Fully Complex Multi-Layer Perceptron Network for Nonlinear Signal Processing
Journal of VLSI Signal Processing Systems
Approximation by fully complex multilayer perceptrons
Neural Computation
Complex independent component analysis of frequency-domain electroencephalographic data
Neural Networks - Special issue: Neuroinformatics
IEEE Transactions on Signal Processing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Optimal output back-off in OFDM systems with nonlinear power amplifiers
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Algorithms for complex ML ICA and their stability analysis using wirtinger calculus
IEEE Transactions on Signal Processing
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
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We describe a framework based on Wirtinger calculus for adaptive signal processing that enables efficient derivation of algorithms by directly working in the complex domain and taking full advantage of the power of complex-domain nonlinear processing. We establish the basic relationships for optimization in the complex domain and the real-domain equivalences for first-and second-order derivatives by extending the work of Brandwood and van den Bos. Examples in the derivation of first-and second-order update rules are given to demonstrate the versatility of the approach.