Complex domain backpropagation 论文

1992IEEE Transactions on Circuits and Systems II Analog and Digital Signal Processing引用 342

Neural Networks and ApplicationsNeural Networks and Reservoir ComputingBlind Source Separation Techniques

人工智能 Neural Networks and Applications Blind Source Separation Techniques Neural Networks and Reservoir Computing

作者

摘要

The backpropagation algorithm is extended to complex domain backpropagation (CDBP) which can be used to train neural networks for which the inputs, weights, activation functions, and outputs are complex-valued. Previous derivations of CDBP were necessarily admitting activation functions that have singularities, which is highly undesirable. In the derivation, CDBP is derived so that that it accommodates classes of suitable activation functions. One such function is found and the circuit implementation of the corresponding neuron is given. CDBP hardware circuits can be used to process sinusoidal signals all at the same frequency (phasors).< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>

作者查看全部 (2)

Cris Koutsougeras

George M. Georgiou

Complex domain backpropagation 论文

详细信息

摘要

作者查看全部 (2)

相关技术查看全部 (2)

相关事件

相关文章