An Analysis of the Fundamental Structure of Complex-Valued Neurons
Neural Processing Letters
Approximation by fully complex multilayer perceptrons
Neural Computation
Biophysics of Computation: Information Processing in Single Neurons (Computational Neuroscience Series)
Neural Computation
On the complex backpropagation algorithm
IEEE Transactions on Signal Processing
The complex backpropagation algorithm
IEEE Transactions on Signal Processing
IEEE Transactions on Neural Networks
An information criterion for optimal neural network selection
IEEE Transactions on Neural Networks
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The key element of neurocomputing research in complex domain is the development of artificial neuron model with improved computational power and generalization ability. The non-linear activities in neuronal interactions are observed in biological neurons. This paper presents architecture of a neuron with a non-linear aggregation function for complex-valued signals. The proposed aggregation function is conceptually based on generalized mean of signals impinging on a neuron. This function is general enough and is capable of realizing various conventional aggregation functions as its special case. The generalized-mean neuron has a simpler structure and variation in the value of generalization parameter embraces higher order structure of a neuron. Hence, it can be used without the hassles of possible combinatorial explosion, as in higher order neurons. The superiority of proposed neuron based network over real and complex multilayer perceptron is demonstrated through variety of experiments.