Complex generalized-mean neuron model and its applications
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.
complex generalized-mean neuron complex multilayer perceptron complex back-propagation conformal mapping