A Novel Complex-Valued Counterpropagation Network

The counterpropagation network is a combination of competitive network (Kohonen layer) and Grossberg outstar structure. In this paper we have proposed a complex valued representation on conventional forward only counterpropagation network. Many researchers have investigated the computational capabilities of neuron models for real values only. The novel part of the paper is, while considering the complex values equal weightage is given to both the real and imaginary parts. A vectored approach is taken to compute the complex numbers while implementing it with complex valued counterpropagation network (CVCPN). The proposed network is tested on benchmark problem (two spiral problem), Julia´s set, rotational transformations and color image compression. The complex valued counterpropagation network (CVCPN) exhibits less percentage of misclassification and error rate is considerably smaller when compared to the equivalent model in backpropagation network. The learning of intermediate forms of vector classes, manipulation with complex numbers, criterion for winning neuron, and the results of the proposed network with various benchmark and classification problems are discussed