Discrete-time recurrent neural networks with complex-valued linear threshold neurons
IEEE Transactions on Circuits and Systems II: Express Briefs
A class of discrete-time recurrent neural networks with multivalued neurons
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Complex-valued neurons with phase-dependent activation functions
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
Periodic activation function and a modified learning algorithm for the multivalued neuron
IEEE Transactions on Neural Networks
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A universal binary neuron (UBN) operates with complex-valued weights and a complex-valued activation function, which is the function of the argument of the weighted sum. The activation function of the UBN separates a whole complex plane onto equal sectors, where the activation function is equal to either 1 or −1 depending on the sector parity (even or odd, respectively). Thus, the UBN output is determined by the argument of the weighted sum. This makes it possible the implementation of the nonlinearly separable (non-threshold) Boolean functions on a single neuron. Hence, the functionality of UBN is incompatibly higher than the functionality of the traditional perceptron. In this paper, we will consider a new modified learning algorithm for the UBN. We will show that classical nonlinearly separable problems XOR and Parity n can be easily solved using a single UBN, without any network. Finally, it will be considered how some other important nonlinearly separable problems may be solved using a single UBN.