The nature of statistical learning theory
The nature of statistical learning theory
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications
Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications
Image Processing Using Cellular Neural Networks Based on Multi-Valued and Universal Binary Neurons
Journal of VLSI Signal Processing Systems
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory
Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Complex-Valued Neural Networks (Studies in Computational Intelligence)
Complex-Valued Neural Networks (Studies in Computational Intelligence)
Solving the XOR and parity N problems using a single universal binary neuron
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on BISCSE 2005 " Forging the Frontiers" Part II
SVM Approach to Breast Cancer Classification
IMSCCS '07 Proceedings of the Second International Multi-Symposiums on Computer and Computational Sciences
Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models
Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models
Complex-valued multistate neural associative memory
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A new design method for the complex-valued multistate Hopfield associative memory
IEEE Transactions on Neural Networks
Blur Identification by Multilayer Neural Network Based on Multivalued Neurons
IEEE Transactions on Neural Networks
Multi-valued neurons: hebbian and error-correction learning
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
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In this paper, we consider a new periodic activation function for the multivalued neuron (MVN). The MVN is a neuron with complex-valued weights and inputs/output, which are located on the unit circle. Although the MVN outperforms many other neurons and MVN-based neural networks have shown their high potential, the MVN still has a limited capability of learning highly nonlinear functions. A periodic activation function, which is introduced in this paper, makes it possible to learn nonlinearly separable problems and nonthreshold multiple-valued functions using a single multivalued neuron. We call this neuron a multivalued neuron with a periodic activation function (MVN-P). The MVN-Ps functionality is much higher than that of the regular MVN. The MVN-P is more efficient in solving various classification problems. A learning algorithm based on the error-correction rule for the MVN-P is also presented. It is shown that a single MVN-P can easily learn and solve those benchmark classification problems that were considered unsolvable using a single neuron. It is also shown that a universal binary neuron, which can learn nonlinearly separable Boolean functions, and a regular MVN are particular cases of the MVN-P.