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
Complex-Valued Neural Networks: Theories and Applications (Series on Innovative Intelligence, 5)
Complex-Valued Neural Networks: Theories and Applications (Series on Innovative Intelligence, 5)
A Complex-Valued RTRL Algorithm for Recurrent Neural Networks
Neural Computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
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
Complex-valued multistate neural associative memory
IEEE Transactions on Neural Networks
A new design method for the complex-valued multistate Hopfield associative memory
IEEE Transactions on Neural Networks
Nonlinear discrete time neural network observer
Neurocomputing
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This paper discusses a class of discrete time recurrent neural networks with multivalued neurons (MVN), which have complex-valued weights and an activation function defined as a function of the argument of a weighted sum. Complementing state-of-the-art of such networks, our research focuses on the convergence analysis of the networks in synchronous update mode. Two related theorems are presented and simulation results are used to illustrate the theory.