Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Introductory Digital Image Processing: A Remote Sensing Perspective
Introductory Digital Image Processing: A Remote Sensing Perspective
Applications of Optical Fourier Transforms
Applications of Optical Fourier Transforms
Binary Polynomial and Nonlinear Digital Filters
Binary Polynomial and Nonlinear Digital Filters
Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications
Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications
Cellular Neural Networks
Color image processing in a cellular neural-network environment
IEEE Transactions on Neural Networks
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
Periodic activation function and a modified learning algorithm for the multivalued neuron
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Multi-valued and universal binary neurons (MVN and UBN) are the neural processing elements with the complex-valued weights and high functionality. It is possible to implement an arbitrary mapping described by partially defined multiple-valued function on the single MVN. An arbitrary mapping described by partially defined or fully defined Boolean function, which can be non-threshold, may be implemented on the single UBN. The quickly converging learning algorithms exist for both types of neurons. Such features of the MVN and UBN may be used for solving the different problems. One of the most successful applications of the MVN and UBN is their usage as basic neurons in the Cellular Neural Networks (CNN). It opens the new effective opportunities in nonlinear image filtering and its applications to noise reduction, edge detection and solving of the super resolution problem. A number of experimental results are presented to illustrate the performance of the proposed algorithms.