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 neural networks: the merits and their origins
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Blind and semi-blind deblurring of natural images
IEEE Transactions on Image Processing
Complex-valued neurons with phase-dependent activation functions
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
Recent progress in applications of complex-valued neural networks
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
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
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Optimization of image processing techniques using neural networks: a review
WSEAS Transactions on Information Science and Applications
A new multi-valued neural network for the extraction of lumped models of analog circuits
Analog Integrated Circuits and Signal Processing
System identification and modelling based on a double modified multi-valued neural network
Analog Integrated Circuits and Signal Processing
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A multilayer neural network based on multivalued neurons (MLMVN) is a neural network with a traditional feedforward architecture. At the same time, this network has a number of specific different features. Its backpropagation learning algorithm is derivative-free. The functionality of MLMVN is superior to that of the traditional feedforward neural networks and of a variety kernel-based networks. Its higher flexibility and faster adaptation to the target mapping enables to model complex problems using simpler networks. In this paper, the MLMVN is used to identify both type and parameters of the point spread function, whose precise identification is of crucial importance for the image deblurring. The simulation results show the high efficiency of the proposed approach. It is confirmed that the MLMVN is a powerful tool for solving classification problems, especially multiclass ones.