Convergence of the Iterates of Descent Methods for Analytic Cost Functions
SIAM Journal on Optimization
ACE4k: An analog I/O 64×64 visual microprocessor chip with 7-bit analog accuracy: Research Articles
International Journal of Circuit Theory and Applications - CNN Technology
SIAM Journal on Optimization
On global exponential stability of standard and full-range CNNs
International Journal of Circuit Theory and Applications - Cellular Wave Computing Architecture
Extended LaSalle's invariance principle for full-range cellular neural networks
EURASIP Journal on Advances in Signal Processing - CNN technology for spatiotemporal signal processing
Subgradient-based neural networks for nonsmooth nonconvex optimization problems
IEEE Transactions on Neural Networks
Convergence of Neural Networks for Programming Problems via a Nonsmooth Łojasiewicz Inequality
IEEE Transactions on Neural Networks
A study on semiflows generated by cooperative full-range CNNs
International Journal of Circuit Theory and Applications
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This paper considers the Full-range (FR) model of Cellular Neural Networks (CNNs) in the case where the signal range is delimited by an ideal hard-limiter nonlinearity with two vertical segments in the i−v characteristic. A Łojasiewicz inequality around any equilibrium point, for a FRCNN with a symmetric interconnection matrix, is proved. It is also shown that the Łojasiewicz exponent is equal to **image**. The main consequence is that any forward solution of a symmetric FRCNN has finite length and is exponentially convergent toward an equilibrium point, even in degenerate situations where the FRCNN possesses non-isolated equilibrium points. The obtained results are shown to improve the previous results in literature on convergence or almost convergence of symmetric FRCNNs. Copyright © 2010 John Wiley & Sons, Ltd.