Cellular Neural Networks: Analysis, Design and Optimization
Cellular Neural Networks: Analysis, Design and Optimization
Parameter Configurations for Hole Extraction in Cellular Neural Networks (CNN)
Analog Integrated Circuits and Signal Processing
An area efficient implementation of a cellular neural network
ANNES '95 Proceedings of the 2nd New Zealand Two-Stream International Conference on Artificial Neural Networks and Expert Systems
Implementation of Time-Multiplexed CNN Building Block Cell
MICRONEURO '96 Proceedings of the 5th International Conference on Microelectronics for Neural Networks and Fuzzy Systems
Digital Image Processing Using MATLAB
Digital Image Processing Using MATLAB
CNN based Hole filler template design using numerical integration techniques
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Investigation on time-multiplexing cellular neural network simulation by RKAHeM(4,4) technique
International Journal of Advanced Intelligence Paradigms
| Hi-index | 0.00 |
The construction of two novel integration algorithms are proposed by formulating Runge-Kutta embedded techniques based on geometric mean (GM) coupled with contra-harmonic mean and harmonic mean with error control under general cellular nonlinear network paradigm. This paper attempts to analyze the performance of hole-filling cellular nonlinear network arrays through potential behaviour of newly proposed versatile algorithm. A promising simulation result shows that more quantitative analysis has been carried out to clearly visualize the goodness and robustness of the proposed embedded algorithms for hole-filler.