Qiao Changge. LEARNING ALGORITHM FOR THE DESIGN OF DIGITAL CELLULAR NEURAL NETWORK[J]. Journal of Electronics & Information Technology, 1997, 19(3): 403-406.
Citation:
Qiao Changge. LEARNING ALGORITHM FOR THE DESIGN OF DIGITAL CELLULAR NEURAL NETWORK[J]. Journal of Electronics & Information Technology, 1997, 19(3): 403-406.
Qiao Changge. LEARNING ALGORITHM FOR THE DESIGN OF DIGITAL CELLULAR NEURAL NETWORK[J]. Journal of Electronics & Information Technology, 1997, 19(3): 403-406.
Citation:
Qiao Changge. LEARNING ALGORITHM FOR THE DESIGN OF DIGITAL CELLULAR NEURAL NETWORK[J]. Journal of Electronics & Information Technology, 1997, 19(3): 403-406.
The application of cellular neural networks is determined by its templates. In accordance with the application of digital cellular neural network the author proposed before, a method for the design of its templates, i. e., inequalities construcion method, is given and a supervised learning algorithm is proposed based on the relaxation method. The learning algorithm provides theoretical basis for the design of DCNN. Simulation results on examples such as connected component detection shows the effectiveness and feasibility of the algorithm.
Chua L O, Yang L. Cellular neural networks:Theory and applications. IEEE Trans. on CAS, 1988, 35(10): 1257-1290.[2]Chua L O, Thiran P. An Analytic method for designing simple CNNs. IEEE Trans. on CAS, 1991, 38(11): 1332-1341.[3]乔长阁.数字式细胞神经网络理论与应用研究:[博士论文].西安:西北工业大学,1993.[4]Matsumoto T, et al. CNN cloning template:Connected component detector. IEEE Trans. on CAS, 1990, 37(5):633-635.[5]Motzkin T S, Schoenberg L J. The Relaxation method for linear inequalities[J].Canada Journal of Mathematics.1954, 6(3):393-404
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