123
85.
Tarassenko, I. and S. Roberts.
Supervised and unsupervised learning in radial basis
function classifiers
. 1994: IET.
86.
Hu, Y.H., J.N. Hwang, and S.W. Perry,
Handbook of neural network signal processing.
The Journal of the Acoustical Society of America, 2002. 111: p. 2525.
87.
Moody, J. and C.J. Darken,
Fast learning in networks of locally-tuned processing units.
Neural computation, 1989. 1(2): p. 281-294.
88.
Sanger, T.D.,
Optimal unsupervised learning in a single-layer linear feedforward
neural network.
Neural Networks, 1989. 2(6): p. 459-473.
89.
Honkela, A. and H. Valpola,
Unsupervised variational Bayesian learning of nonlinear
models.
Advances in neural information processing systems, 2005. 17: p. 593
–
600.
90.
Malki, S., L. Spaanenburg, and N. Ray.
Image stream processing on a packet-switched
discrete-time CNN
. 2004.
91.
Yang, Z., Y. Nishio, and A. Ushida.
A Two Layer CNN in Image Processing Applications
.
92.
Chua, L.O. and T. Roska,
The CNN paradigm.
Circuits and Systems I:
Fundamental
Theory and Applications, IEEE Transactions on, 1993. 40(3): p. 147-156.
93.
Matsumoto, T., et al.
Several image processing examples by CNN
: IEEE.
94.
Kozek, T., T. Roska, and L.O. Chua,
Genetic algorithm for CNN template learning.
Circuits and Systems I: Fundamental Theory and Applications,
IEEE Transactions
on, 1993. 40(6): p. 392-402.
95.
Mohamad, S. and K. Gopalsamy,
Exponential stability of continuous-time and
discrete-time cellular neural networks with delays.
Applied
Mathematics and
Computation, 2003. 135(1): p. 17-38.
96.
Harrer, H. and J.A. Nossek,
Discrete time cellular neural networks.
International
Journal of Circuit Theory and Applications, 1992. 20(5): p. 453-467.
97.
Chen, H.C., et al.,
Image-processing algorithms realized by discrete-time cellular
neural networks and their circuit implementations.
Chaos, Solitons & Fractals, 2006.
29(5): p. 1100-1108.
98.
Kawahara, M., T. Inoue, and Y. Nishio.
Image processing application using CNN with
dynamic template
: IEEE.
99.
Zarándy, Á.,
The art of CNN template design.
International Journal of Circuit Theory
and Applications, 1999. 27(1): p. 5-23.
100.
Feiden, D. and R. Tetzlaff,
Cellular neural networks for motion estimation and
obstacle detection.
Advances in Radio Science-Kleinheubacher Berichte. 1.
101.
Feiden, D. and R. Tetzlaff.
Feature extraction in motion estimation with cellular
neural networks using iterative annealing
.
102.
UCAN, O.N., E. BILGILI, and R. COBAN,
Extraction Of Facial Features Using Genetic
Cellular Neural Networks.
network. 1: p. 4.
103.
Wolfram, S.E.W., S. (Ed.),
Theory and Application of Cellular Automata. Reading, MA:
Addison-Wesley, 1986.
104.
Nossek, J.A., et al.,
Cellular neural networks: Theory and circuit design.
International
Journal of Circuit Theory and Applications, 1992. 20(5): p. 533-553.
105.
Mitchell, M.,
An introduction to genetic algorithms
. 1998: The MIT press.
106.
Gacsádi, A., C. Grava, and A. Grava,
