Abstract
A cellular neural network (CNN) with a bipolar stepwise activation function is considered. A comparative analysis of CNN learning algorithms on a given set of binary reference images for various degrees of noise (inversion of randomly selected pixels) and various cell neighborhood sizes is performed. For CNN training a local projection method, which provides much higher noisy images quality filtering than the classical local perceptron learning algorithm, is proposed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Osowski, S.: Neironnye seti dlya obrabotki informatsii (neural networks for information processing). Finansy i statistika, Moskwa (2002). (in Russian)
Hopfield, J.: Neural networks and physical systems with emergent collective computational abilities. Proc. Nat. Acad. Sci. USA 79, 2554–2558 (1982)
Personnaz, L., Guyon, I., Dreyfus, G.: Collective computational properties of neural networks: new learning mechanisms. Phys. Rev. A 34(5), 4217–4228 (1986)
Michel, A.N., Liu, D.: Qualitative analysis and synthesis of recurrent neural networks. Marcel Dekker Inc., New York (2002)
Tarkov, M.S.: Synapses reduction in autoassociative hopfield network. In: Proceedings of the 2017 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON), Novosibirsk, pp. 158–160 (2017). https://ieeexplore.ieee.org/document/8109860
Chua, L.O., Yang, L.: Cellular neural networks: theory and application. IEEE Trans. Circ. Syst. 35(10), 1257–1290 (1988). CAS
Pudov, S.G.: Learning of cellular neural networks. Future Gener. Comput. Syst. 17, 689–697 (2001)
Park, J., Kim, H.-Y., Park, Y., Lee, S.-W.: A synthesis procedure for associative memories based on space-varying cellular neural networks. Neural Netw. 14(1), 107–113 (2001)
Bise, R., Takahashi, N., Nishi, T.: An improvement of the design method of cellular neural networks based on generalized eigenvalue minimization. IEEE Trans. Circuits Syst.-I 50(12), 1569–1574 (2003)
Li, H., Liao, X., Li, C., Huang, H., Li, C.: Edge detection of noisy images based on cellular neural networks. Commun. Nonlinear Sci. Numer. Simul. 16, 3746–3759 (2011)
Duan, S., Hu, X., Dong, Z., Wang, L., Mazumder, P.: Memristor-based cellular nonlinear/neural network: design, analysis, and applications. IEEE Trans. Neural Netw. Learn. Syst. 26(6), 1202–1213 (2015)
Rosenblatt, F.: Principles of Neurodynamics. Spartan, Washington (1959)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Tarkov, M.S. (2019). Noise Filtering in Cellular Neural Networks. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11554. Springer, Cham. https://doi.org/10.1007/978-3-030-22796-8_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-22796-8_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-22795-1
Online ISBN: 978-3-030-22796-8
eBook Packages: Computer ScienceComputer Science (R0)