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Any information processing platform that allows the construction of arbitrary [[Boolean function|Boolean functions]] is called universal, and as result, this class CNN processors are commonly referred to as universal CNN processors. The original CNN processors can only perform linearly separable Boolean functions. By translating functions from digital logic or look-up table domains into the CNN ___domain, some functions can be considerably simplified. For example, the nine-bit, odd parity generation logic, which is typically implemented by eight nested exclusive-or gates, can also be represented by a sum function and four nested absolute value functions. Not only is there a reduction in the function complexity, but the CNN implementation parameters can be represented in the continuous, real-number ___domain.<ref name=":1" />
There are two methods by which to select a CNN processor along with a template or weights. The first is by synthesis, which involves determine the coefficients offline. This can be done by leveraging previous work, i.e. libraries, papers, and articles, or by mathematically deriving co that best suits the problem. The other is through training the processor. Researchers have used [[back-propagation]] and [[genetic algorithms]] to learn and perform functions. Back-propagation algorithms tend to be faster, but genetic algorithms are useful because they provide a mechanism to find a solution in a discontinuous, noisy search space.<ref>T. Kozek, T. Roska, and L. Chua, "Genetic Algorithms for CNN Template Learning," IEEE Trans. on Circuits and Systems I, 40(6):392-402, 1993.</ref><ref>G. Pazienza, E. Gomez-Ramirezt and X. Vilasis-Cardona, "Genetic Programming for the CNN-UM", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref><ref>E. Lopez, M. Balsif, D. Vilarilio and D. Cabello, "Design and Training of Multilayer Discrete Time Cellular Neural Networks for Antipersonnel Mine Detection Using Genetic Algorithms", Int’l Workshop on Cellular Neural Networks and Their Applications, 2000.</ref>
==Physical implementations==
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*R. Dogaru and L. O. Chua, "Emergence of Unicellular Organisms from a Simple Generalized Cellular Automata", Int’l Journal of Bifurcations and Chaos, 9(6):1219-1236, 1999.
*T. Yang, L. Chua, "Implementing Back-Propagation-Through-Time Learning Algorithm Using Cellular Neural Networks", Int’l Journal of Bifurcations and Chaos, 9(6):1041-1074, 1999.
*J. Nossek, G. Seiler, T. Roska, and L. Chua, "Cellular Neural Networks: Theory and Circuit Design," Int’l Journal of Circuit Theory and Applications, 20: 533-553, 1998.
*A. Zarandry, S. Espejo, P. Foldesy, L. Kek, G. Linan, C. Rekeczky, A. Rodriguez-Vazquez, T. Roska, I. Szatmari, T. Sziranyi and P. Szolgay, "CNN Technology in Action ", Int’l Workshop on Cellular Neural Networks and Their Applications, 2000.
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*G Costantini, D. Casafi., and R. Perfetti, "A New CNN-based Method for Detection of the Axis of Symmetry.", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.
*C. Amenta, P. Arena, S. Baglio, L. Fortuna, D. Richiura, M.Xibilia and L. Vu1, "SC-CNNs for Sensors Data Fusion and Control in Space Distributed Structures", Int’l Workshop on Cellular Neural Networks and Their Applications, 2000.
*E. Bilgili, O. Ucan, A. Albora and I. Goknar, "Potential Anomaly Separation Using Genetically Trained Cellular Neural Networks", Int’l Workshop on Cellular Neural Networks and Their Applications, 2002.
*C. Rekeczky and G. Timar "Multiple Laser Dot Detection and Localization within an Attention Driven Sensor Fusion Framework", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.
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