Вікіпедія

Клітинні нейронні мережі

Кліти́нні нейро́нні мере́жі (КНМ, англ. Cellular neural networks (CNN)) — обчислювальні мережі, схожі за функціонуванням до штучних нейронних мереж, але відрізняються тим, що взаємодії відбуваються лише між сусідніми одиницями. КНМ застосовують для обробки зображень, аналізу 3D поверхонь, обчислення диференційних рівнянь в часткових похідних, приведення негеометричних завдань до геометричних карт, моделювання зору та інших сенсомоторних функцій.

Архітектура КНМ ред.

В зв'язку з великою чисельністю різних архітектур важко дати точне визначення для клітинної нейронної мережі. З точки зору архітектури мереж, КНМ — це система скінченних, чисельно фіксованих, топологічно-фіксованих, локально з'єднаних нелінійних обчислювальних одиниць, що мають багато входів і лише один вихід. Нелінійні обчислювальні одиниці часто називаються нейронами або клітинами.

Математично кожна клітина може бути змодельована як дисипативна, нелінійна динамічна система, в якій інформація закодована через її початковий стан, входи та інші змінні, які визначають її поведінку.

Якщо динаміка станів клітин є неперервною в часі — тоді говорять про Неперервні КНМ (англ. Continuous-Time CNN).

Якщо динаміка є дискретною в часі, то говорять про Дискретні КНМ (англ. Discrete-Time CNN). Кожна клітина має один вихід, за допомогою якого вона передає інформацію про свій стан з іншими клітинами та зовнішніми пристроями. Вихідний сигнал є зазвичай дійсним числом, проте може бути комплексним, або навіть кватерніоном в Багатозмінній КНМ (англ. Multi-Valued CNN).

Зазвичай нейрони (клітини) мережі є однаковим, але в деяких випадках застосовують різні типи (в Неоднорідних КНМ, англ. Non-Uniform Processor CNN). В оригінальній моделі Чуа (англ. Chua) і Янга (англ. Yang) — Chua-Yang CNN[1]стан клітин був ваговою сумою вхідних сигналів, і вихід був кусочно-лінійною функцією. Однак, як і Перцептронні моделі штучних нейронних мереж, ця модель мала обмежені корисні властивості. Наприклад, вона не була здатною моделювати нелінійні функції, наприклад XOR.

Сучасні нелійні КНМ здатні вирішити задачі моделювання нелійних функцій. Клітини зазвичай визначаються в двовимірному просторі з евклідовою геометрією, з розташуванням у вигляді квадратної сітки. Інколи клітини визначаються в багатовимірному просторі з трикутним, гексагональним або іншим просторово-інваріантним розміщенням. Топологічно клітини можуть розміщуватись на кінцевій площині або на тороїдальній площині. Клітини з'єднані локальними зв'язками. Це означає, що усі зв'язки однієї клітини знаходяться в межах певного радіусу (відстань вимірюється топологічно).

Зв'язки також можуть бути із затримкою в часі для того щоб працювати з даними в часі. Більшість архітектур КНМ мають клітини з однаковою коннективністю, однак є приклади застосування нейронних мереж, в яких необхідна просторово варіантна топологія (коннективність різна в різних ділянках КНМ). Можуть також застосовуватись багатошарові КНМ (англ. Multiple-Layer CNN (ML-CNN)), де клітини одного шару є ідентичними, для збільшення обчислювальних можливостей КНМ.

Не зважаючи на локальну коннективність клітинних нейронних мереж, обмін інформацією між віддаленими ділянками нейронної мережі може відбуватись шляхом дифузії. КНМ являють собою набір незалежних, взаємодіючих структур, що формують інтегроване ціле. Складність поведінки КНМ більша, ніж складність поведінки окремих клітин (нейронів). Тобто, клітинні нейронні мережі володіють емерджентними властивостями.

Література ред.

  1. ↑ L. Chua and L. Yang, "Cellular Neural Networks: Theory, " IEEE Trans. on Circuits and Systems, 35(10):1257-1272, 1988. http://nonlinear.eecs.berkeley.edu/raptor/CNNs/CellularNeuralNetworks-Theory.pdf [Архівовано 13 квітня 2021 у Wayback Machine.]
  1. L. Chua and L. Yang, "Cellular Neural Networks: Theory, " IEEE Trans. on Circuits and Systems, 35(10):1257-1272, 1988. [1] [Архівовано 13 квітня 2021 у Wayback Machine.]
  2. L. Chua and L. Yang, «Cellular Neural Networks: Applications» IEEE Trans. on Circuits and Systems, 35(10):1273:1290, 1988.
  3. T. Roska, L. Chua, «The CNN Universal Machine: An Analogic Array Computer», IEEE Trans. on Circuits and Systems-II, 40(3): 163–172, 1993.
  4. V. Cimagalli, M. Balsi, «Cellular Neural Networks: A Review», Neural Nets WIRN Vierti, 1993.
  5. L. Chua, T. Roska, Cellular Neural Networks and Visual Computing: Foundations and Applications, 2005.
  6. I. Szatmari, P. Foldesy, C. Rekeczky and A. Zarandy, «Image Processing Library for the Aladdin Computer», Int'l Workshop on Cellular Neural Networks and Their Applications, 2005.
  7. H. Harrer and J.Nossek, «Discrete-Time Cellular Neural Networks», Int'l Journal of Circuit Theory and Applications, 20:453-467, 1992.
  8. T. Roska and L. Chua, «Cellular Neural Networks with Non-Linear and Delay-Type Template Elements and Non-Uniform Grids», Int'l Journal of Circuit Theory and Applications, 20:469-481, 1992.
  9. S. Majorana and L. Chua, «A Unified Framework for Multilayer High Order CNN», Int'l Journal of Circuit Theory and Applications, 26:567-592, 1998.
  10. C. Wu and Y. Wu, «The Design of CMOS Non-Self-Feedback Ratio Memory Cellular Nonlinear Network without Elapsed Operation for Pattern Learning and Recognition», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  11. D. Balya, G, Tímar, G. Cserey, and T. Roska, «A New Computational Model for CNN-UMs

and its Computational Complexity», Int'l Workshop on Cellular Neural Networks and Their Applications, 2004.

  1. M. Yalcin, J. Suykens, and J. Vandewalle, Cellular Neural Networks, Multi-Scroll Chaos And Synchronization, 2005.
  2. K. Yokosawa, Y. Tanji and M. Tanaka, «CNN with Multi-Level Hysteresis Quantization Output» Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  3. T. Nakaguchi, K. Omiya and M. Tanaka, «Hysteresis Cellular Neural Networks for Solving Combinatorial Optimization Problems», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  4. K. Crounse, C. Wee and L. Chua, «Linear Spatial Filter Design for Implementation on the CNN Universal Machine», Int'l Workshop on Cellular Neural Networks and Their Applications, 2000.
  5. H. Ip, E. Drakakis, and A. Bharath, «Towards Analog VLSI Arrays for Nonseparable 3D Spatiotemporal Filtering», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  6. M. Brugge, «Morphological Design of Discrete−Time Cellular Neural Networks», University of Groningen Dissertation, 2005.
  7. J. Poikonen1 and A. Paasio, «Mismatch-Tolerant Asynchronous Grayscale Morphological Reconstruction», Int'l Workshop on Cellular Neural Networks and Their Applications, 2005.
  8. M. Gilli, T. Roska, L. Chua, and P. Civalleri, «CNN Dynamics Represents a Broader Range Class than PDEs», Int'l Journal of Bifurcations and Chaos, 12(10):2051-2068, 2002.
  9. A. Adamatzky, B. Costello, T Asai «Reaction-Diffusion Computers», 2005.
  10. F. Gollas and R. Tetzlaff, «Modeling Complex Systems by Reaction-Diffusion Cellular Nonlinear Networks with Polynomial Weight-Functions», Int'l Workshop on Cellular Neural Networks and Their Applications, 2005.
  11. A. Selikhov, «mL-CNN: A CNN Model for Reaction Diffusion Processes in m Component Systems», Int'l Workshop on Cellular Neural Networks and Their Applications, 2005.
  12. B. Shi and T. Luo, «Spatial Pattern Formation via Reaction-Diffusion Dynamics in 32x32x4 CNN Chip», IEEE Trans. On Circuits And Systems-I, 51(5):939-947, 2004.
  13. E. Gomez-Ramirez, G. Pazienza, X. Vilasis-Cardona, «Polynomial Discrete Time Cellular Neural Networks to solve the XOR Problem», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  14. F. Chen, G. He, X. Xu1, and G. Chen, «Implementation of Arbitrary Boolean Functions via CNN», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  15. R. Doguru and L. Chua, «CNN Genes for One-Dimensional Cellular Automata: A Multi-Nested Piecewise-Linear Approach», Int'l Journal of Bifurcation and Chaos, 8(10):1987-2001, 1998.
  16. R. Dogaru and L. Chua, «Universal CNN Cells», Int'l Journal of Bifurcations and Chaos, 9(1):1-48, 1999.
  17. 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.
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. K. Wiehler, M. Perezowsky, R. Grigat, «A Detailed Analysis of Different CNN Implementations for a Real-Time Image Processing System», Int'l Workshop on Cellular Neural Networks and Their Applications, 2000.
  23. 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.
  24. L. Chua, L. Yang, and K. R. Krieg, «Signal Processing Using Cellular Neural Networks», Journal of VLSI Signal Processing, 3:25-51, 1991.
  25. T. Roska, L. Chua, «The CNN Universal Machine: An Analogic Array Computer», IEEE Trans. on Circuits and Systems-II, 40(3): 163–172, 1993.
  26. T. Roska and A. Rodriguez-Vazquez, «Review of CMOS Implementations of the CNN Universal Machine-Type Visual Microprocessors», International Symposium on Circuits and Systems, 2000
  27. A. Rodríguez-Vázquez, G. Liñán-Cembrano, L. Carranza, E. Roca-Moreno, R. Carmona-Galán, F. Jiménez-Garrido, R. Domínguez-Castro, and S. Meana, "ACE16k: The Third Generation of Mixed-Signal SIMD-CNN ACE Chips Toward VSoCs, " IEEE Trans. on Circuits and Systems — I, 51(5): 851–863, 2004.
  28. T. Roska, «Cellular Wave Computers and CNN Technology — a SoC architecture with xK Processors and Sensor Arrays», Int'l Conference on Computer Aided Design Accepted Paper, 2005.
  29. K. Karahaliloglu, P. Gans, N. Schemm, and S. Balkir, «Optical sensor integrated CNN for Real-time Computational Applications», IEEE Int'l Symposium on Circuits and Systems, pp. 21-24, 2006.
  30. C. Dominguez-Matas, R. Carmona-Galan, F. Sanchez-Fernaindez, J. Cuadri, and A. Rodriguez-Vaizquez, «A Bio-Inspired Vision Front-End Chip with Spatio-Temporal Processing and Adaptive Image Capture», Int'l Workshop on Computer Architecture for Machine Perception and Sensing, 2006.
  31. C. Dominguez-Matas, R. Carmona-Galan, F. Sainchez-Fernaindez, A. Rodriguez-Vazquez, «3-Layer CNN Chip for Focal-Plane Complex Dynamics with Adaptive Image Capture», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  32. I. Szatmari,, P. Foldesy, C. Rekeczky and A. Zarandy, «Image processing library for the Aladdin Visual Computer», Int'l Workshop on Cellular Neural Networks and Their Applications, 2002.
  33. A. Zarandy and C. Rekeczky, «Bi-i: a standalone ultra high speed cellular vision system», IEEE Circuits and Systems Magazine, 5(2):36-45, 2005.
  34. T. Roska, D. Balya, A. Lazar, K. Karacs, R. Wagner and M. Szuhaj, «System Aspects of a Bionic Eyeglass», IEEE Int'l Symposium on Circuits and Systems, 2006.
  35. K. Karacst and T. Roskatt, «Route Number Recognition of Public Transport Vehicles via the Bionic Eyeglass», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  36. R. Wagner and M. Szuhajt, «Color Processing in Wearable Bionic Glasses»
  37. P.Arena, L. Fortuna, M. Frasca, L. Patane, and M. Pollino, «An Autonomous Mini-Hexapod Robot Controller through a CNN Based VLSI Chip», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  38. C. Wu and C. Cheng, «The Design of Cellular Neural Network with Ratio Memory for Pattern Learning and Recognition», Int'l Workshop on Cellular Neural Networks and Their Applications, 2000.
  39. W. Yen, R. Chen and J. Lai, «Design of Min/Max Cellular Neural Networks in CMOS Technology», Int'l Workshop on Cellular Neural Networks and Their Applications, 2002.
  40. Z. Gallias and M. Ogorzalek, «Influence in System Nonuniformity on Dynamic Phenomenon in Arrays of Coupled Nonlinear Networks», Int'l Workshop on Cellular Neural Networks and Their Applications, 2002
  41. S. Xavier-de-Souza, M. Yalcın, J. Suykens, and J. Vandewalle, «Toward CNN Chip-Specific Robustness», IEEE Trans. On Circuits And Systems — I, 51(5): 892–902, 2004.
  42. D. Hillier, S. Xavier de Souza, J. Suykens, J. Vandewalle, «CNNOPT Learning CNN Dynamics and Chip-specific Robustness», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  43. A. Paasiot and J. Poilkonent, «Programmable Diital Nested CNN», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  44. M. Znggi, R. Dogaru, and L. Chua, «Physical Modeling of RTD-Based CNN Cells», Int'l Workshop on Cellular Neural Networks and Their Applications, 2000.
  45. W. Yen and C. Wu, «The Design of Neuron-Bipolar Junction Transistor (vBJT) Cellular Neural Network(CNN) Structure with Multi-Neighborhood-Layer Template», Int'l Workshop on Cellular Neural Networks and Their Applications, 2000.
  46. F. Sargeni, V. Bonaiuto and M. Bonifazi, «Multiplexed Star-CNN Architecture», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  47. Z. Kincsest, Z. Nagyl, and P. Szolgay, «Implementation of Nonlinear Template Runner Emulated Digital CNN-UM on FPGA», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  48. W. Fangt, C. Wang and L. Spaanenburg, «In Search of a Robust Digital CNN System» Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  49. Z. Voroshazit, Z. Nagyt, A. Kiss and P. Szolgay, «An Embedded CNN-UM Global Analogic Programming Unit Implementation on FPGA», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  50. Eutecus Homepage (https://web.archive.org/web/20100306164810/http://www.eutecus.com/).
  51. A. Loncar, R. Kunz and R. Tetzaff, «SCNN 2000 — Part I: Basic Structures and Features of the Simulation System for Cellular Neural Networks», Int'l Workshop on Cellular Neural Networks and Their Applications, 2000.
  52. V. Tavsanoglu, «Jacobi's Iterative Method for Solving Linear Equations and the Simulation of Linear CNN», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  53. B. Shi, «Estimating the Steady State using Forward and Backward Recursions», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  54. S. Tokes, L. Orzo, and A. Ayoub, «Programmable OASLM as a Novel Sensing Cellular Computer», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  55. W. Porod, F. Werblin, L. Chua, T. Roska, A. Rodriguez-Vázquez, B. Roska, R. Faya, G. Bernstein, Y. Huang, and A. Csurgay, «Bio-Inspired Nano-Sensor-Enhanced CNN Visual Computer», Annals of the New York Academy of Sciences, 1013: 92-109, 2004.
  56. J. Flak, M. Laiho1, and K Halonen, «Programmable CNN Cell Based on SET Transistors», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  57. 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.
  58. L. Chua, S. Yoon and R. Dogaru, "A Nonlinear Dynamics Perspective of Wolfram's New Kind of Science. Part I: Threshold of Complexity, " Int'l Journal of Bifurcation and Chaos, 12(12):2655-2766, 2002.
  59. O. Lahdenoja, M. Laiho and A. Paasio, «Local Binary Pattern Feature Vector Extraction with CNN», Int'l Workshop on Cellular Neural Networks and Their Applications, 2005.
  60. C. Dominguez-Matas, F. Sainchez-Femaindez, R. Carmona-Galan, and E. Roca-Moreno, «Experiments on Global and Local Adaptation to Illumination Conditions based on Focal Plane Average Computation», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  61. L. Torok and A. Zarandy, «CNN Based Color Constancy Algorithm», Int'l Workshop on Cellular Neural Networks and Their Applications, 2002.
  62. P. Ecimovic and J. Wu, «Delay Driven Contrast Enhancement using a Cellular Neural Network with State Dependent Delay», Int'l Workshop on Cellular Neural Networks and Their Applications, 2002.
  63. G. Cserey, C. Rekeczky and P. Foldesy, «PDE Based Histogram Modification with Embedded Morphological Processing of the Level Sets», Int'l Workshop on Cellular Neural Networks and Their Applications, 2002.
  64. L. Orzo, «Optimal CNN Templates for Deconvolution», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006
  65. P. Venetianer and T. Roska, "Image Compression by Cellular Neural Networks, " IEEE Trans. Circuits Syst., 45(3): 205–215, 1998.
  66. R. Dogarut, R. Tetzlaffl and M. Glesner, «Semi-Totalistic CNN Genes for Compact Image Compression», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  67. A. Gacsadi, C. Grava, V. Tiponut, and P. Szolgay, «A CNN Implementation of the Horn & Schunck Motion Estimation Method», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  68. H. Aomori, T. Otaket, N. Takahashi, and M. Tanaka, «A Spatial Domain Sigma Delta Modulator Using Discrete Time Cellular Neural Networks», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  69. H. Kimt, H. Son. J. Lee, I. Kimt and I. Kimt, «An Analog Viterbi Decoder for PRML using Analog Parallel Processing Circuits of the CNN», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  70. S. Chen, M. Kuo and J. Wang, «Image Segmentation Based on Consensus Voting», Int'l Workshop on Cellular Neural Networks and Their Applications, 2005.
  71. G. Grassi, E. Sciascio, A. Grieco and P. Vecchio, «A New Object-oriented Segmentation Algorithm based on CNNs — Part II: Performance Evaluation», Int'l Workshop on Cellular Neural Networks and Their Applications, 2005.
  72. J. Wu, Z. Lin and C. Liou, «Formation and Variability of Orientation Preference Maps in Visual Cortex: an Approach Based on Normalized Gaussian Arrays», Int'l Workshop on Cellular Neural Networks and Their Applications, 2005.
  73. C. Wu and S. Tsai, «Autonomous Ratio-Memory Cellular Nonlinear Network (ARMCNN) for Pattern Learning and Recognition», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  74. G. Timar and C. Rekeczky, «Multitarget Tracking Applications of the Bi-I Platform: Attention-selection, Tracking and Navigation», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  75. Y. Cheng, J. Chung, C. Lin and S. Hsu, «Local Motion Estimation Based On Cellular Neural Network Technology for Image Stabilization Processing», Int'l Workshop on Cellular Neural Networks and Their Applications, 2005.
  76. T. Otake, T. Konishi, H. Aomorit, N. Takahashit and M. Tanakat, «Image Resolution Upscaling Via Two-Layered Discrete Cellular Neural Network», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  77. P. Korbelt and K. Sloti, «Modeling of Elastic Inter-node Bounds in Cellular Neural Network-based Implementation of the Deformable Grid Paradigm», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  78. A. Gacsadi and P. Szolgay, «Image Inpainting Methods by Using Cellular Neural Networks», Int'l Workshop on Cellular Neural Networks and Their Applications, 2005.
  79. B. Shi, T. Roska and L. Chua, "Estimating Optical Flow with Cellular Neural Networks, " Int'l Journal of Circuit Theory and Applications, 26: 344–364, 1998.
  80. D. Vilarino and C. Rekeczky, «Implementation of a Pixel-Level Snake Algorithm on a CNNUM-Based Chip Set Architecture», IEEE Trans. On Circuits And Systems — I, 51(5): 885–891, 2004.
  81. G. Costantini, D. Casali, and R. Perfetti, «Detection of Moving Objects in a Binocular Video Sequence», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  82. 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.
  83. 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.
  84. L. Bertucco, A. Fichaa, G. Nmari and A. Pagano, «A Cellular Neural Networks Approach to Flame Image Analysis for Combustion Monitoring», Int'l Workshop on Cellular Neural Networks and Their Applications, 2000.
  85. 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.
  86. C. Baldanza, F. Bisi, M. Bruschi, I. D'Antone, S. Meneghini, M. Riui, M. Zufa, «A Cellular Neural Network For Peak Finding In High-Energy Physics», Int'l Workshop on Cellular Neural Networks and Their Applications, 2000.
  87. 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.
  88. 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.
  89. Z. Szlavikt R. Tetzlaff1, A. Blug and H. Hoefler, «Visual Inspection of Metal Objects Using Cellular Neural Networks», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  90. K. Huang, C. Chang, W. Hsieh, S. Hsieh, L. Wang and F. Tsai, «Cellular Neural Network For Seismic Horizon Picking», Int'l Workshop on Cellular Neural Networks and Their Applications, 2005.
  91. T. Su, Y. Du, Y. Cheng, and Y. Su, «A Fingerprint Recognition System Using Cellular Neural Networks», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  92. S. Malki, Y. Fuqiang, and L. Spaanenburg, «Vein Feature Extraction Using DT-CNNs», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  93. S. Xavier-de-Souza, M. Van Dyck, J. Suykens and J. Vandewalle, «Fast and Robust Face Tracking for CNN Chips: Application to Wheelchair Driving», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  94. R. Dogaru and I. Dogaru, «Biometric Authentication Based on Perceptual Resonance Between CNN Emergent Patterns and Humans», Int'l Workshop on Cellular Neural Networks and Their Applications, 2002.
  95. Q. Feng , S. Yu and H. Wang, «An New Automatic Nucleated Cell Counting Method With Improved Cellular Neural Networks (ICNN)», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  96. T. Szabot and P. Szolgay, «CNN-UM-Based Methods Using Deformable Contours on Smooth Boundaries», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  97. Z. Szalkat, G. Soost, D. Hilliert, L. Kektt, G. Andrassy and C. Rekeczkytt, «Space-time Signature Analysis of 2D Echocardiograms Based on Topographic Cellular Active Contour Techniques», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  98. M. Bucolo, L. Fortuna, M. Frasca, M. La Rosa, D. Shannahoff-Khalsa, «A CNN Based System to Blind Sources Separation of MEG Signals», Int'l Workshop on Cellular Neural Networks and Their Applications, 2002.
  99. F. Dohlert, A. Chernihovskyi, F. Mormann, C. Elger, and K. Lehnertz, «Detecting Structural Alterations in the Brain using a Cellular Neural Network based Classification of Magnetic Resonance Images», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  100. D. Krug , A. Chernihovskyi, H. Osterhage, C. Elger, and K. Lehnertz, «Estimating Generalized Synchronization in Brain Electrical Activity from Epilepsy Patients with Cellular Nonlinear Networks», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  101. C. Niederhoefer and R. Tetzlaff, «Prediction Error Profiles allowing a Seizure Forecasting in Epilepsy?», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  102. L. Fortuna, P. Arena, D. Balya, and A. Zarandy, «Cellular Neural Networks: A Paradigm for Nonlinear Spatio-Temporal Processing», IEEE Circuits and Systems Magazine, 1(4): 6-21, 2001.
  103. L. Goras, L. Chua, and D. Leenearts, «Turing Patterns in CNNs — Part I: Once Over Lightly», IEEE Trans. on Circuits and Systems — I, 42(10):602-611, 1995.
  104. L. Goras, L. Chua, and D. Leenearts, «Turing Patterns in CNNs — Part II: Equations and Behavior», IEEE Trans. on Circuits and Systems — I, 42(10):612-626, 1995.
  105. L. Goras, L. Chua, and D. Leenearts, «Turing Patterns in CNNs — Part III: Computer Simulation Results», IEEE Trans. on Circuits and Systems — I, 42(10):627-637, 1995.
  106. A. Slavova and M. Markovat, «Receptor Based CNN Model with Hysteresis for Pattern Generation», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  107. L. Komatowskit, K. Slot , P. Dqbiec, and H. Kim, «Generation of Patterns with Predefined Statistical Properties using Cellular Neural Networks», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  108. C. Lin and S. Chen, «Biological Visual Processing for Hybrid-Order Texture Boundary Detection with CNN-UM», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  109. G. Costantini, D. Casali, and M. Carota, «A Pattern Classification Method Based on a Space-Variant CNN Template», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  110. E. David, P. Ungureanu, and L. Goras, «On he Feature Extraction Performances of Gabor-Type Filters in Texture Recognition Applications», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  111. C. Lin and Y. Shou, «Texture Classification and Representation by CNN based Feature Extraction», Int'l Workshop on Cellular Neural Networks and Their Applications, 2005.
  112. T. Roska and L. O. Chua, «The CNN Universal Machine: 10 Years Later, Journal of Circuits, Systems, and Computers», Int'l Journal of Bifurcation and Chaos, 12(4):377-388, 2003.
  113. M. Haenggi, «Mobile Sensor-Actuator Networks: Opportunities and Challenges», Int'l Workshop on Cellular Neural Networks and Their Applications, 2002.
  114. R. Bise, N. Takahashi and T. Nishi, «On the Design Method of Cellular Neural Networks for Associate Memories Based on Generalized Eigenvalue Problem», Int'l Workshop on Cellular Neural Networks and Their Applications, 2002.
  115. D. Balya and V. Galt, «Analogic Implementation of the Genetic Algorithm», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  116. I. Szatmhri, «The Implementation of a Nonlinear Wave Metric for Image Analysis and Classification on the 64x64 I/O CNN-UM Chip», Int'l Workshop on Cellular Neural Networks and Their Applications, 2000.
  117. A. Adamatzky, P. Arena, A. Basile, R. Carmona-Galán, B. Costello, L. Fortuna, M. Frasca, and A. Rodríguez-Vázquez, «Reaction-Diffusion Navigation Robot Control: From Chemical to VLSI Analogic Processors», IEEE Trans. On Circuits And Systems — I, 51(5):926-938, 2004.
  118. I. Gavrilut, V. Tiponut, and A. Gacsadi, «Path Planning of Mobile Robots by Using Cellular Neural Networks», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  119. P. Arena, P. Crucitti, L. Fortuna, M. Frasca, D. Lombardo and L. Patane, «Perceptive Patterns For Mobile Robots via RD-CNN and Reinforcement Learning», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  120. P. Arena, L. Fortuna, M. Frasca, and L. Patane, «CNN Based Central Pattern Generators with Sensory Feedback», Int'l Workshop on Cellular Neural Networks and Their Applications, 2005.
  121. R. Caponetto, L. Fortuna, L. Occhipiniti, and M. G. Xibilii, «SC-CNN Chaotic Signals Generation», Int'l Workshop on Cellular Neural Networks and Their Applications, 2002.
  122. R. Chen and J. Lai, «Data Encryption Using Non-uniform 2-D Von Neumann Cellular Automata», Int'l Workshop on Cellular Neural Networks and Their Applications, 2005.
  123. P. Arena, A. Basile, L. Fortuna, M. E. Yalcin, and J. Vandewalle, «Watermarking for the Authentication of Video on CNN-UM», Int'l Workshop on Cellular Neural Networks and Their Applications, 2002.
  124. K. Slot, P. Korbe, M. Gozdzik, and Hyongsuk Kim, «Pattern detection in spectrograms by means of Cellular Neural Networks», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  125. A. Chernihovskyi , C. Elger, and K. Lehnertz, «Effect of in Inhibitory Diffusive Coupling on Frequency-Selectivity of Excitable Media Simulated With Cellular Neural Networks», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  126. R. Carmona, F. Jimenez-Garrido, R. Dominguez-Castro, S. Espejo and A. Rodriguez-Vazquez, «CMOS Realization of a 2-layer CNN Universal Machine», Int'l Workshop on Cellular Neural Networks and Their Applications, 2002.
  127. Z. Nagyt, Z. Voroshazi and P. Szolgay, «A Real-time Mammalian Retina Model Implementation on FPGA», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  128. D. Balya and B. Roska, «A Handy Retina Exploration Device», Workshop on Cellular Neural Networks and Their Applications, 2005.
  129. P. Arena, M. Bediat, L. Fortuna, D. Lombardo, L. Patane, and M. Velardet, «Spatio-temporal Patterns in CNNs for Classification: the Winnerless Competition Principle», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  130. V. Perez-Munuzuri, A. P. Munuzuri, M. Gomez-Gesteria, V. Perez-Villar, L. Pivka, and L. Chua, "Nonlinear Waves, Patters, and Spatio-Temporal Chaos in Cellular Neural Networks, " Phil. Trans. R. Soc. Lond. A, (353): 101–113, 1995.
  131. M. Ercsey-Ravasz, T. Roska and Z. Neda, «Random Number Generator and Monte Carlo type Simulations on the CMM-UM», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  132. P. Lopez, D. Vilarino, D. Cabello, H. Sahli and M. Balsi, «CNN Based Thermal Modeling of the Soil for Anitpersonnel Mine Detection», Int'l Workshop on Cellular Neural Networks and Their Applications, 2002.
  133. P. Szolgay, T. Hidvegi, Z. Szolgay and P. Kozma, "A Comparison of the Different CNN Implementations in Solving the Problem of Spatiotemporal Dynamics in Mechanical Systems ", Int'l Workshop on Cellular Neural Networks and Their Applications, 2000.
  134. W. Samarrai, J. Yeol, I. Bajis and Y. Ryu, «System Biology Modeling of Protein Process using Deterministic Finite Automata (DFA)», Int'l Workshop on Cellular Neural Networks and Their Applications, 2005.
  135. V. Mladenovt, and A. Slavoval, «On the Period Solutions in One Dimensional Cellular Neural Networks based on Josephson Junctions», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  136. P. Sonkolyt, P. Kozmat, Z. Nagyt and P. Szolgay, «Acoustic Wave Propagation Modeling on CNN-UM Architecture», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  137. S. Kocsardit, Z. Nagyt, S. Kostianevt and P. Szolgay, «FPGA Based Implementation of Water Injection in Geothermal Structure», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  138. R. Brown and L. Chua, «Chaos or Turbulence», Int'l Journal of Bifurcation and Chaos, 2(4):1005-1009, 1992.
  139. P. Arena, L. Fortuna, G. Vagliasindi and A. Basile, «CNN Chip And FPGA To Explore Complexity», Int'l Workshop on Cellular Neural Networks and Their Applications, 2005.
  140. E. Gunay, M. Alci and S. Parmaksizoglu, «N-Scroll Generation in SC-CNN via Neuro Fuzzy Based Non-Linear Function», Int'l Workshop on Cellular Neural Networks and Their Applications, 2006.
  141. M. Gilli, F. Corinto, and P. Checco, «Periodic Oscillations and Bifurcations in Cellular Nonlinear Networks», IEEE Trans. on Circuits and Systems — I, 51(5):948-962, 2004.
  142. K. A. Richardson, «Systems Theory and Complexity: Part 1», Emergence: Complexity and Organization, 6(3):75-79.
  143. K. A. Richardson, «Systems Theory and Complexity: Part 2», Emergence: Complexity and Organization, 6(4):77-82.
  144. K. A. Richardson, «Systems Theory and Complexity: Part 3», Emergence: Complexity and Organization, 7(2):104-114.
  145. P. Anderson, «Emergence», Proceedings of the Second Int'l Conference on Complex Systems, 2004.
  146. K. Mainzer, «CNN and the Evolution of Complex Information Systems in Nature and Technology», Int'l Workshop on Cellular Neural Networks and Their Applications, 2002.
  147. S. Lloyd, Programming the Universe, 2006.
  148. L. Chua, «Local Activity is the Origin of Complexity», Int'l Journal of Bifurcation and Chaos, 15(11):3435-2456, 2005.

Див. також ред.

  Ця стаття є заготовкою. Ви можете допомогти проєкту, доробивши її. Це повідомлення варто замінити точнішим.
Отримано з https://uk.wikipedia.org/w/index.php?title=Клітинні_нейронні_мережі&oldid=36277811