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The Multilayer Random Neural Network

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Abstract

We propose in this paper an extended model of the random neural networks, whose architecture is multi-feedback. In this case, we suppose different layers where the neurons have communication with the neurons of the neighbor layers. We present its learning algorithm and its possible utilizations; specifically, we test its use in an encryption mechanism where each layer is responsible of a part of the encryption or decryption process. The multilayer random neural network is a stochastic neural model, in this way the entire proposed encryption model has that feature.

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References

  1. Abdelbaki H, Gelenbe E, Kocak T (1999) Matched neural filters for EMI based mine detection. In: Proceedings of international joint conference on neural networks. IJCNN, Atlanta, p 3236–3240

  2. Aguilar J (2004) A color pattern recognition problem based on the multiple classes random neural network model. Neurocomputing 61: 71–83

    Article  Google Scholar 

  3. Aguilar J (2001) Learning algorithm and retrieval process for the multiple classes random neural network model. Neural Process Lett 13: 81–91

    Article  MATH  Google Scholar 

  4. Aguilar J (1998) Definition of an energy function for the random neural to solve optimization problems. Neural Netw 11: 731–738

    Article  Google Scholar 

  5. Aguilar A, Colmenares J (1998) Resolution of pattern recognition problems using a hybrid genetic/random neural network learning algorithm. Pattern Anal Appl 1: 52–61

    Article  MATH  Google Scholar 

  6. Aguilar J (2011) Details of the learning procedure for the multilayers random neural network model. Technical report 08-11. CEMISID, Universidad de los Andes, Merida

  7. Atalay V, Gelenbe E, Yalabik N (1992) The random neural network model for texture generation. Int J Pattern Recognit Artif Intell 6: 131–141

    Article  Google Scholar 

  8. Bakircioglu H, Gelenbe E, Kocak T (1997) Image enhancement and fusion with the random neural network. Turk J Electr Eng Comput Sci 5: 65–77

    Google Scholar 

  9. Bakircioglu H, Gelenbe E (1998) Random neural network recognition of shaped objects in strong clutter. In: Proceedings of 3rd conference on applications of artificial neural networks in image processing. Springer, New York, p 22–28

  10. Bakircioglu H, Kocak T (2000) Survey of random neural network applications. Eur J Oper Res 126: 319–330

    Article  MathSciNet  MATH  Google Scholar 

  11. Basterrech S, Mohammed S, Rubino G, Soliman M (2009) Levenberg–Marquardt training algorithms for random neural networks. Comput J 19: 324–337

    Google Scholar 

  12. Cramer C, Gelenbe E, Gelenbe P (1998) Image and video compression. IEEE Potential 17: 29–33

    Article  Google Scholar 

  13. Fourneau M, Gelenbe E, Suros R (1996) G-networks with multiple classes of negative and positive customers. Theor Comput Sci 155: 141–156

    Article  MathSciNet  MATH  Google Scholar 

  14. Gelenbe E (1989) Random neural networks with positive and negative signals and product form solution. Neural Netw 1(4): 502–510

    Google Scholar 

  15. Gelenbe E (1991) Theory of the random neural network model. Neural networks: advances and applications. Elsevier, North Holland

    Google Scholar 

  16. Gelenbe E (1993) Learning in the recurrent neural network. Neural Comput 5: 154–164

    Article  Google Scholar 

  17. Gelenbe E, Feng T, Krishnan K (1996) Neural network methods for volumetric magnetic resonance imaging of the human brain. Proc IEEE 84: 1488–1496

    Article  Google Scholar 

  18. Gelenbe E, Kocak T (2004) Wafer surface reconstruction from top-down scanning electron microscope images. Microelectron Eng 75: 216–233

    Article  Google Scholar 

  19. Gelenbe E, Timotheou S (2008) Synchronized intercations in spiked neuronal models. Comput J 51: 723–730

    Article  Google Scholar 

  20. Gelenbe E, Timotheou S (2008) Random neural networks with synchronized interactions. Neural Comput 20: 2308–2324

    Article  MathSciNet  MATH  Google Scholar 

  21. Georgiopoulos M, Li C, Kocak T (2011) Learning in the feed-forward random neural network: a critical review. Perform Eval 68: 361–384

    Article  Google Scholar 

  22. Halici U, Karaoz E (1996) A linear approximation for the Gelenbe’s learning algorithm for recurrent random neural networks. In: Proceedings of the 6th Turkish symposium on artificial intelligence and neural networks. Springer, New York, p 86–94

  23. Hubert C (1993) Pattern completion with the random neural network using the RPROP learning algorithm. In: Proceedings of international conference on man and cybernetics, vol 2. ICMC, LeTouquet, p 613–617

  24. Hussain K, Moussa G (2005) Laser intensity vehicle classification system based on random neural network. In: Proceedings of 43rd annual southeast regional conference. ACM, New York, p 31–35

  25. Jacobs R (1988) Increased rates of convergence through learning rate adaptation. Neural Netw 1: 151–160

    Article  Google Scholar 

  26. Jordi X (2002) Análisis de redes y sistemas de comunicaciones. Ediciones UPC, Barcelona

    Google Scholar 

  27. Kanter I. (2002) Secure exchange of information by sychronization of neural networks. Europhys Lett 57(1): 141–147

    Article  Google Scholar 

  28. Kiyoharu Y (2004) Advances in multimedia information processing PCM 2004. Springer, Tokyo

    Google Scholar 

  29. Likas A, Stafylopatis A (2000) Training the random neural network using quasi-newton methods. Eur J Oper Res 126: 331–339

    Article  MathSciNet  MATH  Google Scholar 

  30. NCCS.gov. (2011) National center computional sciences. http://www.nccs.gov/computing-resources/jaguar/. Accessed 3 June 2011

  31. Stallings W (2004) Fundamentos de Seguridad en Redes, Aplicaciones y Estandares. Pearson, Madrid

    Google Scholar 

  32. Stork R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11: 341–359

    Article  Google Scholar 

  33. Tanenbaum A (2003) Redes de computadoras. Pearson Educacion, Mexico

    Google Scholar 

  34. Timotheou S (2010) The random neural network: a survey. Comput J 53: 251–267

    Article  Google Scholar 

  35. Triguero J, Guerrero M, Crespo E (2006) Introducción a la criptografía: historia y actualidad. Ediciones de la Universidad de Castilla-La Mancha, Cuenca

    Google Scholar 

  36. Wen Yu J (2006) Cryptography based on delayed chaotic neural networks. Phys Letts 356: 333–338

    Article  Google Scholar 

  37. Zheng Q, Fan Y, Shi Z, Wang Y (2006) Adaptive inertia weight particle swarm optimization. In: Lecture notes in computer science, vol 4029. Springer, Berlin, pp 450–459

    Google Scholar 

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Authors and Affiliations

  1. CEMISID, Departamento de Computación, Escuela de Ingeniería de Sistemas, Universidad de Los Andes, Mérida, Venezuela

    Jose Aguilar & Cristhian Molina

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  1. Jose Aguilar
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  2. Cristhian Molina
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Correspondence to Jose Aguilar.

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Aguilar, J., Molina, C. The Multilayer Random Neural Network. Neural Process Lett 37, 111–133 (2013). https://doi.org/10.1007/s11063-012-9237-x

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