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Complex-valued associative memories with projection and iterative learning rules

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, we investigate the stability of patterns embedded as the associative memory distributed on the complex-valued Hopfield neural network, in which the neuron states are encoded by the phase values on a unit circle of complex plane. As learning schemes for embedding patterns onto the network, projection rule and iterative learning rule are formally expanded to the complex-valued case. The retrieval of patterns embedded by iterative learning rule is demonstrated and the stability for embedded patterns is quantitatively investigated.
Rocznik
Strony
237--249
Opis fizyczny
Bibliogr. 21 poz., rys.
Twórcy
autor
  • Graduate School of Engineering, University of Hyogo, 2167 Shosha, Himeji, Hyogo, 671-2280 Japan
autor
  • Graduate School of Engineering, University of Hyogo, 2167 Shosha, Himeji, Hyogo, 671-2280 Japan
autor
  • Graduate School of Applied Informatics, University of Hyogo, 7-1-28 Minatojima-Minami-cho, Chuo-ku, Kobe, Hyogo, 650-0047 Japan
autor
  • Graduate School of Engineering, University of Hyogo, 2167 Shosha, Himeji, Hyogo, 671-2280 Japan
autor
  • Graduate School of Engineering, University of Hyogo, 2167 Shosha, Himeji, Hyogo, 671-2280 Japan
autor
  • Graduate School of Engineering, University of Hyogo, 2167 Shosha, Himeji, Hyogo, 671-2280 Japan
Bibliografia
  • [1] A. Hirose, editor, Complex-Valued Neural Networks: Theories and Application, volume 5 of Innovative Intelligence, World Scientific Publishing, Singapore, 2003.
  • [2] T. Nitta, editor, Complex-Valued Neural Networks: Utilizing High-Dimensional Parameters, Information Science Reference, Hershey, New York, 2009.
  • [3] A. Hirose, editor, Complex-Valued Neural Networks: Advances and Applications, The IEEE Press Series on Computational Intelligence, WileyIEEE Press, 2013.
  • [4] Y. Nakano and A. Hirose, Improvement of Plastic Landmine Visualization Performance by Use of Ring-CSOM and Frequency-Domain Local Correlation, IEICE Transactions, 92-C(1), pp.102–108, 2009.
  • [5] Rajoo Pandey, Complex-Valued Neural Networks for Blind Equalization of Time-Varying Channels, International Journal of Signal Processing, 1(1), pp.1–8, 2004.
  • [6] A. J. Noest, Associative Memory in Sparse Neural Networks, Europhysics Letters, 6(6), pp.469–474, 1988.
  • [7] N. N. Aizenberg and I. N. Aizenberg, CNN Based on Multi-Valued Neuron as a Model of Associative Memory for Gray-Scale Images, Proceedings of the 2nd IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA92), pp.36–41, 1992.
  • [8] I. N. Aizenberg, N. N. Aizenberg, and J. Vandewalle, Multi-Valued and Universal Binary Neurons– Theory, Learning and Applications –, Kluwer Academic Publishers, Boston/Dordrecht/London, 2000.
  • [9] S. Jankowski, A. Lozowski, and J. M. Zurada,Complex-Valued Multistate Neural Associative Memory, IEEE Transactions on Neural Networks,7(6), pp.1491–1496, 1996.
  • [10] T. Isokawa, H. Nishimura, and N. Matsui, An Iterative Learning Scheme for Multistate ComplexValued and Quaternionic Hopfield Neural Networks, Proceedings of International Joint Conference on Neural Networks (IJCNN2009), pp.1365–1371, 2009.
  • [11] M. K. Muezzino ¨ glu, C. G ˘ uzelis¸, and J. M. Zurada, ¨A New Design Method for the Complex-Valued Multistate Hopfield Associative Memory, IEEE Transactions on Neural Networks, 14(4), pp.891–899, 2003.
  • [12] D.-L. Lee, Improvements of complex-valued Hopfield associative memory by using generalized projection rules, IEEE Transaction on Neural Networks, 17(5), pp.1341–1347, 2006.
  • [13] M. Kobayashi, Pseudo-relaxation learning algorithm for complex-valued associative memory,International Journal of Neural Systems, 18(2), pp.147–156, 2008.
  • [14] T. Kohonen, Self-Organization and Associative Memory, Springer, Berlin, Heidelberg, 1984.
  • [15] L. Personnaz, I. Guyon, and G. Dreyfus, Collective Computational Properties of Neural Networks: New Learning Mechanisms, Physical Review A, 34, pp.4217–4228, 1986.
  • [16] S. Diederich and M. Opper, Learning of Correlated Patterns in Spin-Glass Networks by Local Learning Rules, Physical Review Letters, 58, pp.949–952, 1987.
  • [17] H. Yamamoto, T. Isokawa, H. Nishimura,N. Kamiura, and N.Matsui, Pattern Stability on Complex-Valued Associative Memory by Local Iterative Learning Scheme, Proceedings of 6th International Conference on Soft Computing and Intelligent Systems & 13th International Symposium on Advanced Intelligent Systems (SCIS-ISIS 2012), pp.39–42, 2012.
  • [18] F. Flueret and D. Geman, Stationary Features and Cat Detection, Journal of Machine Learning Research, 9, pp.2549–2578, 2008.
  • [19] T. Isokawa, H. Nishimura, A. Saitoh, N. Kamiura,and N. Matsui, On the Scheme of Quaternionic Multistate Hopfield Neural Network, Proceedings of Joint 4th International Conference on Soft Computing and Intelligent Systems and 9th International Symposium on advanced Intelligent Systems (SCIS & ISIS 2008), pp.809–813, 2008.
  • [20] T. Isokawa, H. Nishimura, and N. Matsui, Commutative Quaternion and Multistate Hopfield Neural Networks, Proceedings of IEEE World Congress on Computational Intelligence (WCCI2010), pp.1281–1286, 2010.
  • [21] T. Minemoto, T. Isokawa, H. Nishimura, and N. Matsui, Quaternionic multistate Hopfield neural network with extended projection rule, Artificial Life and Robotics, 21(1), pp.106–111, 2016.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-751741f0-3fcd-4ad3-8a84-ea16b4683f5a
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