1. bookTom 8 (2018): Zeszyt 3 (July 2018)
Informacje o czasopiśmie
License
Format
Czasopismo
eISSN
2449-6499
Pierwsze wydanie
30 Dec 2014
Częstotliwość wydawania
4 razy w roku
Języki
Angielski
access type Otwarty dostęp

Complex-Valued Associative Memories with Projection and Iterative Learning Rules

Data publikacji: 09 Feb 2018
Tom & Zeszyt: Tom 8 (2018) - Zeszyt 3 (July 2018)
Zakres stron: 237 - 249
Otrzymano: 16 Oct 2017
Przyjęty: 14 Oct 2017
Informacje o czasopiśmie
License
Format
Czasopismo
eISSN
2449-6499
Pierwsze wydanie
30 Dec 2014
Częstotliwość wydawania
4 razy w roku
Języki
Angielski
Abstract

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.

Keywords

[1] A. Hirose, editor, Complex-Valued Neural Networks: Theories and Application, volume 5 of Innovative Intelligence, World Scientific Publishing, Singapore, 2003.10.1142/9789812791184_0001Search in Google Scholar

[2] T. Nitta, editor, Complex-Valued Neural Networks: Utilizing High-Dimensional Parameters, Information Science Reference, Hershey, New York, 2009.10.4018/978-1-60566-214-5Search in Google Scholar

[3] A. Hirose, editor, Complex-Valued Neural Networks: Advances and Applications, The IEEE Press Series on Computational Intelligence, Wiley-IEEE Press, 2013.Search in Google Scholar

[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, 2009.10.1587/transele.E92.C.102Search in Google Scholar

[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.Search in Google Scholar

[6] A. J. Noest, Associative Memory in Sparse Neural Networks, Europhysics Letters, 6(6), pp.469–474, 1988.10.1209/0295-5075/6/5/016Search in Google Scholar

[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 (CNNA-92), pp.36–41, 1992.Search in Google Scholar

[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, 2000.10.1007/978-1-4757-3115-6Search in Google Scholar

[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, 1996.10.1109/72.54817618263542Otwórz DOISearch in Google Scholar

[10] T. Isokawa, H. Nishimura, and N. Matsui, An Iterative Learning Scheme for Multistate Complex-Valued and Quaternionic Hopfield Neural Networks, Proceedings of International Joint Conference on Neural Networks (IJCNN2009), pp.1365–1371, 2009.Search in Google Scholar

[11] M. K. Müezzinoğlu, C. Güzeliş, 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, 2003.10.1109/TNN.2003.81384418238068Otwórz DOISearch in Google Scholar

[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.Search in Google Scholar

[13] M. Kobayashi, Pseudo-relaxation learning algorithm for complex-valued associative memory, International Journal of Neural Systems, 18(2), pp.147–156, 2008.10.1142/S012906570800145218452248Otwórz DOISearch in Google Scholar

[14] T. Kohonen, Self-Organization and Associative Memory, Springer, Berlin, Heidelberg, 1984.Search in Google Scholar

[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.Search in Google Scholar

[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, 1987.10.1103/PhysRevLett.58.94910035080Otwórz DOISearch in Google Scholar

[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, 2012.10.1109/SCIS-ISIS.2012.6505125Search in Google Scholar

[18] F. Flueret and D. Geman, Stationary Features and Cat Detection, Journal of Machine Learning Research, 9, pp.2549–2578, 2008.Search in Google Scholar

[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.Search in Google Scholar

[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.Search in Google Scholar

[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, 2016.10.1007/s10015-015-0247-4Otwórz DOISearch in Google Scholar

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