Abstract
Abstract. In this paper, a binary Hopfield neural network with positive selffeedbacks and its collective computational properties are studied. It is proved theoretically and confirmed by simulating the randomly generated Hopfield neural networks with positive self-feedbacks that the emergent collective properties of the original Hopfield neural network also are present in the Hopfield network with positive self-feedbacks. As an example, the network is also applied to the N-Queens problem and results of computer simulations are presented and used to illustrate the computation power of the networks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hopfield, J.J.: Neural Network and Physical Systems with Emergent Collective Computational Abilities. Proc. Natl. Acad. Sci. USA 79, 2554–2558 (1982)
Hopfield, J.J.: Neurons with Graded Response Have Collective Computational Properties Like Those of Two–State Neurons. Proc. Natl. Acad. Sci. USA 81, 3088–3092 (1984)
Hopfield, J.J., Tank, D.W.: ‘Neural’ Computation of Decisions in Optimization Problems. Bio. Cybern. 52, 141–152 (1985)
Hopfield, J.J., Tank, D.W.: Computing with Neural Circuits: A Model. Science 233, 625–633 (1986)
Tank, D.W., Hopfield, J.J.: Simple Neural Optimization Network: An A/D Converter, Signal Decision Circuit, and Linear Programming Circuit. IEEE Trans, Circuits & Systems CAS–33(5), 533–541 (1986)
Yoshino, K., Watanabe, Y., Kakeshita, T.: Hopfield Neural Network Using Oscillatory Units with Sigmoidal Input-Average out Characteristics. IEICE Trans. Inf. & Syst. J77–DII, 219–227 (1994)
Watanabe, Y., Yoshino, K., Kakeshita, T.: Solving Combinatorial Optimization Problem Using Oscillatory Neural Network. IEICE Trans. Inf. & Syst. E 80–D(1), 72–77 (1997)
Takefuji, Y.: Neural Network Parallel Computing. Kluwer Academic Publishers, Dordrecht (1992)
Takenaka, Y., Funabiki, N., Nishikawa, S.: Maximum Neural Network Algorithms for N–Queens Problem. J. IPSJ 37(10), 1781–1788 (1996)
Takenaka, Y., Funabiki, N., Nishikawa, S.: A Proposal of Competition Resolution Methods on The Maximum Neuron Model through N–Queens Problem. J. IPSJ 38(11), 2142–2148 (1997)
Takenaka, Y., Funabiki, N., Higashino, T.: A Proposal of Neuron Filter: A Constraint Resolution Scheme of Neural Networks for Combinatorial Optimization Problems. IEICE Trans. Fundamentals E 83–A(9), 1815–1823 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, Y., Tang, Z., Wang, R., Xia, G., Wang, J. (2004). A Positively Self-Feedbacked Hopfield Neural Network for N-Queens Problem. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_74
Download citation
DOI: https://doi.org/10.1007/978-3-540-28647-9_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22841-7
Online ISBN: 978-3-540-28647-9
eBook Packages: Springer Book Archive