2009/02/15 by Zekeriya Uykan, Uykan, Zekeriya
Computer Science · Physics and Astronomy · #93C05 #Blind Source Separation Techniques #Data Analysis #FOS: Physical sciences #Neural Networks and Applications #Speech and Audio Processing #Statistics and Probability (physics.data-an) #msc:93C05 #physics.data-an
paper · pdf · doi:10.48550/arxiv.0902.2577
28 pages, 5 figures, submitted to IEEE Transactions on Neural Networks in December 2008
arxiv created 2009/02/15 · openalex publication_date 2009/02/15 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Continuous-time Hopfield network has been an important focus of research area since 1980s whose applications vary from image restoration to combinatorial optimization from control engineering to associative memory systems. On the other hand, in wireless communications systems literature, power control has been intensively studied as an essential mechanism for increasing the system performance. A fully distributed power control algorithm (DPCA), called Sigmoid DPCA, is presented by Uykan in [10] and [11], which is obtained by discretizing the continuous-time system. In this paper, we present a Sigmoid-based "Signal-to-Interference Ratio, (SIR)" balancing dynamic networks, called Sgm"SIR"NN, which includes both the Sigmoid power control algorithm (SgmDPCA) and the Hopfield neural networks, two different areas whose scope of interest, motivations and settings are completely different. It's shown that the Sgm"SIR"N5C5C5C5C5CN exhibits features which are generally attributed to Hopfield Networks. Computer simulations show the effectiveness of the proposed network as compared to traditional Hopfield Network.