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Continuous-time Hopfield network has been an important focus of research areasince 1980s whose applications vary from image restoration to combinatorialoptimization from control engineering to associative memory systems. On theother hand, in wireless communications systems literature, power control hasbeen intensively studied as an essential mechanism for increasing the systemperformance. A fully distributed power control algorithm (DPCA), called SigmoidDPCA, is presented by Uykan in [10] and [11], which is obtained by discretizingthe continuous-time system. In this paper, we present a Sigmoid-based"Signal-to-Interference Ratio, (SIR)" balancing dynamic networks, calledSgm"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 theSgm"SIR"N5C5C5C5C5CN exhibits features which are generally attributed toHopfield Networks. Computer simulations show the effectiveness of the proposednetwork as compared to traditional Hopfield Network.

From Sigmoid Power Control Algorithm to Hopfield-like Neural Networks:  "SIR" ("Signal"-to-"Interference"-Ratio)-Balancing Sigmoid-Based Networks-  Part I: Continuous Time