2009/02/15 by Zekeriya Uykan, Uykan, Zekeriya
Computer Science · Physics and Astronomy · #93C55 #Blind Source Separation Techniques #Data Analysis #FOS: Physical sciences #Fuzzy Logic and Control Systems #Neural Networks and Applications #Statistics and Probability (physics.data-an) #msc:93C55 #physics.data-an
paper · pdf · doi:10.48550/arxiv.0902.2581
23 pages, 2 figures, submitetd 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
In the first part in [12], we present and analyse a Sigmoid-based "Signal-to-Interference Ratio, (SIR)" balancing dynamic network, called Sgm"SIR"NN, which exhibits similar properties as traditional Hopfield NN does, in continuous time. In this second part, we present the corresponding network in discrete time: We show that in the proposed discrete-time network, called D-Sgm"SIR"NN, the defined error vector approaches to zero in a finite step in both synchronous and asynchronous work modes. Our investigations show that i) Establishing an analogy to the distributed (sigmoid) power control algorithm in [10] and [11] if the defined fictitious "SIR" is equal to 1 at the converged eqiulibrium point, then it is one of the prototype vectors. ii) The D-Sgm"SIR"NN exhibits similar features as discrete-time Hopfield NN does. iii) Establishing an analogy to the traditional 1-bit fixed-step power control algorithm, the corresponding "1-bit" network, called Sign"SIR"NN network, is also presented.