Electrical and Electronic Engineering - Research Publications

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Subject: Artificial Intelligence and Image Processing × Type: Journal Article × Start date: 2007 × End date: 2007 ×

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Now showing 1 - 4 of 4
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    An information-based approach to sensor management in large dynamic networks
    Kreucher, CM ; Hero, AO ; Kastella, KD ; Morelande, MR (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2007-05)
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    Feedback control under data rate constraints: An overview
    Nair, GN ; Fagnani, F ; Zampieri, S ; Evans, RJ (INSTITUTE OF ELECTRICAL ELECTRONICS ENGINEERS (IEEE), 2007)
    The emerging area of control with limited data rates incorporates ideas from both control and information theory. The data rate constraint introduces quantization into the feedback loop and gives the interconnected system a two-fold nature, continuous and symbolic. In this paper, we review the results available in the literature on data-rate-limited control. For linear systems, we show how fundamental tradeoffs between the data rate and control goals, such as stability, mean entry times, and asymptotic state norms, emerge naturally. While many classical tools from both control and information theory can still be used in this context, it turns out that the deepest results necessitate a novel, integrated view of both disciplines.
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    Control of large-scale irrigation networks
    Cantoni, M ; Weyer, E ; Li, Y ; Ooi, SK ; Mareels, I ; Ryan, M (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2007-01)
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    Spike-timing-dependent plasticity for neurons with recurrent connections
    Burkitt, AN ; Gilson, M ; van Hemmen, JL (SPRINGER, 2007-05)
    The dynamics of the learning equation, which describes the evolution of the synaptic weights, is derived in the situation where the network contains recurrent connections. The derivation is carried out for the Poisson neuron model. The spiking-rates of the recurrently connected neurons and their cross-correlations are determined self- consistently as a function of the external synaptic inputs. The solution of the learning equation is illustrated by the analysis of the particular case in which there is no external synaptic input. The general learning equation and the fixed-point structure of its solutions is discussed.