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Representation-burden Conservation Network Applied to Learning VQ (NPL270)

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Abstract

A self-creating network effective in learning vector quantization, called RCN (Representation-burden Conservation Network) is developed. Each neuron in RCN is characterized by a measure of representation-burden. Conservation is achieved by bounding the summed representation-burden of all neurons at constant 1, as representation-burden values of all neurons are updated after each input presentation. We show that RCN effectively fulfills the conscience principle [1] and achieves biologically plausible self-development capability. In addition, conservation in representation-burden facilitates systematic derivations of learning parameters, including the adaptive learning rate control useful in accelerating the convergence as well as in improving node-utilization. Because it is smooth and incremental, RCN can overcome the stability-plasticity dilemma. Simulation results show that RCN displays superior performance over other competitive learning networks in minimizing the quantization error.

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Authors and Affiliations

  1. Dept of Electrical Engineering, National Taiwan Ocean University, 2 Peining Rd., Keelung, 202, Taiwan

    Juang-Hua Wang & Chih-Ping Hsiao

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  1. Juang-Hua Wang
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  2. Chih-Ping Hsiao
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Wang, JH., Hsiao, CP. Representation-burden Conservation Network Applied to Learning VQ (NPL270). Neural Processing Letters 5, 209–217 (1997). https://doi.org/10.1023/A:1009651012418

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  • DOI: https://doi.org/10.1023/A:1009651012418

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