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Application Example: An Adaptive Neural Network Source Coder

  • Tsu-Chang Lee
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 133)

Abstract

In this chapter, I apply the S-Level network SPAN introduced in Chapter 4 to source coding problems for digital communication. The basic idea is to use SPAN as an active codebook that can grow from scratch to follow the statistics of source signals, capture the local context of the source signal space, and map onto the structure of the network. As a result, when the statistics of the source signals change, the network can dynamically modify its structure to follow the change.

Keywords

Vector Quantization Finite State Automaton Optimum Distortion Rate Path Code Neuron Position 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1991

Authors and Affiliations

  • Tsu-Chang Lee
    • 1
  1. 1.Cadence Design SystemsStanford UniversityUSA

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