The Computational Beauty of Nature
Computer Explorations of Fractals, Chaos,
Complex Systems, and Adaptation


About the Book
  · title page
  · home*
  · cover artwork
  · jacket text
  · table of contents
  · the author*
  · ordering information
Book Contents
  · three themes
  · part synopses
  · selected excerpts
  · all figures from book
  · quotes from book
  · glossary from book
  · bibliography
  · slide show
Source Code
  · overview &
documentation
  · FAQ list*
  · download source code
  · java applets
Miscellany
  · news*
  · reviews & awards
  · errata
  · for educators
  · bibliography (BibTeX format)
  · other links
Glossary - W


[ A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z ]


W

Weight     In a neural network, the strength of a synapse (or connection) between two neurons. Weights may be positive (excitatory) or negative (inhibitory). The thresholds of a neuron are also considered weights, since they undergo adaptation by a learning algorithm.

White Noise     Noise that uniformly distributed in the frequency domain; randomness that is uniformly distributed; thus, a white noise process with a range of 0 to 1 would yield a random number in this range with probability equal for all possible values. Brown noise is a result of cumulatively adding white noise.


[ A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z ]

Copyright © Gary William Flake, 1998-2002. All Rights Reserved. Last modified: 30 Nov 2002