Applications of Cellular Automata: Attractors and Fractals in Analytical Chemistry

Abstract

A cellular automation is a discrete dynamic system of simple construction, yet capable of exhibiting complex self-organizing behavior. A cellular automaton can be used to model differential systems by assuming that time and space are quantized, and that the dependent variable takes on a finite set of possible values. Cellular-automation behavior falls into four distinct universality classes, analogous to 1) limit points, 2) limit cycles, 3) chaotic attractors (fractals), and 4) universal computers. The behavior of members of each of these four classes is explored in the context of digital spectral filtering. The utility of class 2 behavior in experimental data analysis is demonstrated with a laboratory example. Cellular automata have contributed much to computer graphics, and they have much to contribute to chemistry and other sciences as well. Major changes in parallel processing and the implementation and role of pattern recognition are now underway. The cellular-automation model suggests that more than just the process sensors used in pattern-recognition methods can benefit from simplification: the computers, and even the calculations themselves, can benefit from a union of simplification and parallelism. Future work, particularly in the area of parallel algorithms and the design of instruments optimized for use with such algorithms, will open up a range of applications that have yet to be imagined. Keywords: Fractal mathematics; Chemometrics.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jul 11, 1988
Accession Number
ADA197526

Entities

People

  • Gary M. Hieftje
  • Mark Selby
  • Robert A. Lodder

Organizations

  • Indiana University

Tags

Communities of Interest

  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Analytical Chemistry
  • Automata
  • Chemistry
  • Computer Graphics
  • Data Analysis
  • Differential Equations
  • Experimental Data
  • Graphics
  • Mathematical Analysis
  • Mathematics
  • Military Research
  • Parallel Computing
  • Parallel Processing
  • Pattern Recognition
  • Recognition
  • United States

Readers

  • Image Processing and Computer Vision.
  • Mathematical Modeling and Probability Theory.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Space