Applying visualization techniques to the development of real-world artificial neural networks applications

Gary Whittington; C. Tim Spracklen

doi:10.1117/12.139979

16 September 1992 Applying visualization techniques to the development of real-world artificial neural networks applications

Gary Whittington, C. Tim Spracklen

Author Affiliations +

Proceedings Volume 1709, Applications of Artificial Neural Networks III; (1992) https://doi.org/10.1117/12.139979
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

The importance of visualization of scientific data has increased over recent years. Much of this interest has arisen because of the growth in computing power capable of generating complex and interactive computer graphics. The use of scientific visualization has a wide range of applications and has proven its utility in many new and existing application areas. However, within the field of artificial networks, visualization techniques have been limited to comparatively simple models, such as Hinton boxes and phase diagrams. The techniques developed in other research areas have generally been ignored. This is especially surprising considering the strong geometric and physical analogies present within the field. This paper examines the potential for visualization techniques in the study and implementation of artificial neural networks. The paper is divided into three sections. The first section briefly reviews scientific visualization and acts as an introduction to the remainder of the paper. The second section illustrates the potential for the visualization of the static and temporal properties of artificial neural networks. The static characteristics can include the visualization of trained networks' weights while the temporal characteristics can include the visualization of the networks' adaptation process or their operation. The discussion is illustrated by examples drawn from a study of the Kohonen Feature Map model. The third section examines the utility of visualization as a practical design aid in the construction and development of real world embedded neural network applications. The potential problems and benefits experienced when applying visualization techniques are highlighted and examined.

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Gary Whittington and C. Tim Spracklen "Applying visualization techniques to the development of real-world artificial neural networks applications", Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); https://doi.org/10.1117/12.139979

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