The evaluation and visualization of system performance in chaotic dynamical systems
Information Sciences: an International Journal - Intelligent manufacturing and fault diagnosis (II). Soft computing approaches to fault diagnosis
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Dynamic System Visualization with Rough Performance Maps
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Measures of Inclusion and Closeness of Information Granules: A Rough Set Approach
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Line-crawling robot navigation: a rough neurocomputing approach
Autonomous robotic systems
Measures of Inclusion and Closeness of Information Granules: A Rough Set Approach
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
| Hi-index | 0.01 |
This article introduces a rough set approach to measuring of information granules derived from performance maps. A performance map employs intuitive color-coding to visualize the behavior of system dynamics resulting from variations in system parameters. The resulting image is developed algorithmically via digital computation. With only moderate 脿 priori knowledge, mathematical analysis of a performance map provides an immediate wealth of information. This study is motivated by an interest in measuring the separation between "islands" (collections of pixels with the same color) representing normal (e.g., black pixels) and potentially chaotic (e.g., red pixels) system behavior. A performance map island or sector is identified with groupings of cells in a mesh resulting from the partition of a performance map into equivalence classes. The information granules considered in this paper are associated with a feature set in an information system. The contribution of this article is the application of a measures of granule closeness based on an indistinguishability relation that partitions performance maps intervals into subintervals (equivalence classes). Such partitions are useful in measuring closeness of map cells containing color-coded pixels used to visualize dynamical system behavior.