During recent years, systematic procedures for both analysis and forecasting of tropical cyclone intensity were developed. These procedures were designed to improve both the reliability and the consistency of intensity estimates made from satellite imagery. Although procedures have been used and tested under operational conditions in recent years at the centers responsible for tropical storm surveillance, some difficulties and complexity are still there. It is the analysis of cloud features to find out the T-number of the tropical cyclone patterns. This can be solved by Reinforcement Learning for adaptive tropical cyclone patterns segmentation and feature extraction. Tropical cyclone recognition is a multilevel process requiring a sequence of algorithms at low, intermediate, and high levels. Generally such systems are open loop with no feedback between levels and assuring their robustness is key challenge in computer vision and patterns recognition research. A robust closed-loop system based tropical cyclone forecast on reinforcement learning is introduced in this paper.
|Journal||Proceedings of the IEEE International Conference on Systems, Man and Cybernetics|
|Publication status||Published - 1 Dec 1999|
|Event||1999 IEEE International Conference on Systems, Man, and Cybernetics 'Human Communication and Cybernetics' - Tokyo, Japan|
Duration: 12 Oct 1999 → 15 Oct 1999
ASJC Scopus subject areas
- Control and Systems Engineering
- Hardware and Architecture