Fast-Maneuvering Target Seeking Based on Double-Action Q-Learning
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Simulating human lifting motions using fuzzy-logic control
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special section: Best papers from the 2007 biometrics: Theory, applications, and systems (BTAS 07) conference
Integrate the GM(1,1) and Verhulst models to predict software stage effort
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Applying grey-fuzzy-fuzzy rule to machine vision based unmanned automatic vehicles
ICIRA'10 Proceedings of the Third international conference on Intelligent robotics and applications - Volume Part I
Predicting software project effort: A grey relational analysis based method
Expert Systems with Applications: An International Journal
Nearest neighbor selection for iteratively kNN imputation
Journal of Systems and Software
Analyzing software effort estimation using k means clustered regression approach
ACM SIGSOFT Software Engineering Notes
Grey relational effort analysis technique using robust regression methods for individual projects
International Journal of Computational Intelligence Studies
| Hi-index | 0.00 |
This paper presents a hierarchical grey-fuzzy motion decision-making (HGFMD) algorithm, which is capable of integrating multiple sequential data for decision making and for the design of the control kernel of the target tracking system. The algorithm combines multiple grey prediction modules, each of which can estimate a suitable model from sequential sensory information for approximating the observed dynamic system for future-trend prediction and for decision making through a multilayer fuzzy logic inference engine. We have designed the HGFMD controller for a target tracking system and implemented it in our autonomous mobile robot. The HGFMD is compared with the conventional fuzzy logic controller, multilayer fuzzy controller, and the original grey-fuzzy controller developed previously in various target-tracking experiments. We demonstrated the high reliability of the HGFMD controller and tracking system even when encountering the uncertain status of slow sensory response time and the nonlinear motion behaviors of the target