Symbolic Dynamic Filtering and Language Measure for Behavior Identification of Mobile Robots PROJECT TITLE :Symbolic Dynamic Filtering and Language Measure for Behavior Identification of Mobile RobotsABSTRACT:This paper presents a procedure for behavior identification of mobile robots, which requires limited or no domain knowledge of the underlying process. While the features of robot behavior are extracted by symbolic dynamic filtering of the observed time series, the behavior patterns are classified based on language measure theory. The behavior identification procedure has been experimentally validated on a networked robotic test bed by comparison with commonly used tools, namely, principal component analysis for feature extraction and Bayesian risk analysis for pattern classification. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest A Self-Learning Particle Swarm Optimizer for Global Optimization Problems Language Bootstrapping: Learning Word Meanings From Perception–Action Association