Coupled Hidden Markov Model (CHMM) is the extension of traditional HMM, which is mainly used for complex interactive process modeling such as two-hand gestures. However, the problems of finding optimal model parameter...Coupled Hidden Markov Model (CHMM) is the extension of traditional HMM, which is mainly used for complex interactive process modeling such as two-hand gestures. However, the problems of finding optimal model parameter are still of great interest to the researches in this area. This paper proposes a hybrid genetic algorithm (HGA) for the CHMM training. Chaos is used to initialize GA and used as mutation operator. Experiments on Chinese TaiChi gestures show that standard GA (SGA) based CHMM training is superior to Maximum Likelihood (ML) HMM training. HGA approach has the highest recognition rate of 98.0769%, then 96.1538% for SGA. The last one is ML method, only with a recognition rate of 69.2308%.展开更多
This paper presents temporal memory reinforcement learning for the autonomous micro-mobile robot based-behavior. Human being has a memory oblivion process, i.e. the earlier to memorize, the earlier to forget, only the...This paper presents temporal memory reinforcement learning for the autonomous micro-mobile robot based-behavior. Human being has a memory oblivion process, i.e. the earlier to memorize, the earlier to forget, only the repeated thing can be remembered firmly. Enlightening forms this, and the robot need not memorize all the past states, at the same time economizes the EMS memory space, which is not enough in the MPU of our AMRobot. The proposed algorithm is an extension of the Q-learning, which is an incremental reinforcement learning method. The results of simulation have shown that the algorithm is valid.展开更多
文摘Coupled Hidden Markov Model (CHMM) is the extension of traditional HMM, which is mainly used for complex interactive process modeling such as two-hand gestures. However, the problems of finding optimal model parameter are still of great interest to the researches in this area. This paper proposes a hybrid genetic algorithm (HGA) for the CHMM training. Chaos is used to initialize GA and used as mutation operator. Experiments on Chinese TaiChi gestures show that standard GA (SGA) based CHMM training is superior to Maximum Likelihood (ML) HMM training. HGA approach has the highest recognition rate of 98.0769%, then 96.1538% for SGA. The last one is ML method, only with a recognition rate of 69.2308%.
文摘This paper presents temporal memory reinforcement learning for the autonomous micro-mobile robot based-behavior. Human being has a memory oblivion process, i.e. the earlier to memorize, the earlier to forget, only the repeated thing can be remembered firmly. Enlightening forms this, and the robot need not memorize all the past states, at the same time economizes the EMS memory space, which is not enough in the MPU of our AMRobot. The proposed algorithm is an extension of the Q-learning, which is an incremental reinforcement learning method. The results of simulation have shown that the algorithm is valid.
