Improvement of HMM-based action classification by using state transition probability

2015 ◽  
Author(s):  
Yuka Kitamura ◽  
Haruki Aruga ◽  
Manabu Hashimoto
Keyword(s):  
State Transition ◽  
Transition Probability ◽  
Action Classification ◽  
State Transition Probability

scholarly journals Grey theory–based BP-NN co-training for dense sequence long-term tendency prediction

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yuling Hong ◽  
Yingjie Yang ◽  
Qishan Zhang
Keyword(s):  
State Transition ◽  
Transition Probability ◽  
Training Model ◽  
Transition Probability Matrix ◽  
Content Type ◽  
Fine Grained ◽  
State Transition Probability ◽  
Dense Sequence ◽  
Popularity Prediction

PurposeThe purpose of this paper is to solve the problems existing in topic popularity prediction in online social networks and advance a fine-grained and long-term prediction model for lack of sufficient data.Design/methodology/approachBased on GM(1,1) and neural networks, a co-training model for topic tendency prediction is proposed in this paper. The interpolation based on GM(1,1) is employed to generate fine-grained prediction values of topic popularity time series and two neural network models are considered to achieve convergence by transmitting training parameters via their loss functions.FindingsThe experiment results indicate that the integrated model can effectively predict dense sequence with higher performance than other algorithms, such as NN and RBF_LSSVM. Furthermore, the Markov chain state transition probability matrix model is used to improve the prediction results.Practical implicationsFine-grained and long-term topic popularity prediction, further improvement could be made by predicting any interpolation in the time interval of popularity data points.Originality/valueThe paper succeeds in constructing a co-training model with GM(1,1) and neural networks. Markov chain state transition probability matrix is deployed for further improvement of popularity tendency prediction.

Recognition and Diagnosis of the Incipient Faults in Analog Circuit Using Improved HMM

2012 ◽  
Vol 588-589 ◽  
pp. 843-846
Author(s):  
Ji Jun Zhang ◽  
Deng Wu Ma ◽  
Lin Wang
Keyword(s):  
Analog Circuits ◽  
Analog Circuit ◽  
State Transition ◽  
Transition Probability ◽  
Control Factor ◽  
Time Varying ◽  
State Transition Probability ◽  
Fault Recognition ◽  
Initial Stage ◽  
Incipient Faults

Due to the uncertainties that exist in the running of the analog circuits, the traditional hidden Markov model (HMM) approach is improved through replacing the state transition probability (STP) matrix of the traditional model by time-varying one. An updating control factor is introduced for avoiding the excess updating of the STP in the initial stage of each state. The experimental results indicate that the improved HMM has better fault recognition and diagnosis capability.

A MATHEMATICAL FRAMEWORK FOR CELLULAR LEARNING AUTOMATA

2004 ◽  
Vol 07 (03n04) ◽  
pp. 295-319 ◽  
Author(s):  
HAMID BEIGY ◽  
M. R. MEYBODI
Keyword(s):  
Cellular Automata ◽  
State Transition ◽  
Transition Probability ◽  
Learning Automata ◽  
Mathematical Framework ◽  
Stochastic Cellular Automata ◽  
Learning Automaton ◽  
State Transition Probability ◽  
Theoretical Investigations ◽  
Cellular Learning Automata

The cellular learning automata, which is a combination of cellular automata, and learning automata, is a new recently introduced model. This model is superior to cellular automata because of its ability to learn and is also superior to a single learning automaton because it is a collection of learning automata which can interact with each other. The basic idea of cellular learning automata, which is a subclass of stochastic cellular learning automata, is to use the learning automata to adjust the state transition probability of stochastic cellular automata. In this paper, we first provide a mathematical framework for cellular learning automata and then study its convergence behavior. It is shown that for a class of rules, called commutative rules, the cellular learning automata converges to a stable and compatible configuration. The numerical results also confirm the theoretical investigations.

Sign in / Sign up

Export Citation Format

Share Document