Sentence examples for models and state from inspiring English sources
The paper outline is as follows: Section 2 describes the system, measurement models, and state estimation.
There we set up the sequence of microscopic models and state conditions for convergence.
The hourly sequential models and state variation models for wind turbines, solar array and battery are established.
Sensitive nonlinear flow sediment terms simplified in linear models and state of non-uniform sediment laden flooding flows in loosed boundaries were considered.
The design is carried out in a model based approach where all the properties are analysed taking into account the coupling between the subsystems by means of finite element models and state space models.
In the former case, we seem to be able to translate between models and state spaces.[3] In the latter, we can derive a state space for chaotic models from the full nonlinear model, but we cannot reverse the process and get back to the nonlinear model state space from that of the chaotic model.
A probabilistic hierarchical scheme is designed based on Gaussian mixture models and state-of-the-art sound parameters selected through extensive experimentation.
Experimental results indicate that the developed CNN architecture outperforms both feature-based learning models and state-of-the-art deep learning models with an accuracy of 67.58%.
The model features detailed chemistry, radiation and soot models and state-of-the-art closures for turbulence chemistry interactions and turbulence radiation interactions.
Undecidability is established for policy-existence problems for partially observable infinite-horizon Markov decision processes under discounted and undiscounted total reward models, average-reward models, and state-avoidance models.
The experimental results, validated on the real-world data provided by DiDi Chuxing, show that the FCL-Net achieves the better predictive performance than traditional approaches including both classical time-series prediction models and state-of-art machine learning algorithms (e.g., artificial neural network, XGBoost, LSTM and CNN).
