论文信息 - Discovering Time Differential Law Equations Containing Hidden State Variables and Chaotic Dynamics

Discovering Time Differential Law Equations Containing Hidden State Variables and Chaotic Dynamics

This paper proposes a novel approach to discover simultaneous time differential law equations having high plausibility to represent first principles underlying objective processes. The approach has the power to identify law equations containing hidden state variables and/or representing chaotic dynamics without using any detailed domain knowledge.

[1] Herbert A. Simon,et al. Scientific discovery: compulalional explorations of the creative process , 1987 .

[2] Elizabeth Bradley,et al. Global solutions for nonlinear systems using qualitative reasoning , 1998, Annals of Mathematics and Artificial Intelligence.

[3] Saso Dzeroski,et al. Declarative Bias in Equation Discovery , 1997, ICML.

[4] Kazumi Saito,et al. Robust Induction of Process Models from Time-Series Data , 2003, ICML.

[5] Takashi Washio,et al. Discovering Admissible Model Equations from Observed Data Based on Scale-Types and Identity Constrains , 1999, IJCAI.

[6] Brian Falkenhainer,et al. Integrating quantitative and qualitative discovery: The ABACUS system , 2004, Machine Learning.

[7] Simon J. Godsill,et al. On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[8] S. Haykin. Kalman Filtering and Neural Networks , 2001 .

[9] Takashi Washio,et al. Discovering Admissible Models of Complex Systems Based on Scale-Types and Idemtity Constraints , 1997, IJCAI.

[10] Ryszard S. Michalski,et al. Integrating Quantitative and Qualitative Discovery: The ABACUS System , 1990, Machine Learning.

[11] Sago Deroski,et al. Discovering Dynamics: From Inductive Logic Programming To Machine Discovery , 2002 .

[12] Pierre Bergé,et al. Order within chaos : towards a deterministic approach to turbulence , 1984 .