System Identification이란 무엇인가

By 고봉환. 2011

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Laura von Rueden
2. S. Mayer
3. Annika Pick
4. Katharina Beckh
5. Christian Bauckhage
6. Julius Pfrommer
7. Rajkumar Ramamurthy
8. Jochen Garcke

Citations in the corpus

• Deep learning in neural networks: An overview (2014, neutral): “In the special case of an RL FNN controller C interacting with a deterministic, predictable environment, a separate FNN called M can learn to become C ’s world model through system identification, predicting C ’s inputs from previous actions and inputs (e.g., Cochocki & Unbehauen, 1993; Ge, Hang, Le”
• On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models (2015, neutral): “The first combination of an RL RNN C and an UL RNN M was ours and dates back to 1990 [223, 222, 226, 227], generalizing earlier similar controller/model systems (CM systems) based on FNNs [298, 179]; compare related work [177, 119, 301, 300, 209, 120, 178, 302, 73, 45, 144, 166, 153, 196, 60][245, S”
• Impact of Information Exchange on Supplier Forecasting Performance (2012, unknown): “The parameters na and nb are the orders of the ARX model, and nk is the number of input samples that occur before the input affects the output, also called the dead time in the system [33].”
• Informed Machine Learning – A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems (2019, background): “Approximate solutions to differential equations are studied extensively in numerical mathematics [92] and engineers know the problem of identifying unknown parameters in differential equations as system identification [93].”
• A survey on engineering approaches for self-adaptive systems (2015, unknown): “Two standard schemes of adaptive feedback control loops are Model Identification Adaptive Control (MIAC) [153] and Model Reference Adaptive Control (MRAC) [154].”
• SPICE: A Sparse Covariance-Based Estimation Method for Array Processing (2011, unknown): “, [11], Lemma A.”
• A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems (2011, unknown): “behavior of real-world systems from measured data [150].”
• Optimal Adaptive Control and Differential Games by Reinforcement Learning Principles (2012, unknown): “For online implementation, batch least-squares or RLS can be used to solve (2.110) for the parameter vector Wjþ1 given the regression vector (f(zk) gf(zkþ1)), or (2.112) using regression vector f(zk).”
• Control: A perspective (2014, unknown): “Several books were written as the research progressed [400,506,435,611].”
• Software Engineering for Self-Adaptive Systems: A Research Roadmap (2009, unknown): “For example, control engineering has developed standard approaches to model and reason about feedback such as the Model Reference Adaptive Control (MRAC) [31] and the Model Identification Adaptive Control (MIAC) [37].”