A predictor for dead-time systems based on the Kalman Filter for improved disturbance rejection and robustness

Lima, Daniel Martins; Lima, Bruno M.; Normey‐Rico, Julio E.

doi:10.1016/j.jprocont.2021.07.011

Cited by 11 publications

(1 citation statement)

References 31 publications

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“…In the current research, the methods of prediction systems can be roughly divided into the following three categories: physical knowledge methods [3], qualitative model methods [4], and data-driven methods [5]. The method based on physical knowledge is established through system principles and some engineering laws.…”

Section: Introductionmentioning

confidence: 99%

A New Prediction System Based on Self-Growth Belief Rule Base with Interpretability Constraints

Li¹,

Han²,

Wang³

et al. 2023

Computers, Materials &Amp; Continua

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Prediction systems are an important aspect of intelligent decisions. In engineering practice, the complex system structure and the external environment cause many uncertain factors in the model, which influence the modeling accuracy of the model. The belief rule base (BRB) can implement nonlinear modeling and express a variety of uncertain information, including fuzziness, ignorance, randomness, etc. However, the BRB system also has two main problems: Firstly, modeling methods based on expert knowledge make it difficult to guarantee the model's accuracy. Secondly, interpretability is not considered in the optimization process of current research, resulting in the destruction of the interpretability of BRB. To balance the accuracy and interpretability of the model, a self-growth belief rule base with interpretability constraints (SBRB-I) is proposed. The reasoning process of the SBRB-I model is based on the evidence reasoning (ER) approach. Moreover, the self-growth learning strategy ensures effective cooperation between the datadriven model and the expert system. A case study showed that the accuracy and interpretability of the model could be guaranteed. The SBRB-I model has good application prospects in prediction systems.

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Section: Introductionmentioning

confidence: 99%