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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

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A second-order iterated smoothing algorithm.

Dao Nguyen¹, Edward L Ionides¹

¹Department of Statistics, University of Michigan, Ann Arbor, MI, USA.

Statistics and Computing

|September 2, 2017

Summary

This summary is machine-generated.

This study introduces a novel iterated smoothing algorithm for simulation-based inference in complex dynamic models. The new method enhances likelihood maximization, outperforming existing techniques on benchmark tests.

Keywords:

Hidden Markov model Iterated smoothing Parameter estimation Sequential Monte Carlo State space model

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Area of Science:

Computational Statistics
Statistical Inference
Machine Learning

Background:

Simulation-based inference is crucial for partially observed stochastic dynamic models where direct likelihood computation is infeasible.
Sequential Monte Carlo (SMC) filters, including iterated filtering, are used for likelihood maximization.
Existing derivative approximations for log likelihood via SMC smoothing have theoretical advantages.

Purpose of the Study:

To investigate an iterated smoothing algorithm using existing derivative approximations for likelihood maximization.
To develop and validate a novel iterated smoothing algorithm with modified derivative estimates.
To provide theoretical justification and demonstrate practical performance improvements.

Main Methods:

Utilized simulation-based sequential Monte Carlo smoothing for derivative approximation.
Developed and implemented a new iterated smoothing algorithm with modified derivative estimates.
Evaluated performance on benchmark computational challenges against existing iterated filtering algorithms.

Main Results:

The new iterated smoothing algorithm demonstrated superior performance compared to the first-order iterated filtering algorithm.
Performance was comparable to a recent iterated filtering algorithm based on an iterated Bayes map.
Theoretical results and effective practical performance were established for the novel algorithm.

Conclusions:

The developed iterated smoothing algorithm offers a promising advancement in simulation-based inference.
The theoretical justification and practical success open new avenues for latent variable model inference.
Partially observed Markov process models can benefit from these improved inference techniques.