Hou et al., 2019 - Google Patents
Sparse Bayesian learning for structural damage detection using expectation–maximization techniqueHou et al., 2019
View PDF- Document ID
- 14714185479520566246
- Author
- Hou R
- Xia Y
- Zhou X
- Huang Y
- Publication year
- Publication venue
- Structural Control and Health Monitoring
External Links
Snippet
Sparse Bayesian learning (SBL) methods have been developed and applied in the context of regression and classification, in which latent variables and hyperparameters are iteratively obtained using type‐II maximization likelihood. However, this method is ineffective …
- 238000000034 method 0 title abstract description 30
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
- G06F17/5018—Computer-aided design using simulation using finite difference methods or finite element methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3409—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation; Recording or statistical evaluation of user activity, e.g. usability assessment for performance assessment
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F2201/00—Indexing scheme relating to error detection, to error correction, and to monitoring
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F2217/00—Indexing scheme relating to computer aided design [CAD]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Hou et al. | Sparse Bayesian learning for structural damage detection using expectation–maximization technique | |
Lye et al. | Sampling methods for solving Bayesian model updating problems: A tutorial | |
Kim et al. | Development of a stochastic effective independence (SEFI) method for optimal sensor placement under uncertainty | |
Sankararaman et al. | Integration of model verification, validation, and calibration for uncertainty quantification in engineering systems | |
Goller et al. | Investigation of model uncertainties in Bayesian structural model updating | |
Sun et al. | A hybrid optimization algorithm with Bayesian inference for probabilistic model updating | |
Meruane et al. | An hybrid real genetic algorithm to detect structural damage using modal properties | |
Song et al. | Bayesian model updating of nonlinear systems using nonlinear normal modes | |
Chen et al. | Modeling geometric imperfections for reticulated shell structures using random field theory | |
Nigro et al. | Localized structural damage detection: a change point analysis | |
Ozdagli et al. | Machine learning based novelty detection using modal analysis | |
Ding et al. | Structural damage identification by sparse deep belief network using uncertain and limited data | |
Gobbato et al. | A recursive Bayesian approach for fatigue damage prognosis: An experimental validation at the reliability component level | |
Hou et al. | Sparse Bayesian learning for structural damage detection under varying temperature conditions | |
Jin et al. | Adaptive reference updating for vibration-based structural health monitoring under varying environmental conditions | |
Zhu et al. | A rapid structural damage detection method using integrated ANFIS and interval modeling technique | |
Wang et al. | Laplace approximation in sparse Bayesian learning for structural damage detection | |
Shi et al. | Uncertain identification method of structural damage for beam-like structures based on strain modes with noises | |
Huang et al. | Damage identification of a steel frame based on integration of time series and neural network under varying temperatures | |
Entezami et al. | Probabilistic damage localization by empirical data analysis and symmetric information measure | |
Yin et al. | Model selection for dynamic reduction-based structural health monitoring following the Bayesian evidence approach | |
Zhou et al. | Computational inference of vibratory system with incomplete modal information using parallel, interactive and adaptive Markov chains | |
Zheng et al. | Bayesian probabilistic framework for damage identification of steel truss bridges under joint uncertainties | |
Liu et al. | Structural damage diagnosis with uncertainties quantified using interval analysis | |
Reda Taha | A Neural‐Wavelet Technique for Damage Identification in the ASCE Benchmark Structure Using Phase II Experimental Data |