Zhang et al., 2021 - Google Patents
Research on damage identification of hull girder based on Probabilistic Neural Network (PNN)Zhang et al., 2021
- Document ID
- 17749256300710839751
- Author
- Zhang Y
- Guo J
- Zhou Q
- Wang S
- Publication year
- Publication venue
- Ocean Engineering
External Links
Snippet
Real-time localization and quantitative assessment of hull girder damage are indispensable for subsequent decisions. To deal with the difficulties that ship damages are hard for real- time assessment, this paper proposes an indirect damage identification method based on …
- 230000001537 neural 0 title abstract description 34
Classifications
-
- 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
- G06N3/08—Learning methods
- G06N3/086—Learning methods using evolutionary programming, e.g. genetic algorithms
-
- 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
- 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
- G06N3/04—Architectures, e.g. interconnection topology
- G06N3/0454—Architectures, e.g. interconnection topology using a combination of multiple neural nets
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- 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
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/12—Computer systems based on biological models using genetic models
- G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
- G06Q10/063—Operations research or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F2217/00—Indexing scheme relating to computer aided design [CAD]
- G06F2217/46—Fuselage
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Ho et al. | A hybrid computational intelligence approach for structural damage detection using marine predator algorithm and feedforward neural networks | |
Fan et al. | Machine learning applied to the design and inspection of reinforced concrete bridges: Resilient methods and emerging applications | |
Shariati et al. | Hybridization of metaheuristic algorithms with adaptive neuro-fuzzy inference system to predict load-slip behavior of angle shear connectors at elevated temperatures | |
Zhang et al. | Research on damage identification of hull girder based on Probabilistic Neural Network (PNN) | |
Park et al. | Parameter estimation of the generalized extreme value distribution for structural health monitoring | |
Alavi et al. | An intelligent structural damage detection approach based on self-powered wireless sensor data | |
Pereira et al. | A powerful Lichtenberg Optimization Algorithm: A damage identification case study | |
Feng et al. | Data-driven algorithm for real-time fatigue life prediction of structures with stochastic parameters | |
Zhang et al. | Vibration-based delamination detection in curved composite plates | |
Feng et al. | Ensemble learning for remaining fatigue life prediction of structures with stochastic parameters: a data-driven approach | |
Wang et al. | A deep learning based approach for response prediction of beam-like structures | |
Lai et al. | Interpretable machine-learning models for maximum displacements of RC beams under impact loading predictions | |
Khatir et al. | A robust FRF damage indicator combined with optimization techniques for damage assessment in complex truss structures | |
Falcone et al. | Artificial neural network for technical feasibility prediction of seismic retrofitting in existing RC structures | |
Feng et al. | Reliability-based multi-objective optimization in tunneling alignment under uncertainty | |
Ghannadi et al. | The Differential Evolution Algorithm: An Analysis of More than Two Decades of Application in Structural Damage Detection (2001–2022) | |
Thirumalaiselvi et al. | Response prediction of laced steel-concrete composite beams using machine learning algorithms | |
Yoshida et al. | Bayesian updating of model parameters using adaptive Gaussian process regression and particle filter | |
Wu et al. | Pipeline damage identification based on an optimized back-propagation neural network improved by whale optimization algorithm | |
Shrestha et al. | Enhancing seismic vulnerability assessment: a neural network effort for efficient prediction of multi-storey reinforced concrete building displacement | |
Zhang et al. | Ultimate axial strength prediction of concrete-filled double-skin steel tube columns using soft computing methods | |
Guo et al. | Data mining and application of ship impact spectrum acceleration based on PNN neural network | |
Mohana | Reinforced concrete confinement coefficient estimation using soft computing models | |
Nadjafi et al. | An effective approach for damage identification in beam-like structures based on modal flexibility curvature and particle swarm optimization | |
Zhang et al. | Experimental research on frequency based damage identification of beams with free boundary condition |