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Feature extraction-based intelligent algorithm framework with neural network for solving conditional nonlinear optimal perturbation
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DOI：10.1007/s00500-021-06639-8.png)
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Abstract
En 中文
Conditional nonlinear optimal perturbation (CNOP) defines an optimization problem to study predictability and sensitivity of the oceanic and climatic events in the nonlinear system. One effective method to solve the corresponding problem is feature extraction-based intelligent algorithm (FEIA) framework. In the previous study, the mapper and the re-constructor of the framework are generally obtained by principal component analysis (PCA), but the solving performance still needs to further improve. Recently, neural network has attracted the attention of lots of researcher, and many structures of neural network can be used to construct the mapping-reconstruction structure of FEIA framework. However, the related studies applying neural network in FEIA framework are lacking. Compared with PCA, neural network might obtain a proper structure for FEIA framework with the well-directed training. Therefore, this paper suggests two ways applying neural network in FEIA framework, and the corresponding frameworks are tested to solve CNOP of double-gyre variation in Regional Ocean Modeling System (ROMS). The results show that FEIA framework with neural network can obtain the solutions with better objective function values, and the corresponding solutions have a larger probability leading to the related physical phenomenon.
Keywords:
Neural network
Intelligent algorithm
Large-scale optimization
Dimension reduction
Conditional nonlinear optimal perturbation
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