Abstract
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.
Highlights
Conditional nonlinear optimal perturbation (CNOP) method is proposed by Mu et al (2003) to study predictability and sensitivity of the oceanic and climatic events in the nonlinear system (Wang et al 2009)
The results show that feature extraction-based intelligent algorithm (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
The values of the ks are as follows: kreg is set to 10–6, kspa is set to 10–3, kvae is set to 10–4 and kgan is set to 10–4
Summary
Conditional nonlinear optimal perturbation (CNOP) method is proposed by Mu et al (2003) to study predictability and sensitivity of the oceanic and climatic events in the nonlinear system (Wang et al 2009). Auto-encoder (AE) (Rumelhart et al 1986; Hinton et al 2006; Wang et al 2016; Ramamurthy et al 2020) and its variants such as sparse AE (SAE) (Ng 2011; Liu et al 2019; Zhang et al 2021), convolutional AE (CAE) (Masci et al 2011; Chen et al 2018) and variational AE (VAE) (Kingma et al 2014; Xie et al 2019; Liu et al 2020a, b; Lin et al 2020; Jiao et al 2020) might replace the role of PCA in FEIA framework Another way is applying PCA to obtain the latent space and applying neural network, such as decoder and generative adversarial nets (GAN) (Goodfellow et al 2014; Creswell et al 2018; Zhang et al 2019a, b; Schonfeld et al 2020), to reconstruct the origin space.
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