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Multi-stage Optimization Of A Deep Model

Title: Multi-stage Optimization Of A Deep Model: A Case Study On Ground Motion Modeling.
Name(s): Tahmassebi, Amirhessam, author
Gandomi, Amir H., author
Fong, Simon, author
Meyer-Baese, Anke, author
Foo, Simon Y., author
Type of Resource: text
Genre: Journal Article
Journal Article
Date Issued: 2018-09-19
Physical Form: computer
online resource
Extent: 1 online resource
Language(s): English
Abstract/Description: In this study, a multi-stage optimization procedure is proposed to develop deep neural network models which results in a powerful deep learning pipeline called intelligent deep learning (iDeepLe). The proposed pipeline is then evaluated by a challenging real-world problem, the modeling of the spectral acceleration experienced by a particle during earthquakes. This approach has three main stages to optimize the deep model topology, the hyper-parameters, and its performance, respectively. This pipeline optimizes the deep model via adaptive learning rate optimization algorithms for both accuracy and complexity in multiple stages, while simultaneously solving the unknown parameters of the regression model. Among the seven adaptive learning rate optimization algorithms, Nadam optimization algorithm has shown the best performance results in the current study. The proposed approach is shown to be a suitable tool to generate solid models for this complex real-world system. The results also show that the parallel pipeline of iDeepLe has the capacity to handle big data problems as well.
Identifier: FSU_libsubv1_wos_000445164300050 (IID), 10.1371/journal.pone.0203829 (DOI)
Keywords: networks, algorithm
Publication Note: The publisher’s version of record is available at
Persistent Link to This Record:
Owner Institution: FSU
Is Part Of: Plos One.
Issue: iss. 9, vol. 13

Choose the citation style.
Tahmassebi, A., Gandomi, A. H., Fong, S., Meyer-Baese, A., & Foo, S. Y. (2018). Multi-stage Optimization Of A Deep Model: A Case Study On Ground Motion Modeling. Plos One. Retrieved from