Volatility index prediction based on a hybrid deep learning system with multi-objective optimization and mode decomposition

Tian, Chaonan; Niu, Tong; Wei, Wei

doi:10.1016/j.eswa.2022.119184

Cited by 8 publications

(3 citation statements)

References 57 publications

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“…The study in [38] focuses on optimizing the dynamic performance of aircraft airfoils and designs a deep learning algorithm for multi-objective optimization, aiming to reduce the gap between the solution results and the Pareto front. The study in [39] designed a recursive neural network to predict the financial risks of enterprises.…”

Section: Deep Learningmentioning

confidence: 99%

The GSO-Deep Learning-based Financial Risk Management System for Rural Economic Development Organization

Chen

2023

IJACSA

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Financial risk management has always been a key concern for major enterprises. At the same time, with the continuous attention to impoverished rural areas worldwide, financial risk management tools have become an important component of rural economic development organizations to avoid financial risks. With the rapid development of artificial intelligence technologies such as neural networks and deep learning, and due to their strong learning ability, high adaptability, and good portability, some financial risk management tools are gradually adopting technologies such as neural networks and machine learning. However, existing financial risk management tools based on neural networks are mostly developed for large enterprises such as banks or power grid companies, and cannot guarantee their full applicability to rural economic development organizations. Therefore, this study focuses on the financial risk management system used for rural economic development organizations. In order to improve the accuracy of deep learning algorithms in predicting financial risks, this paper designs an improved Glowworm Swarm Optimization (IGSO) algorithm to optimize Deep Neural Networks (DNN). Finally, the effectiveness of the financial risk management tool based on IGSO-DNN proposed in this article was fully validated using data from 45 rural economic development organizations as a test set.

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Section: Deep Learningmentioning

confidence: 99%

The GSO-Deep Learning-based Financial Risk Management System for Rural Economic Development Organization

Chen

2023

IJACSA

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Section: Introductionmentioning

confidence: 99%

“…According to this relationship, the optimized structural parameters of the camshaft bearing pairs can be obtained through appropriate genetic algorithms, such as multi-objective genetic algorithm (MOGA). Recently, in some pioneering studies, DNN and MOGA have been successfully applied in performance prediction (Tian et al, 2023;Zhang et al, 2016), fault detection and status monitoring (Li et al, 2023;Song et al, 2023), structural and machining process parameters optimization in mechanical manufacturing systems (Guo et al, 2023;Canbulut et al, 2009;Wu et al, 2022). To the authors' knowledge, there are no research studies studying the relationship between structural lubrication performance parameters and optimization design of the camshaft bearing pairs.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of tribological properties of camshaft bearing pairs using DNN coupled with NSGA-II algorithm and TOPSIS

Zhao,

Li,

Xie

et al. 2024

ILT

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Purpose This study aims to propose an optimization framework using deep neural networks (DNN) coupled with nondominated sorting genetic algorithm II and technique for order preference by similarity to an ideal solution method to improve the tribological properties of camshaft bearing pairs of internal combustion engine. Design/methodology/approach A lubrication model based on the theory of elastohydrodynamic lubrication and flexible multibody dynamics was developed for a V6 diesel engine. Setting DNN model as fitness function, the multi-objective optimization genetic algorithm and decision-making method were used to optimize the bearing pair structure with the goal of minimizing the total friction loss and the difference of the average values of minimum oil film thickness. Findings The results show that the lubrication state corresponding to the optimized bearing pair structure is elastohydrodynamic lubrication. Compared with the original structure, the optimized structure significantly reduces the total friction loss. Originality/value The optimized performance and corresponding structural parameters are obtained, and the optimization results were verified through multibody dynamics simulation. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2023-0417/

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Deep Learning in Stock Market: Techniques, Purpose, and Challenges

Marak,

Kulkarni,

Sengupta

2024

Handbook of Formal Optimization

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Volatility index prediction based on a hybrid deep learning system with multi-objective optimization and mode decomposition

Cited by 8 publications

References 57 publications

The GSO-Deep Learning-based Financial Risk Management System for Rural Economic Development Organization

The GSO-Deep Learning-based Financial Risk Management System for Rural Economic Development Organization

Multi-objective optimization of tribological properties of camshaft bearing pairs using DNN coupled with NSGA-II algorithm and TOPSIS

Deep Learning in Stock Market: Techniques, Purpose, and Challenges

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