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- Title
Deriving Controllable Local Optimal Solutions through an Environment Parameter Fixed Algorithm.
- Authors
Jang, Ohtae; Jo, Sangho; Kim, Sungho
- Abstract
This paper addresses the challenge of optimizing objective functions in engineering problems influenced by multiple environmental factors, such as temperature and humidity. Traditional modeling approaches often struggle to capture the complexities of non-ideal situations. In this research, we propose a novel approach called the Environment Parameter Fixed Algorithm (EPFA) for optimizing the objective function of a deep neural network (DNN) trained in a specific environment. By fixing the environmental parameters in the DNN defined objective function, we transform the original optimization problem into a control parameter optimization problem. We integrate EPFA-CLS (Controllable local-Optimal Solution) with Gradient Descent and algorithms such as Adagrad to obtain the optimal solution. To demonstrate the concept, we apply our approach to an optimal course model and validate it using optimal course and Boston house price datasets. The results demonstrate the effectiveness of our approach in handling optimization problems in complex environments, offering promising outcomes for practical engineering applications.
- Subjects
BOSTON (Mass.); ALGORITHMS; HOME prices
- Publication
Applied Sciences (2076-3417), 2023, Vol 13, Issue 12, p7110
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app13127110