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The WuC-adam algorithm based on joint improvement of Warmup and cosine annealing algorithms

MATHEMATICAL BIOSCIENCES AND ENGINEERING 2024年第1期21卷 1270-1285页

作者： Zhang, Can Shao, Yichuan Sun, Haijing Xing, Lei Zhao, Qian Zhang, Le Shenyang Univ Sch Informat Engn Shenyang 110044 Peoples R China Shenyang Univ Sch Intelligent Sci & Engn Shenyang 110044 Peoples R China Univ Surrey Sch Chem & Chem Engn Surrey GU2 7XH England Shenyang Univ Technol Sch Sci Shenyang 110044 Peoples R China

The adam algorithm is a common choice for optimizing neural network models. However, its application often brings challenges, such as susceptibility to local optima, overfitting and convergence problems caused by unstable learning rate behavior. In this article, we introduce an enhanced adam optimization algorithm that integrates Warmup and cosine annealing techniques to alleviate these challenges. By integrating preheating technology into traditional adam algorithms, we systematically improved the learning rate during the initial training phase, effectively avoiding instability issues. In addition, we adopt a dynamic cosine annealing strategy to adaptively adjust the learning rate, improve local optimization problems and enhance the model's generalization ability. To validate the effectiveness of our proposed method, extensive experiments were conducted on various standard datasets and compared with traditional adam and other optimization methods. Multiple comparative experiments were conducted using multiple optimization algorithms and the improved algorithm proposed in this paper on multiple datasets. On the MNIST, CIFAR10 and CIFAR100 datasets, the improved algorithm proposed in this paper achieved accuracies of 98.87%, 87.67% and 58.88%, respectively, with significant improvements compared to other algorithms. The experimental results clearly indicate that our joint enhancement of the adam algorithm has resulted in significant improvements in model convergence speed and generalization performance. These promising results emphasize the potential of our enhanced adam algorithm in a wide range of deep learning tasks.

关键词： deep learning adam algorithm Warmup cosine annealing strategy local optimum

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Utilization Factor Calculation Method Based on adam algorithm and Neural Network 3rd

Utilization Factor Calculation Method Based on Adam Algorith...

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3rd International Conference on Mechatronics and Intelligent Robotics, ICMIR 2019

作者： Weizhong, Lu Ye, Tang Cheng, Chen Hongmei, Huang The School of Electronic and Information Engineering Suzhou University of Science and Technology SuzhouJiangsu215009 China Jiangsu Key Laboratory of Intelligent Building Energy Efficiency SuzhouJiangsu215009 China Virtual Reality Key Laboratory of Intelligent Interaction and Application Technology of Suzhou SuzhouJiangsu215009 China

ISBN: (纸本)9789811502378

A neural network model consisting of a fixed network and a flexible network and optimized by adam algorithm is designed to calculate the utilization factor when the floor reflectance ratio is 0.2 and the correction factor when it is not 0.2. Comparing with traditional illumination calculation methods, the proposed method can reduce the computational time and complexity, while reducing the calculation error and improving accuracy, resulting in a higher applicability in practice. © Springer Nature Singapore Pte Ltd. 2020.

关键词： adam algorithm Illumination calculation Neural network Utilization factor

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Research on Distributed Fiber Optical Strain Testing Technology Based on the adam algorithm

IEEE PHOTONICS JOURNAL

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IEEE PHOTONICS JOURNAL 2024年第2期16卷 1页

作者： Duan, Jie Liu, Yuting Li, Lijuan Li, Jiyu Zhao, Yanan Zheng, Jianli Changchun Univ Sci & Technol Changchun 130022 Peoples R China

Distributed Optical Fiber Sensing Technology, with advantages such as long detection distance, resistance to electromagnetic interference, and easy maintenance, is widely applied in various fields including security monitoring, electrical safety, and aerospace. However, the current Distributed Optical Fiber Sensing Technology faces challenges such as short localization distance, limited range for stress measurement, low accuracy, and time-consuming processes. This study proposes a novel approach for extracting Brillouin frequency shift signals using an adaptive gradient descent algorithm (adam algorithm). A Brillouin strain testing system based on heterodyne coherent detection is also constructed. Experimental results show that the distributed strain testing system using the adam algorithm can achieve accurate and fast measurement of maximum strain up to 9500 mu epsilon within a range of 10 kilometers. The average strain measurement deviation is 32.88 mu epsilon, and the time required for frequency shift extraction is less than 18.5 ms. This method provides a theoretical and experimental basis for the application of BOTDR Distributed Optical Fiber Sensing Technology.

关键词： adam algorithm brillouin frequency shift extraction distributed fiber optical sensing strain testing

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A new short -arc fitting method with high precision using adam optimization algorithm

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OPTIK 2020年 212卷 164788-164788页

作者： Fei, Zhigen Wu, Zhiying Xiao, Yanqiu Ma, Jun He, Wenbin Zhengzhou Univ Light Ind Henan Key Lab Intelligent Mfg Mech Equipment Zhengzhou 450002 Peoples R China

The high-precision short-arc measurement is a huge challenge in scientific research and engineering practice. The popular traditional least square fitting (TLSF) however fails to achieve the precise fitting parameters when the arc gets shorter. In this work, an inequation constrained fitting (ICF) method based on four-parameter circle equation is presented. Lagrangian multiplier and Karush-Kuhn-Tucker criteria are used to redefine the objective function. After that, adam algorithm is utilized to solve the objective function in iterative way. adam algorithm has a strong ability to resist noise pollution by virtue of modifying continually the first-order momentum and second-order momentum with average of gradients during the course of iteration. Finally, simulation and experimental results show that our ICF method is more robust and high-precision than TLSF and Hyper method, so it is very competent to measure short arcs with noise even their central angles are close to 5°.

关键词： Short-arc fitting adam algorithm Lagrangian multiplier

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Fast stability assessment of rock slopes subjected to flexural toppling failure using adaptive moment estimation (adam) algorithm

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LANDSLIDES 2022年第9期19卷 2149-2158页

作者： Zheng, Yun Wang, Runqing Chen, Congxin Meng, Fei Chinese Acad Sci Inst Rock & Soil Mech State Key Lab Geomech & Geotech Engn Wuhan 430071 Hubei Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Wuhan Univ Technol Hubei Key Lab Roadway Bridge & Struct Engn Wuhan 430070 Hubei Peoples R China North China Univ Water Resources & Elect Power Coll Geosci & Engn Zhengzhou 450046 Peoples R China

Stability assessment of anti-dip bedding rock slopes (ABRSs) remains a troublesome issue especially when there are hundreds or thousands of rock layers. The adaptive moment estimation (adam) algorithm proposed in recent years has been widely used in artificial intelligence research and is a powerful tool for solving the above-mentioned problem. In this work, the limit equilibrium method (LEM) is combined with the adam algorithm (referred to as the LEM-adam method) to determine the failure surface of an ABRS undergoing shearing-flexural toppling failure. Two tests reported in previous studies (a 1 g model and a centrifuge model) were analyzed to validate the LEM-adam method. The critical failure surface and the safety factor agree well with the experimental observations, validating the LEM-adam method in the stability analysis of ABRSs. Finally, the LEM-adam method was compared with the LEM combined with a genetic algorithm (GA). The results show that the LEM-adam method has higher computational efficiency and eliminates the randomness of the solution. The LEM-adam method proposed in this work offers a practical and fast approach for the stability analysis and design of ABRSs.

关键词： Anti-dip bedding rock slopes Stability analysis Failure surface Safety factor adam algorithm

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An Improved adam's algorithm for Stomach Image Classification

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algorithmS 2024年第7期17卷 272-272页

作者： Sun, Haijing Yu, Hao Shao, Yichuan Wang, Jiantao Xing, Lei Zhang, Le Zhao, Qian Shenyang Univ Sch Intelligent Sci & Engn Shenyang 110044 Peoples R China Shenyang Univ Sch Informat Engn Shenyang 110044 Peoples R China Univ Surrey Sch Chem & Chem Engn Guildford GU2 7XH England Shenyang Univ Technol Sch Sci Shenyang 110044 Peoples R China

Current stomach disease detection and diagnosis is challenged by data complexity and high dimensionality and requires effective deep learning algorithms to improve diagnostic accuracy. To address this challenge, in this paper, an improved strategy based on the adam algorithm is proposed, which aims to alleviate the influence of local optimal solutions, overfitting, and slow convergence rates by controlling the restart strategy and the gradient norm joint clipping technique. This improved algorithm is abbreviated as the CG-adam algorithm. The control restart strategy performs a restart operation by periodically checking the number of steps and once the number of steps reaches a preset restart period. After the restart is completed, the algorithm will restart the optimization process. It helps the algorithm avoid falling into the local optimum and maintain convergence stability. Meanwhile, gradient norm joint clipping combines both gradient clipping and norm clipping techniques, which can avoid gradient explosion and gradient vanishing problems and help accelerate the convergence of the optimization process by restricting the gradient and norm to a suitable range. In order to verify the effectiveness of the CG-adam algorithm, experimental validation is carried out on the MNIST, CIFAR10, and Stomach datasets and compared with the adam algorithm as well as the current popular optimization algorithms. The experimental results demonstrate that the improved algorithm proposed in this paper achieves an accuracy of 98.59%, 70.7%, and 73.2% on the MNIST, CIFAR10, and Stomach datasets, respectively, surpassing the adam algorithm. The experimental results not only prove the significant effect of the CG-adam algorithm in accelerating the model convergence and improving generalization performance but also demonstrate its wide potential and practical application value in the field of medical image recognition.

关键词： deep learning adam algorithm control restart strategy gradient norm joint clipping CG-adam algorithm

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Evaluating Adan vs. adam: An Analysis of Optimizer Performance in Deep Learning 5th

Evaluating Adan vs. Adam: An Analysis of Optimizer Performan...

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5th International Symposium on Intelligent Computing Systems

作者： Ismail, Amelia Ritahani Azhary, Muhammad Zulhazmi Rafiqi Hitam, Nor Azizah Int Islam Univ Malaysia Kulliyyah Informat Commun Technol Kuala Lumpur Malaysia Higher Coll Technol Fac Comp Informat Syst Dubai U Arab Emirates

ISBN: (纸本)9783031829307;9783031829314

Choosing a suitable optimization algorithm in deep learning is essential for effective model development as it significantly influences convergence speed, model performance, and the success of the training process. Optimizers play an essential role in adjusting the model's parameters to minimize errors, assisting the learning process during the model development. With various optimization algorithms available, choosing the one that best suits the deep learning model and dataset can make a substantial difference in achieving optimal results. Adaptive Moment Estimation (adam) and Adaptive Nesterov Accelerated Gradient (Adan), two well-known optimizers, are widely used in deep learning for their ability to handle large-scale data and complex models efficiently. While adam is known for its balance between speed and reliability, Adan builds on this by incorporating momentum and lookahead mechanisms to enhance the model's performance. However, choosing the right optimizer for different tasks can be challenging, as each optimizer offers various advantages and disadvantages. This paper, therefore, explores the comparative effectiveness of adam and Adan optimizers, analyzing their impact on convergence speed, model performance, and overall training success on different classifications tasks, which are image and text classifications. The results show that adam performs better initially, but prone to overfitting. On the other hand, for image classification tasks, Adan provides more consistent optimisation across extended training periods. Based on these results, this paper aims to provide insights into the strengths and limitations of each optimizer, highlighting the importance of considering task-specific requirements when selecting an optimization algorithm for deep learning models.

关键词： adam algorithm Adan algorithm Adaptive optimizer Deep Learning

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HawkEye: A robust loss function for regression with bounded, smooth, insensitive zone characteristics

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APPLIED SOFT COMPUTING 2025年 176卷

作者： Akhtar, Mushir Tanveer, M. Arshad, Mohd. Indian Inst Technol Indore Dept Math Indore 453552 Madhya Pradesh India

Support vector regression (SVR) encounters challenges when confronted with outliers and noise, primarily due to the limitations of the traditional E-insensitive loss function. To address this, bounded loss functions have gained traction for their robustness and improved generalization. More recent advancements, BLINEX and bounded least square loss, focus on smooth bounded loss functions that enable efficient gradient based optimization. However, these approaches lack an insensitive zone, which is crucial for mitigating deviations and noise. The challenge of designing a loss function that combines boundedness, smoothness, an insensitive zone remains unresolved in the current literature. To address this issue, we develop the HawkEye loss, a novel formulation that integrates boundedness, smoothness, and the presence of an insensitive This unique combination enhances the robustness and generalization capabilities of SVR models, particularly the presence of noise and outliers. Notably, the HawkEye loss is the first in SVR literature to simultaneously incorporate boundedness, smoothness, and an insensitive zone. Leveraging this breakthrough, we integrate the HawkEye loss into the least squares framework of SVR and yield a new robust and scalable termed HE-LSSVR. The optimization problem inherent to HE-LSSVR is addressed by harnessing the adaptive moment estimation (adam) algorithm, known for its adaptive learning rate and efficacy in handling scale problems. To our knowledge, this is the first time adam has been employed to solve an SVR problem. empirically validate the proposed HE-LSSVR model, we evaluate it on UCI, synthetic, time series, and age datasets. The experimental outcomes unequivocally reveal the superiority of the HE-LSSVR model terms of its remarkable generalization performance and its efficiency in training time. The code of the proposed model is publicly available at https://***/mtanveer1/HawkEye.

关键词： Supervised learning Support vector regression Loss function HawkEye loss function Inverse-free optimization adam algorithm

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adam Bald Eagle optimization enabled transfer learning for underwater image fusion

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IMAGING SCIENCE JOURNAL 2024年第5期72卷 615-630页

作者： Sarath, Devika Sucharitha, M. Noorul Islam Ctr Higher Educ Dept Elect & Commun Engn Kumaracoil Tamil Nadu India VITAP Univ Sch Elect Engn Elect & Commun Engn Amaravathi Andhra Pradesh India Noorul Islam Ctr Higher Educ Dept Elect & Commun Engn Kumaracoil 629180 Tamil Nadu India

In this paper, a clear underwater image is attained by a fusion process using Transfer Learning (TL). Two images are selected from the underwater colour image dataset and those images are allowed to Discrete Wavelet Transform (DWT), Tetrolet transform and Saliency maps. Here, the outputs gained from images by the Tetrolet transform are fused and allowed for inverse Tetrolet transform. Moreover, the DWT process done with two images is fused and the output gained is allowed for inverse DWT. Similarly, the same fusion process is carried out with image outputs from Saliency maps. Finally, three image outputs that are considered as input to TL with newly devised optimization. Here, Convolutional Neural Network (CNN) is used with hyperparameters from trained models, such as SqueezeNet and AlexNet, where weights are updated using adam Based Bald Eagle algorithm (ABBEA). This ABBEA is obtained by combining the Bald Eagle Search (BES) algorithm and adam algorithm. Further, the ABBEA has Peak Signal-to-Noise Ratio (PSNR) with maximal of 38.95, Mean Squared Error (MSE) with lesser value of 20.14, Structural Similarity Index Measure (SSIM) with maximal value of 0.92, Mutual Information (MI) with maximal value of 0.86, Signal-to-Noise Ratio (SNR) with lesser value of 0.38.

关键词： SqueezeNet Bald Eagle Search algorithm AlexNet adam algorithm Discrete Wavelet Transform Convolutional Neural Network Transfer Learning

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Adaptive moment estimation for universal portfolio selection strategy

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OPTIMIZATION AND ENGINEERING 2023年第4期24卷 2357-2385页

作者： He, Jin'an Peng, Fangping Sun Yat Sen Univ Sch Business Guangzhou 510275 Peoples R China

Since Cover's initial work in 1991, Online Portfolio Selection is undoubtedly one of crucial branches of portfolio optimization problem. The increasing development of high-frequency trading and the explosive growth of data volume increasingly require online portfolio strategies to have fast execution speed. However, almost all existing online portfolio strategies cannot satisfy both effective and full utilization of historical data and fast execution speed simultaneously. To this end, this work proposes the Adaptive Moment Estimation (AME) strategy. The proposed strategy makes effective and full utilization of historical data in an incremental way, which not only achieves improved robustness in performance but also keeps linear time complexity. Theoretical and experimental analysis are respectively established to analyze the effectiveness of the proposed AME strategy. For theoretical analysis, AME's universality is proved, which illustrates that it can theoretically compete with the benchmark Best Constant Rebalanced Portfolio in hindsight. For experimental analysis, AME achieves good performance on different evaluation metrics, and it can sustain reasonable transaction costs. Thus, it is a robust and excellent strategy with promising practical application prospects.

关键词： Online learning Universal portfolio selection adam algorithm High-frequency trading

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