An enhanced learning algorithm with a particle filter-based gradient descent optimizer method

Kamsing, Patcharin; Torteeka, Peerapong; Yooyen, Soemsak

doi:10.1007/s00521-020-04726-9

Cited by 14 publications

(6 citation statements)

References 28 publications

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“…e LMBP algorithm integrates the advantages of gradient descent method and the Gauss-Newton method and realizes fast operation through standard numerical optimization technology [13,14].…”

Section: Precision Control Of Lmbp Algorithmmentioning

confidence: 99%

Relationship Model between Human Resource Management Activities and Performance Based on LMBP Algorithm

Liu

Zhao

2022

Security and Communication Networks

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The research on the relationship between human resource management activities and performance is an important topic of enterprise human resource management research. There are some errors between the relationship between human resource management activities and performance and the real situation, so it is impossible to accurately predict the performance fluctuation. Therefore, the relationship model between human resource management activities and performance based on the LMBP algorithm is constructed. Using the Levenberg–Marquardt (LM) algorithm and BP (back-propagation) neural network algorithm to establish a new LMBP algorithm, control the convergence of the new algorithm, optimize the accuracy of the algorithm, and then apply the LMBP algorithm to predict the risk of performance fluctuation under human resource management activities of enterprises, the indicators of human resource management activities of enterprises are determined, to complete the mining of enterprise performance data, the grey correlation analysis is combined, and the relationship model between human resource management activities and performance is built. The experimental samples are selected from CSMAR database, and the simulation experiment is designed. Using different algorithms to forecast the fluctuation of enterprise performance, the experimental results show that the LMBP algorithm can more accurately reflect the relationship between enterprise HRM and performance.

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“…e LMBP algorithm integrates the advantages of gradient descent method and the Gauss-Newton method and realizes fast operation through standard numerical optimization technology [13,14].…”

Section: Precision Control Of Lmbp Algorithmmentioning

confidence: 99%

Relationship Model between Human Resource Management Activities and Performance Based on LMBP Algorithm

Liu

Zhao

2022

Security and Communication Networks

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“…A workflow of the applied method to obtain the K value is depicted in Figure 4. e applied method shown in Figure 4 is described as follows [32]:…”

Section: Methodsmentioning

confidence: 99%

“…For example, for real-time traffic estimation, state estimation has been implemented using an extended Kalman filter instead of using Gaussian process regression models with respect to historical data [29]. A particle filter has also been implemented to adjust various parameters to improve image classification [30][31][32] and for some application such as crack propagation filtering [33]. e gradient descent algorithm is mainly used to optimize an objective [34].…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Learning Adaptive Sequential Monte Carlo for Automatic Image and Speech Recognition

Kamsing

Torteeka

Boonpook

et al. 2020

Applied Computational Intelligence and Soft Computing

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To enhance the performance of image classification and speech recognition, the optimizer is considered an important factor for achieving high accuracy. The state-of-the-art optimizer can perform to serve in applications that may not require very high accuracy, yet the demand for high-precision image classification and speech recognition is increasing. This study implements an adaptive method for applying the particle filter technique with a gradient descent optimizer to improve model learning performance. Using a pretrained model helps reduce the computational time to deploy an image classification model and uses a simple deep convolutional neural network for speech recognition. The applied method results in a higher speech recognition accuracy score—89.693% for the test dataset—than the conventional method, which reaches 89.325%. The applied method also performs well on the image classification task, reaching an accuracy of 89.860% on the test dataset, better than the conventional method, which has an accuracy of 89.644%. Despite a slight difference in accuracy, the applied optimizer performs well in this dataset overall.

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“…In this paper, an optimizer used a gradient descent algorithm is selected to update the value of the loss function and parameterW P . It should be noted that the parameter W P here is required by the learned transformation U P [30] in cases 2 and 3. The squared difference loss function:…”

Section: Experiments Proceduresmentioning

confidence: 99%

An Image Localization System Based on Single Photon

Wu¹,

Li²,

Zhu³

et al. 2022

Computers, Materials &Amp; Continua

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As an essential part of artificial intelligence, many works focus on image processing which is the branch of computer vision. Nevertheless, image localization faces complex challenges in image processing with image data increases. At the same time, quantum computing has the unique advantages of improving computing power and reducing energy consumption. So, combining the advantage of quantum computing is necessary for studying the quantum image localization algorithms. At present, many quantum image localization algorithms have been proposed, and their efficiency is theoretically higher than the corresponding classical algorithms. But, in quantum computing experiments, quantum gates in quantum computing hardware need to work at very low temperatures, which brings great challenges to experiments. This paper proposes a single-photon-based quantum image localization algorithm based on the fundamental theory of single-photon image classification. This scheme realizes the operation of the mixed national institute of standards and technology database (MNIST) quantum image localization by a learned transformation for non-noise condition, noisy condition, and environmental attack condition, respectively. Compared with the regular use of entanglement between multi-qubits and low-temperature noise reduction conditions for image localization, the advantage of this method is that it does not deliberately require low temperature and entanglement resources, and it improves the lower bound of the localization success rate. This method paves a way to study quantum computer vision.

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An enhanced learning algorithm with a particle filter-based gradient descent optimizer method

Cited by 14 publications

References 28 publications

Relationship Model between Human Resource Management Activities and Performance Based on LMBP Algorithm

Relationship Model between Human Resource Management Activities and Performance Based on LMBP Algorithm

Deep Neural Learning Adaptive Sequential Monte Carlo for Automatic Image and Speech Recognition

An Image Localization System Based on Single Photon

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