Prediction of amorphous forming ability based on artificial neural network and convolutional neural network

Lu, Fei; Liang, Yongchao; Wang, Xingying; Gao, Tinghong; Chen, Qian; Liu, Yunchun; Zhou, Yu; Yuan, Yongkai; Liu, Yutao

doi:10.1016/j.commatsci.2022.111464

Cited by 32 publications

(10 citation statements)

References 27 publications

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“…A variety of factors directly or indirectly have an impact on stock prices. Determined features using a CNN can assist our framework in learning patterns and relations among data more effectively compared with conventional methods (Lu et al, 2022). Further, an extreme learning machine is selected as the meta‐classifier.…”

Section: Methods and The Proposed Vmd‐denetworkmentioning

confidence: 99%

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Time series forecasting based on a novel ensemble‐based network and variational mode decomposition

Nazarieh

Dehkordi

2023

Expert Systems

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Statistical and computational intelligence methods contain weaknesses in handling nonlinearity, non-stationarity and noise. This research develops a novel decomposition ensemble-based network named VMD-DENetwork for time series forecasting over different horizons. A robust decomposition technique called variational mode decomposition (VMD) is applied to decompose the input sequence into several intrinsic modes in a non-recursive manner. The optimal number of intrinsic modes is selected based on a comprehensive analysis to ensure the stability of the framework. The proposed DENetwork is developed based on stacking architecture and constitutes heterogeneous learners to model the nonlinear and complex relationships. It combines a convolutional neural network, long short-term memory and an extreme learning machine. A firefly optimization algorithm is adopted for utilizing hyperparameters of the proposed model to enhance the efficiency of VMD-DENetwork. The forecasting performance is verified by using six real-world data sets from the New York Mercantile and International Petroleum Exchange. The final obtained results are compared with several peer-advanced algorithms using the root mean squared error (RMSE), mean absolute error (MAE), Theil inequality coefficient (TIC) and correlation coefficient (R) metrics. The experimental results confirm that the proposed model demonstrates outstanding prediction performance. The employed optimization algorithm is compared with three frequently used bio-inspired optimization algorithms, and their performance is tested using standard CEC benchmarks.

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Section: Methods and The Proposed Vmd‐denetworkmentioning

confidence: 99%

“…When the simpler subseries are extracted, a novel deep learning ensemble-based model is used for price forecasting. conventional methods (Lu et al, 2022). Further, an extreme learning machine is selected as the meta-classifier.…”

Section: Vmd-denetwork Forecasting Methodsmentioning

confidence: 99%

Time series forecasting based on a novel ensemble‐based network and variational mode decomposition

Nazarieh

Dehkordi

2023

Expert Systems

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“…The BP neural network, a widely adopted intelligent algorithm renowned for data fitting and prediction, constitutes the focal point of this study. Specifically, the selected feed-forward neural network architecture encompasses an input layer, hidden layer, and output layer, as detailed in references [22,23]. To facilitate effective model training, the input values, namely shot velocity, impact angle of shot, and shot diameter, are meticulously partitioned into training, validation, and test sets, with proportions set at 75%, 15%, and 15%, respectively.…”

Section: Neural Network Modeling For Shot Peening Performance Predict...mentioning

confidence: 99%

Optimization of parameters for the best shot peening effect based on surface response and neural network model

Wang,

Kim

2024

Mater. Res. Express

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To optimize the peening effect of different shot peening process parameters on metal surfaces, the mapping relationship between different shot peening process parameters and metal surface integrity was obtained. In this paper, ABAQUS software was used to establish a DE-FE (Discrete element-Finite element) random multi-shot analysis model to simulate shot peening, then optimize the shot peening process parameters based on the surface response method(RSM), and finally validate it through experiments and BP(back propagation) neural network model. The result shows that when the shot velocity is 70 m/s, the impact angle of shot is 61.45°, and the shot diameter is 0.78 mm, the shot peening effect is the best, the surface roughness value is reduced by 101.84%, and the arc height value is increased by 54.66%; the error between the predicted results of BP neural network and the results of numerical analysis is less than 8%. Therefore, the optimized process parameters significantly improve the shot peening effect, but also shows that the BP neural network prediction model can more accurately predict the mapping relationship between the input parameters of shot velocity, shot diameter, and impact angle of shot and the output parameters of roughness value and arc height value.

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“…In deep learning, compared with other network layers, convolutional layers are better at image processing and can extract local feature information from images [17]. As indicated [18], convolutional layers have the properties of translation invariance and inductive reasoning.…”

Section: Flat Website Design Element Identification Systemmentioning

confidence: 99%

Interface Design and Optimization Methods of Government Websites from a Flat Perspective

Zhong¹,

Liu

2022

Discrete Dynamics in Nature and Society

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Flat website design has become more and more popular in recent years due to its simplicity and elegance. Nevertheless, the current flat design thinking is mainly applied to conventional websites such as corporate websites. Recently, the increase in public demands has significantly increased the browsing frequency of government websites. To optimize the government website, this study establishes a government website design framework from the perspective of flattening. The established framework can extract flat design elements from the massive flat website design schemes in the past. Based on flat design elements, the particle swarm algorithm (PSO) algorithm in the established framework realizes the design and optimization of government websites. Subsequently, the performance of the CNN, which is used as the fitness function of the PSO algorithm, in predicting public acceptance is analyzed with a case study. The case results show that the average relative errors between the prediction results predicted by CNN and the real public acceptance in two provinces are 2.5416% and 1.4788%, respectively, which indicates that the predicted and real results are in good agreement. Moreover, the linear correlation coefficients between predicted and real public acceptance are 0.9553 and 0.9937, respectively, which further indicates that CNN is reliable as a fitness function of the PSO algorithm. Therefore, it is feasible to use the PSO algorithm to optimize the government website design scheme.

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Prediction of amorphous forming ability based on artificial neural network and convolutional neural network

Cited by 32 publications

References 27 publications

Time series forecasting based on a novel ensemble‐based network and variational mode decomposition

Time series forecasting based on a novel ensemble‐based network and variational mode decomposition

Optimization of parameters for the best shot peening effect based on surface response and neural network model

Interface Design and Optimization Methods of Government Websites from a Flat Perspective

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