Reliability prediction-based improved dynamic weight particle swarm optimization and back propagation neural network in engineering systems

Bai, Baojun; Zhang, Junyi; Wu, Xuan; Zhu, Guang Wei; Li, Xinye

doi:10.1016/j.eswa.2021.114952

Cited by 55 publications

(16 citation statements)

References 47 publications

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“…Turbocharges in diesel engine Car engines (data from [17]) Values of Reliability Index and probability of failure are the same as those obtained with other methods [30] Model SDWPSO-BPNN (hybrid of dynamic weight particle swarm optimisation-based sine map and back propagation neural network) Turbocharges in diesel engine (data from [17]) and industrial robot systems NRMSE = 6.9 × 10 −5 for turbocharges NRMSE = 2.3 × 10 −6 for industrial robot systems [21] Cascade feedforward neural network CNC machine tool spindles MAPE: 1.56-2.53% [22] ANN supported stochastic process Simulated data and degradation dataset of spindle systems Mean absolute relative error < 0.13 for all samples of real degradation data [24] ANN supported Wiener process Simulated data and degradation dataset of spindle systems Mean absolute relative error < 0.06 for all samples of real degradation data [25] Model based on convolutional neural networks…”

Section: Introductionmentioning

confidence: 64%

“…The first was developed as a hybrid of artificial neural networks and genetic algorithms, for which the magnitude of the time series delay was determined using the Shannon entropy. The smallest error, close to zero, for the datasets from the article [17] was obtained by the one presented in the paper [21]. Its design is based on a hybridisation of dynamic weight particle swarm optimisation-based sine map and back propagation neural network.…”

Section: Introductionmentioning

confidence: 99%

“…A significant improvement in prediction accuracy occurred for models presented in publications [20,21]. The first was developed as a hybrid of artificial neural networks and genetic algorithms, for which the magnitude of the time series delay was determined using the Shannon entropy.…”

Section: Introductionmentioning

confidence: 99%

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Reliability Analysis of Military Vehicles Based on Censored Failures Data

2022

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The paper proposes a methodology of reliability testing as applied to vehicles used in military transport systems. After estimating the value of the reliability function using the Kaplan–Meier estimator, reliability models were developed and analysed. The neural model, which achieved the value of the correlation coefficient R exceeding 0.99, was determined to fit the empirical data the best. On the basis of the approximated reliability function of several models, the reliability characteristics of the tested sample of vehicles were determined. Plots of the failure probability density function for all three models had similar courses over a significant part of the function domain. A failure intensity function was also determined, which varied between models. For the exponential and Weibull model, the expected mileage between failures was calculated, which proved impossible for the neural model. The proposed methodology is capable of modelling reliability characteristics based on the observation of an assumed period of the exploitation process of the selected group of military vehicles.

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Section: Introductionmentioning

confidence: 64%

Section: Introductionmentioning

confidence: 99%

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Reliability Analysis of Military Vehicles Based on Censored Failures Data

2022

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“…Bai et al [52] proposed a dynamic weight PSO-based sine map (SDWPSO) algorithm for optimising weights and biases of a backpropagation neural network (BPNN) for reliability prediction in engineering problems. A new position updating operation was proposed, where dynamic weights were used to adjust the proportions of contributions of the current position, the new velocity and the global best solution for position updating.…”

Section: Variants Of Particle Swarm Optimisationmentioning

confidence: 99%

An evolving ensemble model of multi-stream convolutional neural networks for human action recognition in still images

Slade

Zhang

et al. 2022

Neural Comput & Applic

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Still image human action recognition (HAR) is a challenging problem owing to limited sources of information and large intra-class and small inter-class variations which requires highly discriminative features. Transfer learning offers the necessary capabilities in producing such features by preserving prior knowledge while learning new representations. However, optimally identifying dynamic numbers of re-trainable layers in the transfer learning process poses a challenge. In this study, we aim to automate the process of optimal configuration identification. Specifically, we propose a novel particle swarm optimisation (PSO) variant, denoted as EnvPSO, for optimal hyper-parameter selection in the transfer learning process with respect to HAR tasks with still images. It incorporates Gaussian fitness surface prediction and exponential search coefficients to overcome stagnation. It optimises the learning rate, batch size, and number of re-trained layers of a pre-trained convolutional neural network (CNN). To overcome bias of single optimised networks, an ensemble model with three optimised CNN streams is introduced. The first and second streams employ raw images and segmentation masks yielded by mask R-CNN as inputs, while the third stream fuses a pair of networks with raw image and saliency maps as inputs, respectively. The final prediction results are obtained by computing the average of class predictions from all three streams. By leveraging differences between learned representations within optimised streams, our ensemble model outperforms counterparts devised by PSO and other state-of-the-art methods for HAR. In addition, evaluated using diverse artificial landscape functions, EnvPSO performs better than other search methods with statistically significant difference in performance.

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“…The algorithm is distinguished by its simple process, random initialization, and fewer control parameters, demonstrating strong optimization ability [13]. PSO became the most popular methods for the advantages of easy implementation, which had high precision and fast convergence among numerous of metaheuristic algorithms [14]. According to another study, PSO is one of the most researched populationbased stochastic optimization algorithms [15].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Particle Swarm Optimization (PSO) to Improve Neural Network Performance in Univariate Time Series Prediction

Tyas¹,

Setianama²,

Fajriyah³

et al. 2021

KINETIK

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One of the oldest known predictive analytics techniques is time series prediction. The target in time series prediction is use historical data about a specific quantity to predicts value of the same quantity in the future. Multivariate time series (MTS) data has been widely used in time series prediction research because it is considered better than univariate time series (UTS) data. However, in reality MTS data sets contain various types of information which makes it difficult to extract information to predict the situation. Therefore, UTS data still has a chance to be developed because it is actually simpler than MTS data. UTS prediction treats forecasts as a single variable problem, whereas MTS may employ a large number of time-concurred series to make predictions. Neural Network (NN) model could be built to predict the target variable given the other (predictor) variables. In this study, we used Particle Swarm Optimization (PSO) algorithm to optimize performance of NN on a UTS dataset. Our proposed model is validated using x-validation and and use RMSE to measure its performance. The experimental results show that NN performance after optimization using PSO produces good results compared to classical NN performance. This is evidenced by the value of RMSE = 0.410 which is the smallest RMSE value produced. The smaller the RMSE value, the better the model performance. It can be concluded that the proposed method can improve NN performance on UTS data.

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Reliability prediction-based improved dynamic weight particle swarm optimization and back propagation neural network in engineering systems

Cited by 55 publications

References 47 publications

Reliability Analysis of Military Vehicles Based on Censored Failures Data

Reliability Analysis of Military Vehicles Based on Censored Failures Data

An evolving ensemble model of multi-stream convolutional neural networks for human action recognition in still images

Implementation of Particle Swarm Optimization (PSO) to Improve Neural Network Performance in Univariate Time Series Prediction

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