Nonlinear Time Series Prediction Model Based on Particle Swarm Optimization B-spline Network

Kong, Lingjiang; Gong, Xiaolong; Yuan, Chuanlai; Xiao, Huaibao; Liu, Jianhua

doi:10.1016/j.ifacol.2018.09.421

Cited by 5 publications

(3 citation statements)

References 2 publications

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“…Liu et al [26] proposed a coupled thermal-hydraulic-mechanical nonlinear model for predict and warn of water inrush in mines. Finally, Kong et al [27] proposed a fault diagnosis model using a particle swarm optimization algorithm to optimize the structure of a B-spline network, aiming to improve the prediction accuracy of nonlinear time series.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing LightGBM for Industrial Fault Warning: An Innovative Hybrid Algorithm

Li,

Jin,

Dogani

et al. 2024

Processes

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The reliable operation of industrial equipment is imperative for ensuring both safety and enhanced production efficiency. Machine learning technology, particularly the Light Gradient Boosting Machine (LightGBM), has emerged as a valuable tool for achieving effective fault warning in industrial settings. Despite its success, the practical application of LightGBM encounters challenges in diverse scenarios, primarily stemming from the multitude of parameters that are intricate and challenging to ascertain, thus constraining computational efficiency and accuracy. In response to these challenges, we propose a novel innovative hybrid algorithm that integrates an Arithmetic Optimization Algorithm (AOA), Simulated Annealing (SA), and new search strategies. This amalgamation is designed to optimize LightGBM hyperparameters more effectively. Subsequently, we seamlessly integrate this hybrid algorithm with LightGBM to formulate a sophisticated fault warning system. Validation through industrial case studies demonstrates that our proposed algorithm consistently outperforms advanced methods in both prediction accuracy and generalization ability. In a real-world water pump application, the algorithm we proposed achieved a fault warning accuracy rate of 90%. Compared to three advanced algorithms, namely, Improved Social Engineering Optimizer-Backpropagation Network (ISEO-BP), Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN), and Grey Wolf Optimizer-Light Gradient Boosting Machine (GWO-LightGBM), its Root Mean Square Error (RMSE) decreased by 7.14%, 17.84%, and 13.16%, respectively. At the same time, its R-Squared value increased by 2.15%, 7.02%, and 3.73%, respectively. Lastly, the method we proposed also holds a leading position in the success rate of a water pump fault warning. This accomplishment provides robust support for the timely detection of issues, thereby mitigating the risk of production interruptions.

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Section: Literature Reviewmentioning

confidence: 99%

Enhancing LightGBM for Industrial Fault Warning: An Innovative Hybrid Algorithm

Li,

Jin,

Dogani

et al. 2024

Processes

View full text Add to dashboard Cite

show abstract

“…In [28], the scheme of the RBF network optimized by the particle swarm optimization algorithm was proposed. The particle swarm optimization (PSO) algorithm helps to improve the learning ability of the RBF network, and effectively expands the processing ability of the RBF network for ambiguity and inconsistency.…”

Section: Related Workmentioning

confidence: 99%

A BP Neural Network Prediction Model Based on Dynamic Cuckoo Search Optimization Algorithm for Industrial Equipment Fault Prediction

et al. 2019

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The fault prediction problem for modern industrial equipment is a hot topic in current research. So, this paper first proposes a dynamic cuckoo search algorithm. The algorithm improves the step size and discovery probability. Then, it introduces the change trend of fitness function value into the step size update formula to balance the search speed and accuracy. At the same time, the algorithm initial global search step is larger, while the step size of the local search is smaller in the latter part of the algorithm. In the process of discovering the global optimal solution, the probability of preserving the offspring with good fitness is increased, and the uncertainty of preference random walk is improved. As the search progresses, the probability of discovery is reduced, which makes it easy to produce new individuals in the later stage of evolution. Based on this, a BP neural network prediction model based on dynamic cuckoo search algorithm optimization is established. And the experimental results show that the proposed prediction model has faster convergence and higher accuracy.INDEX TERMS IWSN, fault prediction, BP neural network, dynamic cuckoo search.

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“…[17] considered Buys-Ballot and classical methods of decomposing to estimate the cubic trend as well as other components of the times series and obtained the chain base and fixed base estimators with their statistical properties. [19] proposed a particle swarm optimization B-spline network to improve the prediction accuracy of non-linear time series. They adopted a forecasting error square sum to evaluate the training effect of the B-spline network.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Unit Root Test for AR(1) Model with Trend Approximated

Kumar¹,

Varun²,

Kumar³

et al. 2020

Stat., optim. inf. comput.

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The objective of present study is to develop a time series model for handling the non-linear trend process using a spline function. Spline function is a piecewise polynomial segment concerning the time component. The main advantage of spline function is the approximation, non linear time trend, but linear time trend between the consecutive join points. A unit root hypothesis is projected to test the non stationarity due to presence of unit root in the proposed model. In the autoregressive model with linear trend, the time trend vanishes under the unit root case. However, when non-linear trend is present and approximated by the linear spline function, through the trend component is absent under the unit root case, but the intercept term makes a shift with r knots. For decision making under the Bayesian perspective, the posterior odds ratio is used for hypothesis testing problems. We have derived the posterior probability for the assumed hypotheses under appropriate prior information. A simulation study and an empirical application are presented to examine the performance of theoretical outcomes.

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Nonlinear Time Series Prediction Model Based on Particle Swarm Optimization B-spline Network

Cited by 5 publications

References 2 publications

Enhancing LightGBM for Industrial Fault Warning: An Innovative Hybrid Algorithm

Enhancing LightGBM for Industrial Fault Warning: An Innovative Hybrid Algorithm

A BP Neural Network Prediction Model Based on Dynamic Cuckoo Search Optimization Algorithm for Industrial Equipment Fault Prediction

Bayesian Unit Root Test for AR(1) Model with Trend Approximated

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