A prediction method for deck-motion of air-carrier based on PSO-KELM

Liu, Xixiang; Huang, Yongjiang; Wang, Qiming; Song, Qing; Zhao, Liye

doi:10.1109/icsenst.2016.7796310

Cited by 3 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…KELM is a new kind of single hidden layer forward learning algorithm derived from ELM theory [11]. The Compared with the traditional ELM, KELM obtains better regression prediction accuracy by introducing kernel functions.…”

Section: Review Of Kelmmentioning

confidence: 99%

A Direct Torque Control of Permanent Magnet Synchronous Motor for Electric Vehicles Based on ADRC Optimized by CKMTOA-KELM

Yi¹,

Zhang²,

Jia³

et al. 2018

dteees

View full text Add to dashboard Cite

Electric vehicles are considered as a new generation of transport to solve the energy crisis. Permanent magnet synchronous motor (PMSM) has been widely used in electric vehicle drive system. A new direct torque control (DTC) for PMSM based on active-disturbance rejection control (ADRC) optimized by improved kernel extreme learning machine (KELM) method is proposed in this paper, which aims to overcome the defects of traditional PI controller. The CKMTOA-KELM optimal regression model is obtained by using chaotic kinetic molecular theory optimization algorithm (CKMTOA) to optimize the kernel parameters and penalty coefficients of KELM regression model. CKMTOA uses chaos search to prevent the algorithm from falling into local optimum and improves the convergence rate by employing adaptive inertia weighting factor. Finally, the ADRC controller embedded the CKMTOA-KELM optimal regression model is analyzed and optimized to improve the dynamic response speed and anti-jamming capability of the system and enhance the robustness of the system. The simulation and experiment results have verified the feasibility and effectiveness of this method.

show abstract

Section: Review Of Kelmmentioning

confidence: 99%

A Direct Torque Control of Permanent Magnet Synchronous Motor for Electric Vehicles Based on ADRC Optimized by CKMTOA-KELM

Yi¹,

Zhang²,

Jia³

et al. 2018

dteees

View full text Add to dashboard Cite

show abstract

“…However, there are issues that must be considered regarding the actual number of hidden nodes included in an ANN [13]. Liu et al introduced a prediction method based on extreme learning machine, support vector machine and particle swarm optimization (PSO-KELM), which has a simple structure, fast training speed and simulation results showed high accuracy prediction [14]. Luo et al adopted support vector machine for the parametric identification of ship coupled heave and pitch motions with real oceanic conditions [15].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid BPNN-GARF-SVR Prediction Model Based on EEMD for Ship Motion

Han¹,

Wang²

2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

Accurate prediction of ship motion is very important for ensuring marine safety, weapon control, and aircraft carrier landing, etc. Ship motion is a complex time-varying nonlinear process which is affected by many factors. Time series analysis method and many machine learning methods such as neural networks, support vector machines regression (SVR) have been widely used in ship motion predictions. However, these single models have certain limitations, so this paper adopts a multi-model prediction method. First, ensemble empirical mode decomposition (EEMD) is used to remove noise in ship motion data. Then the random forest (RF) prediction model optimized by genetic algorithm (GA), back propagation neural network (BPNN) prediction model and SVR prediction model are respectively established, and the final prediction results are obtained by results of three models. And the weights coefficients are determined by the correlation coefficients, reducing the risk of prediction and improving the reliability. The experimental results show that the proposed combined model EEMD-GARF-BPNN-SVR is superior to the single predictive model and more reliable. The mean absolute percentage error (MAPE) of the proposed model is 0.84%, but the results of the single models are greater than 1%.

show abstract

“…Model optimization has been proven to improve the performance of wind speed forecasting models. Much of heuristic optimization algorithms have been integrated into hybrid models for model improvement [ [35]- [36]]. Many good examples can be found in this method.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, the performance of the SVM and KELM rely on the choice of hyperparameters highly. So these models usually are optimized by a heuristic optimization algorithm, like PSO-KELM [36], PSO-SVM [37]. Reference [35] applied GSA to synchronously optimize hyperparameters in a hybrid model.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Deep Interval Prediction Model for Wind Speed Forecasting

Chen

et al. 2021

IEEE Access

View full text Add to dashboard Cite

How to predict wind speed with high accuracy is a fundamental issue for the generation of wind power and energy management of power systems. Moreover, the nonlinear and non-stationary characteristics of wind speed make the task extremely challenging. To resolve this issue, a novel hybrid interval prediction model based on long short-term memory (LSTM) networks and variational mode decomposition (VMD) algorithm is developed in the frame of lower upper bound estimation in this study. Firstly, VMD is applied to decompose sequences of raw wind speed into several different modal components containing different information. Then, a LSTM-based model for each component is built to predict the corresponding wind speed information. In the LSTM structure, lower and upper bounds of the prediction intervals (PIs) are constructed and two customized cost functions based on the probability of convergence and width of the PIs are proposed to optimize the model with gradient descent algorithm. Additionally, particle swarm optimization (PSO) is applied to optimize the hyperparameters in the two cost functions of each LSTM. Finally, experiments with different scales of datasets were conducted to show the feasibility and effectiveness of the proposed method by comparing it with traditional methods. The results demonstrate that the proposed model yielded significantly superior performance with a clear enhancement in the PI quality.INDEX TERMS Interval prediction, wind speed forecasting, long short-term memory networks, variational mode decomposition, lower upper bound estimation, particle swarm optimization, customized cost function.

show abstract

A prediction method for deck-motion of air-carrier based on PSO-KELM

Cited by 3 publications

References 5 publications

A Direct Torque Control of Permanent Magnet Synchronous Motor for Electric Vehicles Based on ADRC Optimized by CKMTOA-KELM

A Direct Torque Control of Permanent Magnet Synchronous Motor for Electric Vehicles Based on ADRC Optimized by CKMTOA-KELM

A Hybrid BPNN-GARF-SVR Prediction Model Based on EEMD for Ship Motion

A Hybrid Deep Interval Prediction Model for Wind Speed Forecasting

Contact Info

Product

Resources

About