Modeling error PDF optimization based wavelet neural network modeling of dynamic system and its application in blast furnace ironmaking

Zhou, Ping; Wang, Chenyu; Li, Mingjie; Wang, Hong; Wu, Yongjian; Chai, Tianyou

doi:10.1016/j.neucom.2018.01.040

Title: Modeling error PDF optimization based wavelet neural network modeling of dynamic system and its application in blast furnace ironmaking

Full Record
Other Related Research

Abstract

In general, the modeling errors of dynamic system model are a set of random variables. The traditional performance index of modeling such as means square error (MSE) and root means square error (RMSE) can not fully express the connotation of modeling errors with stochastic characteristics both in the dimension of time domain and space domain. Therefore, the probability density function (PDF) is introduced to completely describe the modeling errors in both time scales and space scales. Based on it, a novel wavelet neural network (WNN) modeling method is proposed by minimizing the two-dimensional (2D) PDF shaping of modeling errors. First, the modeling error PDF by the tradional WNN is estimated using data-driven kernel density estimation (KDE) technique. Then, the quadratic sum of 2D deviation between the modeling error PDF and the target PDF is utilized as performance index to optimize the WNN model parameters by gradient descent method. Since the WNN has strong nonlinear approximation and adaptive capability, and all the parameters are well optimized by the proposed method, the developed WNN model can make the modeling error PDF track the target PDF, eventually. Simulation example and application in a blast furnace ironmaking process show that the proposed method hasmore »« less

Authors:

; Wang, Chenyu; Li, Mingjie; Wang, Hong; Wu, Yongjian; Chai, Tianyou

Publication Date:: 2018-04-01

Research Org.:: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1440618

Report Number(s):: PNNL-SA-130982
Journal ID: ISSN 0925-2312

DOE Contract Number:: AC05-76RL01830

Resource Type:: Journal Article

Journal Name:: Neurocomputing

Additional Journal Information:: Journal Volume: 285; Journal Issue: C; Journal ID: ISSN 0925-2312

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: Steel making; Modelling; Probability Density Function method

Citation Formats


                    Zhou, Ping, Wang, Chenyu, Li, Mingjie, Wang, Hong, Wu, Yongjian, and Chai, Tianyou. Modeling error PDF optimization based wavelet neural network modeling of dynamic system and its application in blast furnace ironmaking.  United States: N. p., 2018. 
        Web.  doi:10.1016/j.neucom.2018.01.040.

Copy to clipboard


                    Zhou, Ping, Wang, Chenyu, Li, Mingjie, Wang, Hong, Wu, Yongjian, & Chai, Tianyou. Modeling error PDF optimization based wavelet neural network modeling of dynamic system and its application in blast furnace ironmaking.  United States.  https://doi.org/10.1016/j.neucom.2018.01.040

Copy to clipboard


                    Zhou, Ping, Wang, Chenyu, Li, Mingjie, Wang, Hong, Wu, Yongjian, and Chai, Tianyou. 2018.  
        "Modeling error PDF optimization based wavelet neural network modeling of dynamic system and its application in blast furnace ironmaking".  United States.  https://doi.org/10.1016/j.neucom.2018.01.040.

Copy to clipboard


                    
@article{osti_1440618,

  title        = {Modeling error PDF optimization based wavelet neural network modeling of dynamic system and its application in blast furnace ironmaking},

  author       = {Zhou, Ping and Wang, Chenyu and Li, Mingjie and Wang, Hong and Wu, Yongjian and Chai, Tianyou},

  abstractNote = {In general, the modeling errors of dynamic system model are a set of random variables. The traditional performance index of modeling such as means square error (MSE) and root means square error (RMSE) can not fully express the connotation of modeling errors with stochastic characteristics both in the dimension of time domain and space domain. Therefore, the probability density function (PDF) is introduced to completely describe the modeling errors in both time scales and space scales. Based on it, a novel wavelet neural network (WNN) modeling method is proposed by minimizing the two-dimensional (2D) PDF shaping of modeling errors. First, the modeling error PDF by the tradional WNN is estimated using data-driven kernel density estimation (KDE) technique. Then, the quadratic sum of 2D deviation between the modeling error PDF and the target PDF is utilized as performance index to optimize the WNN model parameters by gradient descent method. Since the WNN has strong nonlinear approximation and adaptive capability, and all the parameters are well optimized by the proposed method, the developed WNN model can make the modeling error PDF track the target PDF, eventually. Simulation example and application in a blast furnace ironmaking process show that the proposed method has a higher modeling precision and better generalization ability compared with the conventional WNN modeling based on MSE criteria. Furthermore, the proposed method has more desirable estimation for modeling error PDF that approximates to a Gaussian distribution whose shape is high and narrow.},

  doi          = {10.1016/j.neucom.2018.01.040},

  url          = {https://www.osti.gov/biblio/1440618},
  journal      = {Neurocomputing},

  issn         = {0925-2312},

  number       = C,

  volume       = 285,

  place        = {United States},

  year         = {2018},

  month        = {4}

}

Copy to clipboard

Journal Article:

https://doi.org/10.1016/j.neucom.2018.01.040

Other availability

Find in Google Scholar

Search WorldCat to find libraries that may hold this journal

Save / Share:

Export Metadata

Save to My Library

Similar records in OSTI.GOV collections:

Modeling error PDF optimization based wavelet neural network modeling of dynamic system and its application in blast furnace ironmaking

Journal Article Zhou, Ping; Wang, Chenyu; Li, Mingjie; ... - Neurocomputing

In general, the modeling errors of dynamic system model are a set of random variables. The traditional performance index of modeling such as means square error (MSE) and root means square error (RMSE) cannot fully express the connotation of modeling errors with stochastic characteristics both in the dimension of time domain and space domain. Therefore, the probability density function (PDF) is introduced to completely describe the modeling errors in both time scales and space scales. Based on it, a novel wavelet neural network (WNN) modeling method is proposed by minimizing the two-dimensional (2D) PDF shaping of modeling errors. First, themore »« less
Cited by 16
https://doi.org/10.1016/J.NEUCOM.2018.01.040

Full Text Available
Data-Driven Robust M-LS-SVR-Based NARX Modeling for Estimation and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking

Journal Article Zhou, Ping; Guo, Dongwei; Wang, Hong; ... - IEEE Transactions on Neural Networks and Learning Systems

Optimal operation of an industrial blast furnace (BF) ironmaking process largely depends on a reliable measurement of molten iron quality (MIQ), which is not feasible using the conventional sensors. This paper proposes a novel data-driven robust modeling method for online estimation and control of MIQ indices. First, to completely capture the nonlinear dynamics of the BF process, the nonlinear autoregressive exogenous (NARX) model is constructed for the MIQ indices. Then, considering that the standard least-squares support vector regression (LS-SVR) cannot directly cope with the multi-output problem, the multi- task transfer learning is proposed to design a novel multi-output LS-SVR (M-LS-SVR)more »« less
https://doi.org/10.1109/TNNLS.2017.2749412
Data-Driven Robust M-LS-SVR-Based NARX Modeling for Estimation and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking

Journal Article Zhou, Ping; Guo, Dongwei; Wang, Hong; ... - IEEE Transactions on Neural Networks and Learning Systems

Optimal operation of an industrial blast furnace (BF) ironmaking process largely depends on a reliable measurement of molten iron quality (MIQ) indices, which are not feasible using the conventional sensors. This paper proposes a novel data-driven robust modeling method for the online estimation and control of MIQ indices. First, a nonlinear autoregressive exogenous (NARX) model is constructed for the MIQ indices to completely capture the nonlinear dynamics of the BF process. Then, considering that the standard least-squares support vector regression (LS-SVR) cannot directly cope with the multioutput problem, a multitask transfer learning is proposed to design a novel multioutput LS-SVRmore »« less
Cited by 63
https://doi.org/10.1109/TNNLS.2017.2749412

Full Text Available
Magnetic resonance imaging-targeted, 3D transrectal ultrasound-guided fusion biopsy for prostate cancer: Quantifying the impact of needle delivery error on diagnosis

Journal Article Cool, Derek; Romagnoli, Cesare; Fenster, Aaron; ... - Medical Physics

Purpose: Magnetic resonance imaging (MRI)-targeted, 3D transrectal ultrasound (TRUS)-guided “fusion” prostate biopsy intends to reduce the ∼23% false negative rate of clinical two-dimensional TRUS-guided sextant biopsy. Although it has been reported to double the positive yield, MRI-targeted biopsies continue to yield false negatives. Therefore, the authors propose to investigate how biopsy system needle delivery error affects the probability of sampling each tumor, by accounting for uncertainties due to guidance system error, image registration error, and irregular tumor shapes. Methods: T2-weighted, dynamic contrast-enhanced T1-weighted, and diffusion-weighted prostate MRI and 3D TRUS images were obtained from 49 patients. A radiologist and radiologymore »« less
https://doi.org/10.1118/1.4883838
Accurate modeling of switched reluctance machine based on hybrid trained WNN

Journal Article Song, Shoujun; Ge, Lefei; Ma, Shaojie; ... - AIP Advances

According to the strong nonlinear electromagnetic characteristics of switched reluctance machine (SRM), a novel accurate modeling method is proposed based on hybrid trained wavelet neural network (WNN) which combines improved genetic algorithm (GA) with gradient descent (GD) method to train the network. In the novel method, WNN is trained by GD method based on the initial weights obtained per improved GA optimization, and the global parallel searching capability of stochastic algorithm and local convergence speed of deterministic algorithm are combined to enhance the training accuracy, stability and speed. Based on the measured electromagnetic characteristics of a 3-phase 12/8-pole SRM, themore »« less
https://doi.org/10.1063/1.4873535

Similar Records