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Bin Wang

Bio: Bin Wang is an academic researcher from Central South University. The author has contributed to research in topics: Regression analysis & Backpropagation. The author has an hindex of 2, co-authored 2 publications receiving 16 citations.

Papers
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Proceedings ArticleDOI
Bin Wang1, Bin Yang1, Jinfang Sheng1, Mengsheng Chen, Guoqiang He 
23 Jan 2009
TL;DR: The proposed conjugate gradient algorithm has global convergence, optimizes the learning steps using new line search rules and improves the convergence speed, and the new algorithm is applied in the cost prediction of actual sintering production.
Abstract: This paper studies various training algorithms of BP neural network and proposes an improved conjugate gradient algorithm which combines conjugate gradient algorithm with inexact line search route based on generalized Curry principle. The proposed algorithm has global convergence, optimizes the learning steps using new line search rules and improves the convergence speed. The new algorithm is applied in the cost prediction of actual sintering production. Simulation results show that the algorithm has better convergence compared with traditional conjugate gradient algorithms. The MSE of prediction is 0.0098 and accuracy rate reaches 94.31%.

14 citations

Proceedings ArticleDOI
Bin Wang1, Yan Fang1, Jinfang Sheng1, Weihua Gui1, Ying Sun 
23 Jan 2009
TL;DR: A hybrid BTP prediction model is presented which is based on artificial neural network and multi-linear regression error compensation algorithm and calculates the final prediction result based on both the prediction value from ANN and the compensation value from linear regression model.
Abstract: In this paper, data mining technology is adopted to find correlations from massive production data to predict Burning Through Point (BTP) of sintering process. A hybrid BTP prediction model is presented which is based on artificial neural network and multi-linear regression error compensation algorithm. In this model, the final prediction result is calculated based on both the prediction value from ANN model and the compensation value from linear regression model. Compared with the pure ANN model, the maximal prediction error is reduced from 3.85 meter to 1.3 meter and the prediction errors are kept in the range of 1 meter with the probability of 98.89%. Finally, the prediction values and correlated variables are sent back to L1 system through BTP control model to realize a closed-loop control system.

5 citations


Cited by
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Journal ArticleDOI
Min Wu1, Xiaoxia Chen1, Weihua Cao1, Jinhua She1, Chunsheng Wang1 
TL;DR: In this paper, an intelligent integrated optimization system (IIOS) was developed for the proportioning step, which contains two phases: the first and second proportionings, and the predicted quality indices were fed back to the optimizations of the first two proportionings to find feasible optimal dosing schemes.

37 citations

Journal ArticleDOI
TL;DR: In this paper, an integrated neural-network-based model for predicting the burn-through point (BTP) of a lead-zinc sintering process was presented.

21 citations

Journal ArticleDOI
TL;DR: The proposed method provides a valid reference to control the stable operation of the iron ore sintering process and can effectively predict the fluctuation interval of the BTP, and then successfully recognize the operating mode.
Abstract: The operating mode is an essential factor affecting product quality and yield of the sinter ore, which inspires the realization of operating mode recognition. Taking burn-through point (BTP) as the decision parameter of operating mode, an operating mode recognition method based on the fluctuation interval prediction is presented. First, combining the principal component analysis and the fuzzy information granulation method, a fluctuation interval prediction model of the BTP is established through utilizing the Elman neural network. Then, the operating mode classification rules are built according to the data distribution of the BTP in the fluctuation interval. Finally, experiments are executed with the data collected from a factory. The results indicate that it can effectively predict the fluctuation interval of the BTP, and then successfully recognize the operating mode. In this article, the proposed method provides a valid reference to control the stable operation of the iron ore sintering process.

9 citations

Journal ArticleDOI
TL;DR: In this article , a multistep prediction model, called denoising spatial-temporal encoder-decoder, is developed to predict the burn-through point (BTP) in a sintering process.
Abstract: Sinter ore is the main raw material of the blast furnace, and burn-through point (BTP) has a direct influence on the yield, quality, and energy consumption of the ironmaking process. Since iron ore sintering is a very complex industrial process with strong nonlinearity, multivariable coupling, random noises, and time variation, traditional soft-sensor models are hard to learn the knowledge of the sintering process. In this article, a multistep prediction model, called denoising spatial–temporal encoder–decoder, is developed to predict BTP in advance. First, the mechanism analysis is carried out to determine the relevant-BTP variables, and the BTP prediction is defined as a sequence-to-sequence modeling problem. Second, motivated by the random noises of industrial data, a denoising gated recurrent unit (DGRU) is designed to alleviate the impact of noise by adding a denoising gate into the GRU. In this case, the encoder with DGRU can better extract the latent variables of original sequence data. Then, spatial–temporal attention is embedded into the decoder to simultaneously capture the time-wise and variable-wise correlations between the latent variables and the target variable BTP. Finally, the experimental results on the real-world dataset of a sintering process demonstrated that the integrated multistep prediction model is effective and feasible.

7 citations