Neural network ensemble modeling for nosiheptide fermentation process based on partial least squares regression

Niu, Dapeng; Wang, Fuli; Zhang, Lingling; He, Dakuo; Jia, Mingxing

doi:10.1016/j.chemolab.2010.11.007

Cited by 24 publications

(14 citation statements)

References 18 publications

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“…For the three datasets, the average method resulted in the following vector of weights w = [0. 25 As can be seen from Tables III-V, the proposed IOWA fusion method exhibits an interesting behavior as it provides an NMSE very close to or better than the best estimator of the ensemble. It also outperformed the state-of-the-art method called residualbased correction [30], for the Tecator and wine datasets.…”

Section: Fusion With Induced Ordered Weighted Averaging Operatorsmentioning

confidence: 76%

A novel fusion approach based on induced ordered weighted averaging operators for chemometric data analysis

Alhichri

Bazi

Alajlan

et al. 2013

Journal of Chemometrics

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This paper proposes a novel approach for the estimation of spectroscopic data by combining the predictions of an ensemble of estimators using the induced ordered weighted averaging (IOWA) fusion operators. For ensemble generation, we use Gaussian process regression (GPR) and extreme learning machine (ELM) estimators associated with different kernels. To render the model selection issue of ELM as efficiently as in the GPR Bayesian estimation method, we develop an automatic solution based on the powerful differential evolution (DE) algorithm. During the fusion process, the IOWA operator needs two things: (1) an order‐inducing value; and (2) a way to determine its weights. For the order‐inducing value, we propose to use the residual of each estimated output value. Because we cannot compute the true residual, we explore the idea of estimating the residuals themselves by associating to each estimator of the ensemble a second estimator of the same kind called a residual estimator. To learn the weights associated with these nonlinear operators, the proposed method relies on the concept of prioritized aggregation, where we generate the weights directly from the estimated residuals. Experimental results obtained on three real spectroscopic datasets confirm the interesting capabilities of the proposed IOWA fusion method. Copyright © 2013 John Wiley & Sons, Ltd.

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Section: Fusion With Induced Ordered Weighted Averaging Operatorsmentioning

confidence: 76%

A novel fusion approach based on induced ordered weighted averaging operators for chemometric data analysis

Alhichri

Bazi

Alajlan

et al. 2013

Journal of Chemometrics

View full text Add to dashboard Cite

show abstract

“…As shown in Figure 1, the Elman NN consists of the context layer, input layer, hidden layer, and output layer [29][30][31]. 1 denotes the weight from the context layer to the hidden layer, W 2 denotes the weight from the input layer to the hidden layer, and W 3 denotes the weight from the hidden layer to the output layer.…”

Section: Quality Prediction Model Based On Elman Neural Network Ensemmentioning

confidence: 99%

“…Generally, optimization methods can overcome the deficiencies of NNs. Additionally, the NN ensemble is a technique that can significantly improve the generalization ability of NNs through training a number of NNs and then combining them ( [31], He and Cao, 2012). Generally, the most common methods for the NN ensemble model are simple averaging and weighted averaging for regression problems [34].…”

Section: Quality Prediction Model Based On Elman Neural Network Ensemmentioning

confidence: 99%

Quality Prediction Model Based on Novel Elman Neural Network Ensemble

Zhang

2019

Complexity

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In this paper, we propose a novel prediction algorithm based on an improved Elman neural network (NN) ensemble for quality prediction, thus achieving the quality control of designed products at the product design stage. First, the Elman NN parameters are optimized using the grasshopper optimization (GRO) method, and then the weighted average method is improved to combine the outputs of the individual NNs, where the weights are determined by the training errors. Simulations were conducted to compare the proposed method with other NN methods and evaluate its performance. The results demonstrated that the proposed algorithm for quality prediction obtained better accuracy than other NN methods. In this paper, we propose a novel Elman NN ensemble model for quality prediction during product design. Elman NN is combined with GRO to yield an optimized Elman network ensemble model with high generalization ability and prediction accuracy.

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“…18 The goal of the combination is not only to combine the selected individual learners' results as a final result of the ensemble but also to optimally realize the complementariness of the participants from their diversity. 18,19 Generally, the most common methods are simple averaging or weighted averaging 19 for regression problems and almost all the individuals are using the same input as the training data. 20 This article focuses on an indoor localization system based on an NN ensemble model.…”

Section: Introductionmentioning

confidence: 99%

A novel time difference of arrival localization algorithm using a neural network ensemble model

Zhang

Jiang

et al. 2018

International Journal of Distributed Sensor Networks

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In a localization system, time difference of arrival technique is widely used to estimate the location of a mobile station. To improve the performance of mobile station location estimation, a novel algorithm-based artificial neural network ensemble and time difference of arrival information is proposed in non-line-of-sight environments. Back propagation neural network is a classic artificial neural network and may be effectively used for mathematical modeling and prediction, and an artificial neural network ensemble has better generalization ability and stability than a single network. First, the parameters, such as the weights and biases of the single neural network are optimized by the ant lion optimization method which is novel and effective. Then four types of different information from the time difference of arrival measurements are respectively used to train the individual neural network. Finally, the weighted average method is improved to combine the outputs of the different individual neural network, where weights are determined by the training errors. The estimation accuracy of the locating system is evaluated through experimental measurements. The simulation results show that the proposed algorithm is efficient in improving the generalization ability and localization precision of the neural network ensemble model.

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Neural network ensemble modeling for nosiheptide fermentation process based on partial least squares regression

Cited by 24 publications

References 18 publications

A novel fusion approach based on induced ordered weighted averaging operators for chemometric data analysis

A novel fusion approach based on induced ordered weighted averaging operators for chemometric data analysis

Quality Prediction Model Based on Novel Elman Neural Network Ensemble

A novel time difference of arrival localization algorithm using a neural network ensemble model

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