An Improved Bagging Neural Network Ensemble Algorithm and Its Application

Chen, Ruqing; Yu, Junzhi

doi:10.1109/icnc.2007.207

Cited by 13 publications

(8 citation statements)

References 6 publications

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“…Bagging is a statistical resampling technique for generating training data for each individual in an ensemble model [35,36]. Each training dataset is drawn randomly with a replacement from the original training set.…”

Section: Nn Ensemble Model For Qualitymentioning

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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Section: Nn Ensemble Model For Qualitymentioning

confidence: 99%

Quality Prediction Model Based on Novel Elman Neural Network Ensemble

Zhang

2019

Complexity

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“…Standard PSO with linearly decreasing weight strategy (LDW-PSO) and constriction factor method PSO (CFM-PSO) algorithms are used to compare with the proposed algorithm. Four test complex functions are described as follows [6][7]:…”

Section: Simulation Studymentioning

confidence: 99%

Soft sensor modeling based on modified PSO

Chen

2012

2012 8th International Conference on Natural Computation

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Standard particle swarm optimization (PSO) has the drawback of trapping into local minima easily when used for the optimization of high-dimension complex functions with a lot of local minima. In order to deal with the problem an improved PSO algorithm with crossover operator is developed. Better particles are selected in this algorithm, thus can avoid premature convergence to local optimum as well as accelerate the convergence speed. Four high-dimension complex benchmark functions are introduced to test this method. Simulation analysis shows that improved PSO algorithm has better capabilities in convergence accuracy and speed as well as its global search performance by comparison with normal PSO algorithms. Finally the improved PSO based neural network (NN) soft sensor model for ethylene yield is developed, results of the application in industrial process control show that this model has high prediction precision and good generalization ability, it can satisfy the need of spot measurement.

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“…For example, He, Yang, and Kong used a genetic algorithm to determine weights of support vector machine based member classifiers of an ensemble [18]. For training neural network based member classifiers to construct an ensemble, Shen and Kong used a genetic algorithm to determine weights that would be given to votes from these member classifiers [19], while Chen and Yu used a particle swarm optimization based method to determine weights [20]. Kim et al proposed an approach to weight adjustment whose basic idea is to decrease the biases caused by the different quality levels of the data sets used to train member classifiers [12].…”

Section: Related Workmentioning

confidence: 99%

On Adjustment Functions for Weight-Adjusted Voting-Based Ensembles of Classifiers

Hsu

2014

JCP

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An ensemble of classifiers is a system consisting of multiple member classifiers which are trained individually and whose outcomes are aggregated into an overall outcome for a testing data instance. Voting is a common approach used to aggregate outcomes generated by member classifiers. Ensembles based on weighted voting have been studied for some time. However, the focus of most studies is more on weight assignment rather than on weight adjustment, whose basic idea is to increase the weights of votes from member classifiers performing better on data instances of higher difficulty. In this paper, we present our study on adjustment functions in each of which both the performance of a member classifier and the difficulty of a data set are determined nonlinearly. We report results from experiments conducted on several data sets, demonstrating the potential of the studied functions.

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An Improved Bagging Neural Network Ensemble Algorithm and Its Application

Cited by 13 publications

References 6 publications

Quality Prediction Model Based on Novel Elman Neural Network Ensemble

Quality Prediction Model Based on Novel Elman Neural Network Ensemble

Soft sensor modeling based on modified PSO

On Adjustment Functions for Weight-Adjusted Voting-Based Ensembles of Classifiers

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