A BP neural network model optimized by Mind Evolutionary Algorithm for predicting the ocean wave heights

Wang, Wen-Xu; Tang, Ruichun; Li, Cheng; Liu, Peishun; Luo, Liang

doi:10.1016/j.oceaneng.2018.04.039

Cited by 158 publications

(70 citation statements)

References 18 publications

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“…The improved BP neural network algorithm adopts the () Delta  learning rule, which belongs to the tutor learning algorithm [25], and completes the input to output mapping through a process that minimizes the loss function. This process is used to adjust the connection weights and thresholds among the neurons.…”

Section: B the Principle Of Improved Bp Neural Network Learning Algomentioning

confidence: 99%

Application of BP Neural Network Ensemble Model Based on Bagging Algorithm

Liao¹,

Liao²,

Lv³

et al. 2019

IJMLC

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The latest coal mine policy of China puts forward new requirements for safe mining in the coalmine. This paper proposes a BP neural network ensemble learning model based on additional momentum term combined with Bagging algorithm for the prediction of coal and gas outburst. The additional momentum term approach is used to optimize the weights and thresholds of BP neural network during the training process. In order to enhance the prediction ability of BP neural network, the Bagging algorithm is adopted to build ensemble neural network. Experiments show that as compared with the standard BP neural network, the ensemble learning model can improve the prediction accuracy of 8%-10% points in the prediction of coal and gas outburst and the prediction accuracy is over 95%.Index Terms-BP neural network, Bagging algorithm, ensemble learning model, additional momentum terms, coal and gas outburst prediction.

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Section: B the Principle Of Improved Bp Neural Network Learning Algomentioning

confidence: 99%

Application of BP Neural Network Ensemble Model Based on Bagging Algorithm

Liao¹,

Liao²,

Lv³

et al. 2019

IJMLC

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show abstract

“…However, the GA requires a lot of iteration time to find the best fitting state. To solve this problem, the MEA‐BP neural network has been proposed . The prediction effect of the Standard BP (St‐BP) model in the training phase is good, the effect of the verification phase is worse, and the effect of the test phase becomes worse.…”

Section: Generalizationmentioning

confidence: 99%

“…On the basis of avoiding the overfitting problem of neural network algorithm and ensuring its generalization ability, the efficiency of neural network algorithm becomes the focus of optimization algorithm and its application in spectral analysis . In contrast, BP neural networks are generally considered to have advantages in improving algorithm efficiency . For the BP neural network, Luo Liqiang proposed to use the tapping technology to optimize the model parameters, thereby reducing training time and improved the efficiency of the algorithm by using the single‐component prediction method based on BEP.…”

Section: Neural Network Algorithm Efficiencymentioning

confidence: 99%

“…In this review, we mainly demonstrated several kinds of typical ANN models (variants) to overcome the challenges in quantitative analysis applied in XRF, such as Self‐improving Segmented Particle Swarm Optimization (SPSO) Adaptive Neural Network (SANN), Deep Sparse Auto‐encoder Neural Network (DSAENN), Single Component Prediction Based On Backward Error Propagation (SCP‐BEP), Multiple Component Prediction Based On Backward Error Propagation (MCP‐BEP), Backward Propagation Network Model Based On Principal Component Analysis (PCA‐BP), Genetic Algorithm Backward Propagation (GA‐BP), Mind Evolutionary Algorithm Backward Propagation (MEA‐BP), and Probabilistic Neural Network (PNN) . We did not discuss all available models in this field; instead, the focus of this review is on overfitting problem, generalization ability, and iteration efficiency.…”

Section: Introductionmentioning

confidence: 99%

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Application of artificial neural networks to X‐ray fluorescence spectrum analysis

et al. 2018

X-Ray Spectrometry

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X‐ray fluorescence (XRF) is widely applied as a mature nondestructive testing method, and appropriate improvement of quantitative analysis methods can improve the accuracy of XRF. Artificial neural network is an intelligent information processing system, its developments and application in XRF are reviewed, and representative models (back propagation, radial basis function, genetic algorithm artificial neural network, and others) are discussed in more details in overfitting, generalization, and algorithm efficiency. Potential directions of developing artificial neural network applied in XRF are proposed in this review as a further study.

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“…For nonlinear prediction, the more commonly used methods are curve fitting (Motulsky and Ransnas, 1987), gray-box model (Pearson and Pottmann, 2000), homogenization function model (Monteiro et al, 2008), neural network (Deo et al, 2001;Y. Wang et al, 2015;Kim et al, 2016) and so on.…”

Section: Introductionmentioning

confidence: 99%

Hybrid improved empirical mode decomposition and BP neural network model for the prediction of sea surface temperature

Jiang

Conde

et al. 2019

Ocean Sci.

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Sea surface temperature (SST) is the major factor that affects the ocean-atmosphere interaction, and in turn the accurate prediction of SST is the key to ocean dynamic prediction. In this paper, an SST-predicting method based on empirical mode decomposition (EMD) algorithms and back-propagation neural network (BPNN) is proposed. Two different EMD algorithms have been applied extensively for analyzing time-series SST data and some nonlinear stochastic signals. The ensemble empirical mode decomposition (EEMD) algorithm and complementary ensemble empirical mode decomposition (CEEMD) algorithm are two improved algorithms of EMD, which can effectively handle the mode-mixing problem and decompose the original data into more stationary signals with different frequencies. Each intrinsic mode function (IMF) has been taken as input data to the back-propagation neural network model. The final predicted SST data are obtained by aggregating the predicted data of individual series of IMFs (IMFi). A case study of the monthly mean SST anomaly (SSTA) in the northeastern region of the North Pacific shows that the proposed hybrid CEEMD-BPNN model is much more accurate than the hybrid EEMD-BPNN model, and the prediction accuracy based on a BP neural network is improved by the CEEMD method. Statistical analysis of the case study demonstrates that applying the proposed hybrid CEEMD-BPNN model is effective for the SST prediction. Highlights include the following: Highlights.-An SST-predicting method based on the hybrid EMD algorithms and BP neural network method is proposed in this paper.-SST prediction results based on the hybrid EEMD-BPNN and CEEMD-BPNN models are compared and discussed.-A case study of SST in the North Pacific shows that the proposed hybrid CEEMD-BPNN model can effectively predict the time-series SST.

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A BP neural network model optimized by Mind Evolutionary Algorithm for predicting the ocean wave heights

Cited by 158 publications

References 18 publications

Application of BP Neural Network Ensemble Model Based on Bagging Algorithm

Application of BP Neural Network Ensemble Model Based on Bagging Algorithm

Application of artificial neural networks to X‐ray fluorescence spectrum analysis

Hybrid improved empirical mode decomposition and BP neural network model for the prediction of sea surface temperature

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