Analysis of ammonia nitrogen concentration using stochastic configuration networks

Wang, Wei; Li, Kang; Cai, Kewei; He, Di

doi:10.1109/cac.2018.8623311

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…The 3σ rule and median filtering method have been used to detect outliers in previous research in the lab [22]. The 3σ criterion, where σ stands for standard deviation, measures how far a number in a data set deviates from its mean.…”

Section: Data Pre-processingmentioning

confidence: 99%

Prediction Model of Ammonia Nitrogen Concentration in Aquaculture Based on Improved AdaBoost and LSTM

Wang,

Xu,

et al. 2024

Mathematics

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The concentration of ammonia nitrogen is significant for intensive aquaculture, and if the concentration of ammonia nitrogen is too high, it will seriously affect the survival state of aquaculture. Therefore, prediction and control of the ammonia nitrogen concentration in advance is essential. This paper proposed a combined model based on X Adaptive Boosting (XAdaBoost) and the Long Short-Term Memory neural network (LSTM) to predict ammonia nitrogen concentration in mariculture. Firstly, the weight assignment strategy was improved, and the number of correction iterations was introduced to retard the shortcomings of data error accumulation caused by the AdaBoost basic algorithm. Then, the XAdaBoost algorithm generated and combined several LSTM su-models to predict the ammonia nitrogen concentration. Finally, there were two experiments conducted to verify the effectiveness of the proposed prediction model. In the ammonia nitrogen concentration prediction experiment, compared with the LSTM and other comparison models, the RMSE of the XAdaBoost–LSTM model was reduced by about 0.89–2.82%, the MAE was reduced by about 0.72–2.47%, and the MAPE was reduced by about 8.69–18.39%. In the model stability experiment, the RMSE, MAE, and MAPE of the XAdaBoost–LSTM model decreased by about 1–1.5%, 0.7–1.7%, and 7–14%. From these two experiments, the evaluation indexes of the XAdaBoost–LSTM model were superior to the comparison models, which proves that the model has good prediction accuracy and stability and lays a foundation for monitoring and regulating the change of ammonia nitrogen concentration in the future.

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Section: Data Pre-processingmentioning

confidence: 99%

Prediction Model of Ammonia Nitrogen Concentration in Aquaculture Based on Improved AdaBoost and LSTM

Wang,

Xu,

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

“…2, where x is the network input vector, y is the network output vector, ω and b are the weighting vector and bias vectors of the input layer to the hidden layer, respectively, β is the weighting vector of the hidden layer to the output layer, g i denotes the output of the i-th hidden node and L is the number of hidden nodes. In this article, we used the SC-III algorithm for SCNs [14], [15], [24]. In particular, the design flow have been recalled in this session [14].…”

Section: Stochastic Configuration Networkmentioning

confidence: 99%

Defect Recognition in Concrete Ultrasonic Detection Based on Wavelet Packet Transform and Stochastic Configuration Networks

Zhao

Zheng

et al. 2021

IEEE Access

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On-line ammonia nitrogen measurement using generalized additive model and stochastic configuration networks

Wang

Jia

et al. 2021

Measurement

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Analysis of ammonia nitrogen concentration using stochastic configuration networks

Cited by 4 publications

References 7 publications

Prediction Model of Ammonia Nitrogen Concentration in Aquaculture Based on Improved AdaBoost and LSTM

Prediction Model of Ammonia Nitrogen Concentration in Aquaculture Based on Improved AdaBoost and LSTM

Defect Recognition in Concrete Ultrasonic Detection Based on Wavelet Packet Transform and Stochastic Configuration Networks

On-line ammonia nitrogen measurement using generalized additive model and stochastic configuration networks

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