Erratum to: Water Consumption Prediction for City Pumping Station Using Neural Networks

Data on the effective operation of new pumping station is scarce, and the unit structure is complex, as the temperature changes of different parts of the unit are coupled with multiple factors. The multivariable grey system prediction model can effectively predict the multiple parameter change of a nonlinear system model by using a small amount of data, but the value of its q parameters greatly influences the prediction accuracy of the model. Therefore, the particle swarm optimization algorithm is used to optimize the q parameters and the multi-sensor temperature data of a pumping station unit is processed. Then, the change trends of the temperature data are analyzed and predicted. Comparing the results with the unoptimized multi-variable grey model and the BP neural network prediction method trained under insufficient data conditions, it is proved that the relative error of the multi-variable grey model after optimizing the q parameters is smaller.

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Grey Model Optimized by Particle Swarm Optimization for Data Analysis and Application of Multi-Sensors

Qiu

Yang

et al. 2018

Sensors

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Erratum to: Water Consumption Prediction for City Pumping Station Using Neural Networks

Abstract: In the original version of the book, the misspelt co-author name "Marko Kleapch" has been changed to read as "Marko Klepach" in Chapter 44. The erratum chapter and the book have been updated with the change.

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Grey Model Optimized by Particle Swarm Optimization for Data Analysis and Application of Multi-Sensors

Grey Model Optimized by Particle Swarm Optimization for Data Analysis and Application of Multi-Sensors

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