Prediction of temperature and humidity of ship cabin based on ensemble of SVM
For challenges in maintaining cabin temperature and humidity,as well as data predicting,ensemble of support vector machine(ESVM)based on clonal selection algorithm(CSA)was proposed.Firstly,individual SVMs were optimized by CSA,and then the cabin temperature and humidity time data series were sampled.Lastly,ESVM was used for training and testing.finally,Statistical testing results and comparison of prediction errors with BP neural network,individual SVM and GM(2,1)models show that the ESVM model can effectively predict the changes in humidity and temperature in submarine cabins under air conditioning fault conditions.The method provides a technical support for the use and maintenance of equipment.
ensemble of SVMship cabinprediction of temperature and humidity
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