For accurate prediction of nitrogen oxides(NOx) concentration during the municipal solid waste incineration(MSWI) process, in this paper, a prediction modeling method based on a sparse regularization stochastic config...For accurate prediction of nitrogen oxides(NOx) concentration during the municipal solid waste incineration(MSWI) process, in this paper, a prediction modeling method based on a sparse regularization stochastic configuration network is proposed. The method combines Drop Connect regularization with L1 regularization. Based on the L1 regularization constraint stochastic configuration network output weights, Drop Connect regularization is applied to the input weights to introduce sparsity. A probability decay strategy based on network residuals is designed to address situations where the Drop Connect fixed drop probability affects model convergence. Finally, the generated sparse stochastic configuration network is used to establish the model, and is validated through experiments with standard datasets and actual data from an MSWI plant in Beijing. The experimental results prove that this modeling method exhibits high-precision prediction and generalization ability while effectively simplifying the model structure, which enables accurate prediction of NOx concentration.展开更多
基金supported by the National Natural Science Foundation of China (62373017, 62073006)the Beijing Natural Science Foundation of China (4212032)。
文摘For accurate prediction of nitrogen oxides(NOx) concentration during the municipal solid waste incineration(MSWI) process, in this paper, a prediction modeling method based on a sparse regularization stochastic configuration network is proposed. The method combines Drop Connect regularization with L1 regularization. Based on the L1 regularization constraint stochastic configuration network output weights, Drop Connect regularization is applied to the input weights to introduce sparsity. A probability decay strategy based on network residuals is designed to address situations where the Drop Connect fixed drop probability affects model convergence. Finally, the generated sparse stochastic configuration network is used to establish the model, and is validated through experiments with standard datasets and actual data from an MSWI plant in Beijing. The experimental results prove that this modeling method exhibits high-precision prediction and generalization ability while effectively simplifying the model structure, which enables accurate prediction of NOx concentration.
