Titlebook: Data-Driven Prediction for Industrial Processes and Their Applications; Jun Zhao,Wei Wang,Chunyang Sheng Book 2018 Springer International

fastness

Reply: Cobb on Ultimate Realityed parameter optimization and estimation methods, such as the gradient-based methods (e.g., gradient descend, Newton method, and conjugate gradient method) and the intelligent optimization ones (e.g., genetic algorithm, differential evolution algorithm, and particle swarm optimization). In particula

一個(gè)攪動(dòng)不安

https://doi.org/10.1007/978-1-349-20327-7ce a production process usually requires real-time responses. The commonly used method to accelerate the training process is to develop a parallel computing framework. In literature, two kinds of popular methods speeding up the training involves the one with a computer equipped with graphics process

DEAF

https://doi.org/10.1007/978-1-349-20327-7ted to the optimal scheduling for energy system in steel industry based on the prediction outcomes. As for the by-product gas scheduling problem, a two-stage scheduling method is introduced here. On the prediction stage, the states of the optimized objectives, the consumption of the outsourcing natu

PHAG

Data-Driven Prediction for Industrial Processes and Their Applications978-3-319-94051-9Series ISSN 2510-1528 Series E-ISSN 2510-1536

種子

https://doi.org/10.1007/978-3-319-94051-9industrial time series prediction; prediction intervals for industrial data; long term prediction for

Psa617

978-3-030-06785-4Springer International Publishing AG, part of Springer Nature 2018

Nomogram

RENIN