An Improved Dynamic Process Neural Network Prediction Model Identification Method

摘要

Dynamic system parameter recognition is a contextual test of the N.N. training process's simplification and mitigation values with data imbalances. The parameters need to be the cause, and theoretical analysis to explain the problem's methodological roots suggests improving the standard neural network model. Neural Network (N. N.) is used successfully for simulation system parameters as training data. When comparing the process to direct computation duplication, the significant reduction using N.N. reduces the calculation time for each prediction from 1-30 minutes to reduce accuracy in the fraction of seconds. The impact of the changes on the training principles on this work system's performance will be fundamental. This approach modeling is not the system responsible for tracking all datasets in N.N. time-series predictions but instead identifying target system factors. Its structure is known to have come with changes in the learning process towards parametric improvements. Predictive ability is excellent due to the short window of forecast prediction. A highly flexible modeling framework's characteristics allow policymakers to develop the objectives and resources within the constraints of planning vector control programs and case monitoring strategy adjustments.