The Evaluation of Fabric Prickle Based on BP Neural Network

Abstract

A three-layer BP neural network model was established by relating subjective evaluation of fabric prickle level and 16 objective parameters from KES-FB system. The elastic gradient decrease method was adopted for network training to achieve the preset precision of the model which was later applied to fabric prickle level evaluation. Results from this method gave a considerably accuracy compared with actual subjective results which implied a compatibility between BP neural network and traditional subjective evaluation.

Research on Evaluation Method of Urban Human Settlement Environment Quality Based on Back Propagation Neural Network

“…( ) ∑   h) Judge whether the algorithm iteration ends: If the iteration is completed, it means that the training process of urban human settlements environmental quality assessment model based on back propagation neural network can be completed, and the show is established; In case the iteration isn't completed, return to the covered up layer yield evaluation part and begin a new preparing alteration procedure [20] until the method iteration is completed.…”

BP Neural Network Based Optimization for China Railway Freight Transport Network

“…Compared with the mentioned methods with low calculation efficiency and less consideration of various influencing factors, BP neural network has been proved well feasibility in the similar problems, such as satellite classification [6], image classification [7], customer classification [8], and signal classification [9]. In addition, BP neural network shows three advantages in solving these problems, including self-learning, associative memory and high speed optimal solving [10].…”