Predicting the Fiber diameter of Spunbonding Nonwovens Via Empirical Statistical methods and Neural Network Model

Journal Title: INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY - Year 2014, Vol 14, Issue 1

Abstract

In this paper, the empirical statistical and artificial neural network methods are established. We present a comparative study of two modeling methodological for predicting the fiber diameter of spunbonding nonwovens from the process parameters. The radial basis neural network, which has good approximation capability and fast convergence rate, is employed in this work, and it can provide quantitative predictions of fiber diameter. The effects of process parameters on fiber diameter are also determined by the ANN model. The results show the artificial neural network model yield more accurate and stable predictions than the statistical method, which reveals that artificial neural network technique is really an effective and viable modeling method.

Authors and Affiliations

Bo Zhao

Keywords

Related Articles

Social Representations of ICT in High School Students in Niger

This article examines social representations of information and communications technologies (ICT) in high school students in Niamey, Niger, and explores whether these representations are determined by training in and reg...

A Review of Security Threats by The Unauthorized in The E-Learning

Computers have become an integral part of our everyday existence. They are used to store and to send among students’ letters and sensitive documents, materials. In today's focused world, each Organization is endeavorin...

Advantage of Collaboration Workflows in the Automotive Supply Chain: Case Study on the Automotive Cluster of Slovenia

Strengthening of collaboration among individual business partners has proved essential for the structuring of Slovenian economy and enhancing competitive advantage on the global market. At the same time, ontology, as an...

Novel Approach for Frequent Pattern Algorithm for Maximizing Frequent Patterns in Effective Time

The essential aspect of mining association rules is to mine the frequent patterns. Due to native difficulty it is impossible to mine complete frequent patterns from a dense database. FP-growth algorithm has been implemen...

A Study of Distance Metrics in Histogram Based Image Retrieval

There has been a profound expansion of digital data both in terms of quality and heterogeneity. Trivial searching techniques of images by using metadata, keywords or tags are not sufficient. Efficient Content-based Image...

Download PDF file
  • EP ID EP650608
  • DOI 10.24297/ijct.v14i1.2123
  • Views 66
  • Downloads 0

How To Cite

Bo Zhao (2014). Predicting the Fiber diameter of Spunbonding Nonwovens Via Empirical Statistical methods and Neural Network Model. INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 14(1), 5323-5328. https://europub.co.uk/articles/-A-650608