A hybrid wavelet analysis and support vector machines in forecasting development of manufacturing

Guo, Xuesong; Sun, Liankun; Li, Gang; Wang, Song

doi:10.1016/j.eswa.2007.07.052

Cited by 29 publications

(14 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Other application areas include handwriting classification (Scheidat et al 2009). Time series forecasting is also a domain where SVM optimization is often applied (Tay and Cao 2002;Guo et al 2008;Salahshoor et al 2010).…”

Section: Application Fieldsmentioning

confidence: 99%

Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning

Wuest¹

2015

Springer Theses

View full text Add to dashboard Cite

Section: Application Fieldsmentioning

confidence: 99%

Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning

Wuest¹

2015

Springer Theses

View full text Add to dashboard Cite

“…This is due to the fact that the generalization property of an SVM does not depend on the complete training data but only a subset thereof, the so-called support vectors. Now, SVM has been applied in many fields as follows: multiple objective discrete optimization [9], demand forecasting [10][11][12][13][14][15][16][17], image retrieval [18], subsurface characterization [19], intrusion detection [20], rule extraction [21].…”

Section: Introductionmentioning

confidence: 99%

Product demand forecasts using wavelet kernel support vector machine and particle swarm optimization in manufacture system

2010

Journal of Computational and Applied Mathematics

View full text Add to dashboard Cite

a b s t r a c tDemand forecasts play a crucial role in supply chain management. The future demand for a certain product is the basis for the respective replenishment systems. Aiming at demand series with small samples, seasonal character, nonlinearity, randomicity and fuzziness, the existing support vector kernel does not approach the random curve of the sales time series in the L 2 (R n ) space (quadratic continuous integral space). In this paper, we present a hybrid intelligent system combining the wavelet kernel support vector machine and particle swarm optimization for demand forecasting. The results of application in car sale series forecasting show that the forecasting approach based on the hybrid PSOWv-SVM model is effective and feasible, the comparison between the method proposed in this paper and other ones is also given, which proves that this method is, for the discussed example, better than hybrid PSOv-SVM and other traditional methods.

show abstract

“…However, the overall ability of ML algorithm to achieve results in a manufacturing environment was successfully proven (e.g. Alpaydin, 2010;Filipic & Junkar, 2000;Guo, Sun, Li, & Wang, 2008;Kim, Kang, Cho, Lee, & Doh, 2012;Nilsson, 2005).…”

Section: Advantages Of Machine Learning Application In Manufacturingmentioning

confidence: 99%

“…handwriting classification (Scheidat, Leich, Alexander, & Vielhauer, 2009). Time series forecasting is also a domain where SVM optimization is often applied (Guo et al, 2008;Salahshoor et al, 2010;Tay & Cao, 2002).…”

Section: Application Areas Of Supervised Machine Learning In Manufactmentioning

confidence: 99%

Machine learning in manufacturing: advantages, challenges, and applications

Wuest

Weimer²,

Irgens

et al. 2016

Production & Manufacturing Research

709

438

View full text Add to dashboard Cite

The nature of manufacturing systems faces ever more complex, dynamic and at times even chaotic behaviors. In order to being able to satisfy the demand for high-quality products in an efficient manner, it is essential to utilize all means available. One area, which saw fast pace developments in terms of not only promising results but also usability, is machine learning. Promising an answer to many of the old and new challenges of manufacturing, machine learning is widely discussed by researchers and practitioners alike. However, the field is very broad and even confusing which presents a challenge and a barrier hindering wide application. Here, this paper contributes in presenting an overview of available machine learning techniques and structuring this rather complicated area. A special focus is laid on the potential benefit, and examples of successful applications in a manufacturing environment.

show abstract

A hybrid wavelet analysis and support vector machines in forecasting development of manufacturing

Cited by 29 publications

References 16 publications

Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning

Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning

Product demand forecasts using wavelet kernel support vector machine and particle swarm optimization in manufacture system

Machine learning in manufacturing: advantages, challenges, and applications

Contact Info

Product

Resources

About