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Classification, feature selection and prediction with Neural-network Taguchi System

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  • Bharatendra K. Rai

Abstract

Mahalanobis-Taguchi System (MTS) is often compared with artificial neural networks as both methodologies share common application areas. However, the comparison has been strictly limited to latter as a standalone process. Neural networks in a MTS framework, due to availability of a large array of architectures, has potential to lend flexibility needed to deal with a wide variety of application areas. This paper proposes a Neural-network Taguchi System (NTS) approach that incorporates neural networks in a MTS framework and consists of four stages viz., plan, validate, identify, and monitor. The workability of the proposed approach is illustrated using a tool-breakage prediction problem.

Suggested Citation

  • Bharatendra K. Rai, 2009. "Classification, feature selection and prediction with Neural-network Taguchi System," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 4(6), pages 645-664.
    • Handle: RePEc:ids:ijisen:v:4:y:2009:i:6:p:645-664
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