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Computer-based robust engineering : essentials for DFSS / / Genichi Taguchi, Rajesh Jugulum, Shin Taguchi
Computer-based robust engineering : essentials for DFSS / / Genichi Taguchi, Rajesh Jugulum, Shin Taguchi
Autore Taguchi Genʼichi <1924-2012, >
Pubbl/distr/stampa Milwaukee, Wisconsin : , : ASQ Quality Press, , 2004
Descrizione fisica 1 online resource (240 p.)
Disciplina 658.4/013
Soggetto topico Taguchi methods (Quality control)
Industrial design
Six sigma (Quality control standard)
Information technology
Soggetto genere / forma Electronic books.
ISBN 600-00-4743-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910463098903321
Taguchi Genʼichi <1924-2012, >  
Milwaukee, Wisconsin : , : ASQ Quality Press, , 2004
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
The Mahalanobis-Taguchi Strategy: A Pattern Technology System
The Mahalanobis-Taguchi Strategy: A Pattern Technology System
Autore Taguchi Genichi
Pubbl/distr/stampa [Place of publication not identified], : Wiley Imprint, 2002
Disciplina 658.5/62
ISBN 0-471-27533-6
0-470-17224-X
0-470-34606-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910144587703321
Taguchi Genichi  
[Place of publication not identified], : Wiley Imprint, 2002
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
The Mahalanobis-Taguchi Strategy: A Pattern Technology System
The Mahalanobis-Taguchi Strategy: A Pattern Technology System
Autore Taguchi Gen'ichi <1924-2012>
Pubbl/distr/stampa [Place of publication not identified], : Wiley Imprint, 2002
Descrizione fisica 1 online resource (xxii, 234 pages) : illustrations
Disciplina 658.5/62
Soggetto topico Pattern recognition systems
ISBN 0-471-27533-6
0-470-17224-X
0-470-34606-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Preface. -- Acknowledgments. -- Terms and Symbols. -- Definitions of Mathematical and Statistical Terms. -- 1 Introduction. -- 1.1 The Goal. -- 1.2 The Nature of a Multidimensional System. -- 1.2.1 Description of Multidimensional Systems. -- 1.2.2 Correlations between the Variables. -- 1.2.3 Mahalanobis Distance. -- 1.2.4 Robust Engineering/ Taguchi Methods. -- 1.3 Multivariate Diagnosis The State of the Art. -- 1.3.1 Principal Component Analysis. -- 1.3.2 Discrimination and Classification Method. -- 1.3.3 Stepwise Regression. -- 1.3.4 Test of Additional Information (Rao s Test). -- 1.3.5 Multiple Regression. -- 1.3.6 Multivariate Process Control Charts. -- 1.3.7 Artificial Neural Networks. -- 1.4 Approach. -- 1.4.1 Classification versus Measurement. -- 1.4.2 Normals versus Abnormals. -- 1.4.3 Probabilistic versus Data Analytic. -- 1.4.4 Dimensionality Reduction. -- 1.5 Refining the Solution Strategy. -- 1.6 Guide to This Book. -- 2 MTS and MTGS. -- 2.1 A Discussion of Mahalanobis Distance. -- 2.2 Objectives of MTS and MTGS. -- 2.2.1 Mahalanobis Distance (Inverse Matrix Method). -- 2.2.2 Gram Schmidt Orthogonalization Process. -- 2.2.3 Proof That Equations 2.2 and 2.3 Are the Same. -- 2.2.4 Calculation of the Mean of the Mahalanobis Space. -- 2.3 Steps in MTS. -- 2.4 Steps in MTGS. -- 2.5 Discussion of Medical Diagnosis Data: Use of MTGS and MTS Methods. -- 2.6 Conclusions. -- 3 Advantages and Limitations of MTS and MTGS. -- 3.1 Direction of Abnormalities. -- 3.1.1 The Gram Schmidt Process. -- 3.1.2 Identification of the Direction of Abnormals. -- 3.1.3 Decision Rule for Higher Dimensions. -- 3.2 Example of a Graduate Admission System. -- 3.3 Multicollinearity. -- 3.4 A Discussion of Partial Correlations. -- 3.5 Conclusions. -- 4 Role of Orthogonal Arrays and Signal-to-Noise Ratios in Multivariate Diagnosis. -- 4.1 Role of Orthogonal Arrays. -- 4.2 Role of S/ N Ratios. -- 4.3 Advantages of S/ N ratios. -- 4.3.1 S/ N Ratio as a Simple Measure to Identify Useful Variables. -- 4.3.2 S/ N Ratio as a Measure of Functionality of the System. -- 4.3.3 S/ N Ratio to Predict the Given Conditions. -- 4.4 Conclusions. -- 5 Treatment of Categorical Data in MTS/MTGS Methods. -- 5.1 MTS/ MTGS with Categorical Data. -- 5.2 A Sales and Marketing Application. -- 5.2.1 Selection of Suitable Variables. -- 5.2.2 Description of the Variables. -- 5.2.3 Construction of Mahalanobis Space. -- 5.2.4 Validation of the Measurement Scale. -- 5.2.5 Identification of Useful Variables (Developing Stage). -- 5.2.6 S/ N Ratio of the System (Before and After). -- 5.3 Conclusions. -- 6 MTS/ MTGS under a Noise Environment. -- 6.1 MTS/ MTGS with Noise Factors. -- 6.1.1 Treat Each Level of the Noise Factor Separately. -- 6.1.2 Include the Noise Factor as One of the Variables. -- 6.1.3 Combine Variables of Different Levels of the Noise Factor. -- 6.1.4 Do Not Consider the Noise Factor If It Cannot Be Measured. -- 6.2 Conclusions. -- 7 Determination of Thresholds A Loss Function Approach. -- 7.1 Why Threshold Is Required in MTS/ MTGS. -- 7.2 Quadratic Loss Function. -- 7.2.1 QLF for the Nominal-the-Best Characteristic. -- 7.2.2 QLF for the Larger-the-Better Characteristic. -- 7.2.3 QLF for the Smaller-the-Better Characteristic. -- 7.3 QLF for MTS/ MTGS. -- 7.3.1 Determination of Threshold. -- 7.3.2 When Only Good Abnormals Are Present. -- 7.4 Examples. -- 7.4.1 Medical Diagnosis Case. -- 7.4.2 A Student Admission System. -- 7.5 Conclusions. -- 8 Standard Error of the Measurement Scale. -- 8.1 Why Mahalanobis Distance Is Used for Constructing the Measurement Scale. -- 8.2 Standard Error of the Measurement Scale. -- 8.3 Standard Error for the Medical Diagnosis Example. -- 8.4 Conclusions. -- 9 Advance Topics in Multivariate Diagnosis. -- 9.1 Multivariate Diagnosis Using the Adjoint Matrix Method. -- 9.1.1 Related Topics of Matrix Theory. -- 9.1.2 Adjoint Matrix Method for Handling Multicollinearity. -- 9.2 Examples for the Adjoint Matrix Method. -- 9.2.1 Example 1. -- 9.2.2 Example 2. -- 9.3 β-Adjustment Method for Small Correlations. -- 9.4 Subset Selection Using the Multiple Mahalanobis Distance Method. -- 9.4.1 Steps in the MMD Method. -- 9.4.2 Example. -- 9.5 Selection of Mahalanobis Space from Historical Data. -- 9.6 Conclusions. -- 10 MTS/ MTGS versus Other Methods. -- 10.1 Principal Component Analysis. -- 10.2 Discrimination and Classification Method. -- 10.2.1 Fisher s Discriminant Function. -- 10.2.2 Use of Mahalanobis Distance. -- 10.3 Stepwise Regression. -- 10.4 Test of Additional Information (Rao s Test). -- 10.5 Multiple Regression Analysis. -- 10.6 Multivariate Process Control. -- 10.7 Artificial Neural Networks. -- 10.7.1 Feed-Forward (Backpropagation) Method. -- 10.7.2 Theoretical Comparison. -- 10.7.3 Medical Diagnosis Data Analysis. -- 10.8 Conclusions. -- 11 Case Studies. -- 11.1 American Case Studies. -- 11.1.1 Auto Marketing Case Study. -- 11.1.2 Gear-Motor Assembly Case Study. -- 11.1.3 ASQ Research Fellowship Grant Case Study. -- 11.1.4 Improving the Transmission Inspection System Using MTS. -- 11.2 Japanese Case Studies. -- 11.2.1 Improvement of the Utility Rate of Nitrogen While Brewing Soy Sauce. -- 11.2.2 Application of MTS for Measuring Oil in Water Emulsion. -- 11.2.3 Prediction of Fasting Plasma Glucose (FPG) from Repetitive Annual Health Checkup Data. -- 11.3 Conclusions. -- 12 Concluding Remarks. -- 12.1 Important Points of the Proposed Methods. -- 12.2 Scientific Contributions from MTS/MTGS Methods. -- 12.3 Limitations of the Proposed Methods. -- 12.4 Recommendations for Future Research. -- Bibliography. -- Appendixes. -- A.1 ASI Data Set. -- A.2 Principal Component Analysis (MINITAB Output). -- A.3 Discriminant and Classification Analysis (MINITAB Output). -- A.4 Results of Stepwise Regression (MINITAB Output). -- A.5 Multiple Regression Analysis (MINITAB Output). -- A.6 Neural Network Analysis (MATLAB Output). -- A.7 Variables for Auto Marketing Case Study. -- Index.
Record Nr. UNINA-9910678111303321
Taguchi Gen'ichi <1924-2012>  
[Place of publication not identified], : Wiley Imprint, 2002
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui