Abstract
This paper presents the application to the modeling of a novel technique of artificial intelligence. Through a transductive learning process, a neuro-fuzzy inference system enables to create a different model for each input to the system at issue. The model was created from a given number of known data with similar features to data input. The sum of these individual models yields greater accuracy to the general model because it takes into account the particularities of each input. To demonstrate the benefits of this kind of modeling, this system is applied to the tool wear modeling for turning process.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ljung, L.: System Identification: Theory for the User. Prentice-Hall, Upper Saddle River (1999)
Sjoberg, J., Zhang, Q., Ljung, L., Benveniste, A., Delyon, B., Glorennec, P.Y., Hjalmarsson, H., Juditsky, A.: Nonlinear black-box modeling in system identification: a unified overview. Automatica, 1691–1724 (1995)
Bohlin, T.: A Case-Study of Gray Box Identification. Automatica, 307–318 (1994)
Keller, J.M., Hunt, D.J.: Incorporating fuzzy membership functions into the perceptron algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI 7, 693–699 (1985)
Mitra, S., Hayashi, Y.: Neuro-fuzzy rule generation: survey in soft computing framework. IEEE Transactions on Neural Networks 11, 748–768 (2000)
Jang, J.-S.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics 23, 665–685 (1993)
Kim, J., Kasabov, N.: HyFIS: Adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems. Neural Networks 12, 1301–1319 (1999)
Nauck, D., Klawonn, F., Kruse, R.: Foundations of Neuro–Fuzzy Systems. Wiley, Chichester (1997)
Wang, L.X.: A course in fuzzy systems and control. Prentice-Hall, Inc., Upper Saddle River (1996)
Vapnik, V.: Statistical Learning Theory. John Wiley & Sons, Inc., New York (1998)
Song, Q., Kasabov, N.: TWNFI - a transductive neuro-fuzzy inference system with weighted data normalization for personalized modeling. Neural Networks 19, 1591–1596 (2006)
Joachims, T.: Transductive inference for text classification using support vector machines. In: Machine Learning: Proceedings of the Sixteenth International Conference (1999)
Heyer, L.J., Kruglyak, S., Yooseph, S.: Exploring expression data identification and analysis of coexpressed genes. Genome Research 9, 1106–1115 (1999)
Sharma, V.S., Sharma, S.K., Sharma, A.K.: Cutting tool wear estimation for turning. Journal of Intelligent Manufacturing 19, 99–108 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gajate, A., Haber, R.E., Alique, J.R., Vega, P.I. (2009). Transductive-Weighted Neuro-Fuzzy Inference System for Tool Wear Prediction in a Turning Process. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds) Hybrid Artificial Intelligence Systems. HAIS 2009. Lecture Notes in Computer Science(), vol 5572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02319-4_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-02319-4_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02318-7
Online ISBN: 978-3-642-02319-4
eBook Packages: Computer ScienceComputer Science (R0)