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these different punctured convolutional codes are obtained by simply modifying the puncturing matrix P p . On the other hand, both the punctured encoder and decoder are based on the trellis structure of the base convolutional code, so that they can adaptively operate in the family of punctured convolutional codes by just knowing the changes in the puncturing matrix P p . This is called a set of rate-compatible punctured convolutional (RCPC) codes. RCPC codes are very useful in automatic repeat request (ARQ) and hybrid-ARQ schemes in order to optimize the data rate and BER performance as a function of the signal-to-noise ratio of the channel. Thus, when the signal-to-noise ratio is high, high-rate codes (i.e., medium error-control capability codes) are used, and when the signal-to-noise ratio in the channel is low, relatively small-rate codes (i.e., high error-control capability codes) are then utilized. The relative absence or presence of repeat transmission requests can be used to raise or lower the rate of the codes in the RCPC set, usually starting from a high rate. Other system requirements or status information can also be used to control the RCPC scheme, such as the quality-of-service requirements of a sender, or channel state information in a multiuser wireless system.

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17 D P Filev, F Tseng, Novelty Detection Based Machine Health Prognostics Proc 2006 Int Symposium on Evolving Fuzzy Systems, Lake District, UK, 2006, pp 193 199 18 R M Gray, Vector Quantization, IEEE ASSP, pages 4 29, 1984 19 C W Groetsch, The theory of Tikhonov regularization for Fredholm Equations of the First Kind Boston: Pitman, 1984 20 W Groi b ck, E Lughofer, E P Klement, A Comparison of Variable Selection Methods with o the Main Focus on Orthogonalization In Soft Methodology and Random Information Systems: Advances in Soft Computing (M Lop z-D M A Gil, P Grzegorzewski, O e az, Hryniewicz, J Lawry, ed) Berlin, Heidelberg, New York: Springer, 2004, pp 479 486 21 N Sundararajan, H-J Rong, G-B Huang, P Saratchandran, Sequential Adaptive Fuzzy Inference System (SAFIS) for Nonlinear System Identi cation and Prediction, Fuzzy Sets and Systems, 157 (9): 1260 1275, 2006.

22 P C Hansen, The truncated svd as a Method for Regularization, BIT, 27: 534 553, 1987 23 F E Harrel Regression Modeling Strategies New York: Springer-Verlag New York, 2001 24 T Hastie, R Tibshirani, J Friedman, The Elements of Statistical Learning: Data Mining, Inference and Prediction New York, Berlin, Heidelberg: Springer-Verlag, 2001 25 S Haykin, Neural Networks: A Comprehensive Foundation, 2nd Edition Upper Saddle River, NJ: Prentice Hall, 1999 26 A Hensel, T Spittel, Kraft- und Arbeitsbedarf bildsamer Formgebungsverfahren VEB Deutscher Verlag f r Grundstof ndustrie, 1978 u 27 G B Huang, P Saratchandran, N Sundararajan, An Ef cient Sequential Learning Algorithm for Growing and Pruning RBF (GAP-RBF) Networks, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 34 (6): 2284 2292, 2004 28 R Iserman, Fault Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance Berlin: Springer-Verlag, 2005 29 H Ishibushi, T Nakashima, T.

Murata, Performance Evaluation of Fuzzy Classi er Systems for Multidimensional Pattern Classi cation Problems, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 29 (5): 601 618, 1999 30 H Ishibushi, Y Nojima, Analysis of Interpretability-Accuracy Tradeoff of Fuzzy Systems by Multiobjective Fuzzy Genetics-Based Machine Learning, Int Journal of Approximate Reasoning, 44 (1): 4 31, 2007 31 T A Johansen, R Babuska, Multiobjective Identi cation of Takagi-Sugeno Fuzzy Models, IEEE Transactions on Fuzzy Systems, 11 (6): 847 860, 2003 32 N Kasabov, Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines London: Springer-Verlag, 2002 33 N K Kasabov, Evolving Fuzzy Neural Networks for Supervised/Unsupervised Online Knowledge-Based Learning, IEEE Trans on Systems, Men and Cybernetics, Part B (Cybernetics), 31 (6): 902 918, 2001 34 N K Kasabov, Q Song, DENFIS: Dynamic Evolving Neural-Fuzzy Inference System and Its Application for Time-Series Prediction, IEEE Trans.

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on Fuzzy Systems, 10 (2): 144 154, 2002 35 C C Lee, Fuzzy Logic in Control Systems: Fuzzy Logic Controller, Parts I and II, IEEE Trans System, Man, Cybernetics, 20: 404 435, 1990 36 G Leng, T M. McGinnity, G Prasad, An Approach for On-line Extraction of Fuzzy Rules Using a Self-Organising Fuzzy Neural Network, Fuzzy Sets and Systems, 150 (2): 211 243, 2005..