By James Smolen Ph. D.
Presents the practising petroleum engineer with diagnostic info to optimize restoration or plan workover operations. James Smolen is an across the world well-known specialist in cased gap logging with greater than two decades of oil event. DLC: Oil good logging.
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Learning a Neuro-Fuzzy Classifier from Unbalanced Data in a Machine Vision Domain, In: Fuzzy-Neuro Systems 1999 - Computational Intelligence (FNS'99), pp. 23-32, G. Brewka, R Der, S. Gottwald, and A Schierwagen, Leipziger Universitatsverlag, Leipzig. 23. , and Nauck, D. (1999). Fuzzy Data Analysis: Challenges and Perspectives. Proc. 8th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'99, Seoul, Korea). IEEE Press, Piscataway, NJ, USA 24. , and Klawonn, F. (1994). , New York, Chichester.
The discussion on this matter is presented in several ensuing sections (Section 5, 6, and 7). The notion of consistency of 39 rules is introduced and studied in detail along with some computational details (Section 8). Conclusions are discussed in Section 9. 2 Granulation of Information In this section, we concentrate on the essence of information granulation and the role of information granules in a spectrum of perception processes carried out by humans. Then our focus moves into the construction of information granules in the setting of fuzzy sets.
Second European Workshop on Fuzzy Decision Analysis and Neural Networks for Management, Planning, and Optimization (EFDAN'97), pp. 170-179, Dortmund. 40. , and Liitjen, K. (1996). ), Proceedings IAPR-TC7 Workshop, Graz, pp. 229-244, R Oldenbourg, MUnchen. 35 41. , and Lee, M. (1993). Neural Networks and Genetic Algorithms, In: Fuzzy Logic in Artificial Intelligence (FLAI93), pp. 68-79, Springer-Verlag, Berlin. 42. -S. (1999). A fuzzy inductive learning strategy for modular rules. Fuzzy Sets and Systems, 103:91-105.