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Angelov, P (2002) Evolving Rule-Based Models: A Tool for Design of Flexible Adaptive Systems Heidelberg, New York: Springer-Verlag, ISBN 3-7908-1457-1 Angelov, P, D Filev (2004) An Approach to On-line Identi cation of Takagi-Sugeno Fuzzy Models, IEEE Trans on System, Man, and Cybernetics, Part B Cybernetics, Vol 34, No 1, pp 484 498, ISSN 1094-6977 Angelov, P, C Xydeas, D Filev (2004) On-line Identi cation of MIMO Evolving TakagiSugeno Fuzzy Models Int Joint Conf on Neural Networks and Int Conf on Fuzzy Systems, IJCNN-FUZZ-IEEE, Budapest, Hungary, July 25 29, pp 55 60, ISBN 0-7803-8354-0 Angelov, P (2006) Machine Learning (Collaborative Systems), patent WO2008053161 (priority date November 1, 2006) Angelov, P, X Zhou (2006) Evolving Fuzzy Systems from Data Streams in Real-Time Proc 2006 Int Symp on Evolving Fuzzy Systems, Ambleside, UK, September 7 9, IEEE Press, pp 29 35, ISBN 0-7803-9719-3 Angelov, P, X Zhou (2008a).

On Line Learning Fuzzy Rule-based System Structure from Data Streams Proc World Congress on Computational Intelligence, WCCI-2008, Hong Kong, June 1 6, IEEE Press, pp 915 922, ISBN 978-1-4244-1821-3/08 Angelov, P, X Zhou (2008b) Evolving Fuzzy Rule-Based Classi ers from Data Streams, IEEE Trans on Fuzzy Systems, ISSN 1063-6706, special issue on evolving fuzzy systems, Dec 2008, Vol 16, No 6, pp 1462 1475 Angelov, P, A Kordon, X Zhou (2008) Evolving Fuzzy Inferential Sensors for Process Industry Proc 3rd Int Workshop on Genetic and Evolving Fuzzy Systems, Witten-Bomerholz, Germany, March 4 7, IEEE Press, pp 41 46, ISBN 978-1-4244-1613-4 Astrom, K J, B Wittenmark (1989) Adaptive Control Reading, MA: Addison-Wesley Barsamian, A, Mac J (1998) Neural Network-Based Inferential Property Predictors, as, Hydrocarbon Processing, Vol 77, No 10, pp 107 116 Bhat, S, D Chatterjee, N Saraf (2003).

Further information on how numerical models (especially nite difference models) are used in practice is given in Section 106 below 1044 Playing dice: probabilistic modeling One philosophical problem which we have not yet broached in our discussion is the issue of nonuniqueness When groundwater models began to become widely used in the 1980s, many commentators began to point out that the uncertainties which hinder our understanding of real aquifers are suf cient to cast doubt on the practice of developing a single model for any one domain (eg Gutjahr and Gelhar 1981; Dagan 1982; Marsily 1986) It is relatively easy to demonstrate that the same set of results can be obtained using very different combinations of boundary conditions, T and S values, and recharge distributions.

A phenomenological analysis of coping effectiveness in golf Sport Psychologist, 19, 111 130 Nideffer, R M (1981) The ethics and practice of applied sport psychology Ithaca, NY: Mouvement Nideffer, R M, DuFresne, P, Nesvig, D, & Selder, D (1980) The future of applied sport psychology Journal of Sport Psychology, 2, 170 174 Nideffer, R M, Feltz, D L, & Salmela, J (1982) A rebuttal to Danish and Hale: A committee report Journal of Sport Psychology, 4, 3 6 Nideffer, R M, & Sagal, M (2006) Applied sport psychology: Personal growth to peak performance (pp 382 403) Boston: McGraw-Hill Norlander, T, Bergman, H, & Archer, T (1999) Primary process in competitive archery performance: Effects of flotation REST Journal of Applied Sport Psychology, 11, 194 209..

On-line Data Processing and Product Properties Prediction for Crude Distillation Units AIChe 2003 Spring National Meeting, New Orleans, LA, pp 1 19 Camacho, E F, C Bordons (1999) Model Predictive Control London: Springer, ISBN 3-54076241-8..

Where the different combinations of parameters are equally credible (which many experienced practitioners would argue is not so often as the critics liked to suggest), then on what basis can we offer a single set of model results as our unique answer to a given problem This classic single input/single output approach is often referred to as deterministic modeling, to distinguish it from the alternative approach of probabilistic modeling (also known as stochastic modeling) The proponents of probabilistic groundwater modeling credibly argue that it is more reasonable to conceive of our input data as probability distributions rather than single numbers If we then process these probabilistic inputs through numerical modeling programs, the output will also be delivered in the form of probability distributions There are a number of procedures for doing this (Marsily 1986).

American Psychological Association (2000) Membership dues statement (No 7) Washington, DC: Author Ball, S (1984) Educational psychology as an academic chameleon: An editorial assessment after 75 years Journal of Educational Psychology, 76(6), 993 999 Berliner, D C (1993) The 100-year journey of educational psychology: From interest to disdain, to respect for the practice In T K Fagan & G R VandenBos (Eds), Exploring applied psychology: Origins and critical analyses (pp 41 78)Washington, DC: American Psychological Association Berliner, D C, & Calfee, R C (Eds) (1996) Handbook of educational psychology New York: Macmillan Library Reference USA Brownlee, S, Leventhal, H, & Leventhal, E A (2000) Regulation, self-regulation, and construction of the self in the maintenance of physical health In M Boekaerts, P R Pintrich, & M Zeidner (Eds), Handbook of self-regulation (pp 369 416) San Diego, CA: Academic Press Charles, D C (1976).