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Juang, A TSK-Type Recurrent Fuzzy Network for Dynamic Systems Processing by Neural Network and Genetic Algorithms, IEEE Trans on Fuzzy Systems, Vol 10, No 2, pp 155 169, 2002 23 N K Kasabov, Evolving Fuzzy Neural Networks for Supervised/Unsupervised Online Knowledge-Based Learning, IEEE Trans on Systems, Man and Cybernetics, Vol 31, No 6, pp 902 918, 2001 24 N K Kasabov, Q Song, DENFIS: Dynamic Evolving Neural-Fuzzy Inference System and Its Application for Time-Series Prediction, IEEE Trans on Fuzzy Systems, Vol 10, No 2, pp 144 154, 2002 25 K Kim, E J Whang, C-W Park, E Kim, M Park, A TSK Fuzzy Inference Algorithm for Online Identi cation, Berlin, Heidelberg: Springer-Verlag, 2005, pp 179 188 26 E B Kosmatopoulos, MM Polycarpou, MA Christodoulou, PA Ioannou, High-order Neural Network Structures for Identi cation of Dynamic Systems, IEEE Trans on Neural Networks, Vol 6, No 2, pp.

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422 431, 1995 27 G Leng, T M McGinnity, G Prasad, An Approach for On-line Extraction of Fuzzy Rules Using a Self-Organizing Fuzzy Neural Network, Fuzzy Sets and Systems, Vol 150, pp 211 243, 2005 28 C T Lin, Neural Fuzzy Control Systems with Structure and Parameter Learning New York: World Scienti c, 1994 29 K S Narendra, K Parthasarathy, Identi cation and Control of Dynamic Systems Using Neural Networks, IEEE Trans Neural Networks, Vol 1, No 1, pp 4 27, 1990 30 A G Parlos, S K Menon, A F Atiya, An Algorithm Approach to Adaptive State Filtering Using Recurrent Neural Network, IEEE Trans Neural Networks, Vol 12, No 6, pp 14111432, 2001 31 J Platt, A Resource-Allocating Network for Function Interpolation, Neural Computation, Vol 3, No 2, pp 213 225, 1991 32 I Rivals, L Personnaz, Neural Network Construction and Selection in Nonlinear Modeling, IEEE Trans.

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There is a distinct maximum of occurrence of arctic haze in late winter/early spring (March May) during anti-cyclonic conditions This is the time of greatest strength and largest extent of the arctic front, which at these times encompasses several Eurasian industrial areas These areas are also considered to be the origin of the particles forming the arctic haze Therefore, arctic.

on Neural Networks, Vol 14, No 4, pp 804 820, 2003 33 J J Rubio, W Yu, A New Discrete-Time Sliding-Mode Control with Time-Varying Gain and Neural Identi cation, Int J Control, Vol 79, No 4, pp 338 348, April 2006 34 J J Rubio, W Yu, Nonlinear System Identi cation with Recurrent Neural Networks and Dead-Zone Kalman Filter Algorithm, Neurocomputing, Vol 70, pp 2460 2466, 2007 35 J J Rubio, W Yu, Dead-Zone Kalman Filter Algorithm for Recurrent Neural Networks 44th IEEE Conf on Decision and Control, Spain, 2005, pp 2562 2567..

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Flash provides movie clips with defined parameters, called components, to aid in developing rich user experiences in Flash movies. Each built-in Flash component has its own unique set of ActionScript methods that allow you to set and change the authoring parameters and additional options at runtime. By combining the easy drop-in capabilities of the predefined components with the powerful capabilities of ActionScript, you can create fully functional applications on the Web. For more information on components, see 15, Using Components, on page 289. For an interactive introduction to components, choose Help > Tutorials > Introduction to Components.

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When ion organic interactions are neglected, both models should give equal results, as both utilise the same activity coef cient models for treating the inorganic and organic fractions, respectively In the model of Ming and Russell [18], interaction parameters between different groups in UNIFAC were calculated using binary inorganic/organic systems at limited concentrations for sugars, carboxylic acids, (NH4 )2 SO4 and NaCl Whilst the model reproduced measured growth factor data well for simple inorganic/organic systems where data were available, the interactions between organics and NH+ and SO2 were assumed to be equal to those of Na+ and Cl 4 4 Raatikainen and Laaksonen [114] compared some existing activity coef cient models that could be potentially suitable for modelling the hygroscopic properties of organic-electrolyte particles.

Om_1 Om-2 Om-3

distribution before the drift; the samples marked with rectangles represent the underlying data distribution after the drift; the big circles represent the cluster center for the original data (left), the wrongly updated center by standard incremental learning algorithm (middle), and the real center for the new data distribution (right).

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One way to implement optimistic concurrency control is to require that all local schedulers maintain a total commitment order for all local transactions. These schedulers are required to send their total commitment order graphs to the responsible global TM when they validate a transaction locally. The global TM receiving all such graphs will only commit the transaction globally if the order of committing the transaction in all local graphs is compatible. Otherwise, the global TM aborts the transaction.

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