Intelligent Systems Design and Applications by Juan Manuel Górriz, Carlos G. Puntonet, J. J. G. de la Rosa,

By Juan Manuel Górriz, Carlos G. Puntonet, J. J. G. de la Rosa, Moisés Salmerón (auth.), Ajith Abraham, Katrin Franke, Mario Köppen (eds.)

This quantity comprises the complaints of the 3rd foreign convention on clever platforms layout and functions (ISDA 2003) held in Tulsa, united states, August 10-13. present examine in all parts of computational intelligence is gifted together with layout of synthetic neural networks, fuzzy structures, evolutionary algorithms, hybrid computing structures, clever brokers, and their purposes in technology, expertise, company and advertisement. major subject matters addressed via the convention are the architectures of clever structures, snapshot, speech and sign processing, web modeling, info mining, company and administration purposes, keep watch over and automation, software program brokers and information management.

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First, it is assumed by way of automatic voltage regulation that bus one voltage, E, is kept constant at 1 per-unit. We also assume that the Synchronous Machine is unregulated-meaning it can output as much power as demanded from it. We also let t5 = t5 -{} to simplify calculations. Then we write the system equations using the swing equation to model the synchronous machine, and power balance equations to find power demand at the bus and power supplied by the generator [11]. (1) (2) PG = E 2 G - EV(Gcos(o) - Bsin(o)) (3) Neural Network Predictive Control Applied to Power System Stability QG = E2 B - EV{Gsin(o) - Bcos(o») 45 (4) 0= _BV2 - EV(G sineS) - B cos(S)) - QD (5) 0=-GV 2 -EV(Gcos(S)+Bsin(S))-PD (6) Noticing that (5) and (6) are algebraic equations we can use time-scale decomposition [12, 13] to model the system in the singular perturbation form of: x=j(x,u,y,~) (7) g (8) = g{x,u, y,;-) Where g represents the algebraic equations andjrepresents the state equations.

Results and Discussion The applicability of different kinds of neural networks for the probabilistic analysis of structures, when the sources of randomness can be modeled as random On Improving Data Fitting Procedure in Reservoir Operation 19 variables is summarized. The comparison comprehends two network algorithms (multi-layer neural networks). This is a relevant result for neural networks learning because most of the practical applications employ the neural learning heuristic back-propagation, which uses different activation functions.

Yj (t) = the predicted network output at discrete time t and dj (t) = the desired network output at discrete time t. The aim of the training procedure is to adjust the connection weight until the global minimum in the error surface has been reached. The output from linear programming model; inflow, storage, release and actual demand are given as input into ANN model of the basin. Monthly values of Time period, Inflow, initial storage and demand are the input into a three layer neural network and output from this network is monthly release.

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