Bidirectional associative memories
IEEE Transactions on Systems, Man and Cybernetics
A two-phase optimization neural network
IEEE Transactions on Neural Networks
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An approach based on augmented Lagrange programming neural network (ALPNN) is proposed for the optimal operation of multi-reservoir network control problems. The main objective here is to find out the optimal hourly water releases from each hydro-plant in the interconnected hydro system to minimize the energy deficit and to distribute uniformly the energy deficit if any in each time interval. The interdependence between water discharge rate variables is very apparent in multi-reservoir network control problems. This proposed method takes into account the concurrent interaction among all the water discharge rate variables of the problem. This approach is based on the Lagrange multiplier theory and search for solutions satisfying the necessary conditions of optimality in the state space. The network equilibrium point satisfies the Kuhn-Tucker condition for the problem and corresponds to the Lagrange solution of the problem. This technique has been applied to a standard 10-reservoir interconnected network in which each hydro-power plant has a linear generation model and discretized time varying river inflows. Results obtained from this approach are compared with those obtained by the conventional discrete maximum principle method. It is observed from the results that the proposed method is very effective and provides better results with respect to constraint satisfaction.