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Sufficient Fritz John optimality conditions are obtained for a control problem in which objective functional is pseudoconvex and constraint functions are quasiconvex or semi-strictly quasiconvex. A dual to the control problem is formulated using Fritz John type optimality criteria instead of Karush-Kuhn-Tucker optimality criteria and hence does not require a regularity condition. Various duality results amongst the control problem and its proposed dual are validated under suitable generalized convexity requirements. The relationship of our duality results to those of a nonlinear programming problem is also briefly outlined.
The prediction of riverflows requires the understanding of rainfall-runoff process which is highly nonlinear, dynamic and complex in nature. In this research streamflow decomposition based integrated ANN (SD-ANN) model is developed to improve the efficacy rather than using a single ANN model for the flow hydrograph. The streamflows are decomposed into two states namely 1) the rise state and 2) the fall state. The rainfall-runoff data obtained from the Kolar River basin is used to test the efficacy of the proposed model when compared to feed-forward ANN model (FF-ANN). The results obtained in this study indicate that the proposed SD-ANN model outperforms the single ANN model in terms of both the statistical indices and the prediction of high flows.