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Article Citation - WoS: 13Citation - Scopus: 14Improved Global Robust Stability for Interval-Delayed Hopfield Neural Networks(Springer, 2008) Singh, VimalA modified form of a recent criterion for the global robust stability of interval-delayed Hopfield neural networks is presented. The effectiveness of the modified criterion is demonstrated with the help of an example.Article Citation - WoS: 2Citation - Scopus: 2Discrete Time Neuro Sliding Mode Control With a Task-Specific Output Error(Springer, 2004) Efe, MO; Department of Electrical & Electronics EngineeringThe problem of obtaining the error at the output of a neuro sliding mode controller is analyzed in this paper. The controller operates in discrete time and the method presented describes an error measure that can be used if the task to be achieved is to drive the system under control to a predefined sliding regime. Once the task-specific output error is calculated, the neurocontroller parameters can be tuned so that the task is achieved. The paper postulates the strategy for discrete time representation of uncertain nonlinear systems belonging to a particular class. The performance of the proposed technique has been clarified on a third order nonlinear system, and the parameters of the controller are adjusted by using the error backpropagation algorithm. It is observed that the prescribed behavior can be achieved with a simple network configuration.Conference Object Citation - WoS: 22Citation - Scopus: 21Predicting dominant phytoplankton quantities in a reservoir by using neural networks(Springer, 2003) Gurbuz, H; Kivrak, E; Soyupak, S; Yerli, SVThe Levenberg-Marquardt algorithm was used to train artificial neural networks to predict the abundance of Cyclotella ocellata Pant. and Cyclotella kutzingiana Thwaites using time, depth, temperature, pH, dissolved oxygen, and electrical conductivity as input parameters for the oligo-mesotrophic Kuzgun Dam Reservoir, Turkey. The data were collected in monthly intervals during two ice-free seasons: between April 2000-November 2000 and April 2001-November 2001. To reduce over-fitting of the neural network based models, we employed single hidden layer networks with early stopping of training. Correlation coefficients, of neural network predictions with measurements of abundance of Cyclotella ocellata Pant. and Cyclotella kutzingiana Thwaites were 0.88 and 0.86, respectively.
