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Showing 2 results for Mousavi

Misaghi F., Mohammadi K., Mousavizadeh M.h.,
Volume 1, Issue 1 (September 2003)
Abstract

In the present paper, ANN is used to predict the tidal level fluctuations, which is an important parameter in maritime areas. A time lagged recurrent network (TLRN) was used to train the ANN model. In this kind of networks, the problem is representation of the information in time instead of the information among the input patterns, as in the regular ANN models. Two sets of data were used to test the proposed model. San Francisco Bay tidal levels were used to test the performance of the model as a predictive tool. The second set of data was collected in Gouatr Bay in southeast of Iran. This data set was used to show the ability of the ANN model in predicting and completing of data in a station, which has a short period of records. Different model structures were used and compared with each other. In addition, an ARMA model was used to simulate time series data to compare the results with the ANN forecasts. Results proved that ANN can be used effectively in this field and satisfactory accuracy was found for the two examples. Based on this study, an operational real time environment could be achieved when using a trained forecasting neural network.
S.j. Mousavi, K. Ponnambalam, F. Karray,
Volume 3, Issue 2 (June 2005)
Abstract

A dynamic programming fuzzy rule-based (DPFRB) model for optimal operation of reservoirs system is presented in this paper. A deterministic Dynamic Programming (DP) model is used to develop the optimal set of inflows, storage volumes, and reservoir releases. These optimal values are then used as inputs to a Fuzzy Rule-Based (FRB) model to derive the general operating policies. Subsequently, the operating policies are evaluated in a simulation model while optimizing the parameters of the FRB model. The algorithm then gets back to the FRB model to establish the new set of operating rules using the optimized parameters. This iterative approach improves the value of the performance function of the simulation model and continues until the satisfaction of predetermined stopping criteria. The DPFRB performance is tested and compared to a model which uses the multiple regression based operating rules. Results show that the DPFRB performs well in terms of satisfying the system target performances.

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