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Articles by M. Zakermoshfegh
Total Records ( 3 ) for M. Zakermoshfegh
  M. Shakiba , M. Teshnehlab , S. Zokaie and M. Zakermoshfegh
  In this study, a hybrid learning algorithm for training the Dynamic Synapse Neural Network (DSNN) to high accurate prediction of congestion in TCP computer networks is introduced. The idea behind this technique is to inform the TCP transmitters of congestion before it happens and to make transmitters decrease their data sending rate to a level which does not overflow the routers buffer. Traffic rate data are available in the format of time series and these real data are used to train and predict the future traffic rate condition. Hybrid learning algorithm aims to solve main problems of the Gradient Descent (GD) based method for the optimization of the DSNN, which are instability, local minima and the problem of generalization of trained network to the test data. In this method, Adaptable Weighted Particle Swarm Optimization (AWPSO) as a global optimizer is used to optimize the parameters of synaptic plasticity and the GD algorithm is used to optimize the weighted parameters of DSNN. As AWPSO is a derivative free optimization technique, a simpler method for the train of DSNN is achieved. Also the results are compared to GD algorithm.
  M. Zakermoshfegh , M. Ghodsian , S.A.A. Salehi Neishabouri and M. Shakiba
  River flow forecasting is required to provide important information on a wide range of cases related to design and operation of river systems. Since there are a lot of parameters with uncertainties and non-linear relationships, the calibration of conceptual or physically-based models is often a difficult and time consuming procedure. So it is preferred to implement a heuristic black box model to perform a non-linear mapping between the input and output spaces without detailed consideration of the internal structure of the physical process. In this study, the capability of artificial neural networks for stream flow forecasting in Kashkan River in West of Iran is investigated and compared to a NAM model which is a lumped conceptual model with shuffled complex evolution algorithm for auto calibration. Multi Layer Perceptron and Radial Basis Function neural networks are introduced and implemented. The results show that the discharge can be more adequately forecasted by Multi Layer Perceptron neural network, compared to other implemented models, in case of both peak discharge and base flow forecasting.
  M. Zakermoshfegh , S.A.A.S. Neyshabouri and C. Lucas
  The main objective in Conceptual Rainfall-Runoff (CRR) model calibration is to find a set of optimal model parameter values that provides a best fit between observed and estimated flow hydrographs, where the traditional trial and error manual calibration is very tedious and time consuming. Recently in multi dimensional combinatorial optimization problems, meta-heuristic algorithms have shown an encouraging performance with a low computational cost. In this study as a new application of Particle Swarm Optimization (PSO) algorithm, it is applied to automatic calibration of HEC-1 lumped CRR model and the methodology is tested in two example applications: a synthetic hypothetical example and a real case study for the Gorganrood river basin in the north of Iran. The results show encouraging performance of the proposed automated methodology.
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