River Flow Estimation from Upstream Flow Records Using Support Vector Machines

A novel architecture for flood routing model has been proposed and its efficiency is validated on several problems by employing support vector machines. The architecture is designed by including the inputs and observed and calculated outflows from the previous time step output. Whole observed data have been used for determining the model parameters in the heuristic methods given in the literature, which constitutes the major disadvantage of the existing approaches. Moreover, using the whole data for training may lead to overtraining problem that causes overfitting of estimations and data. Therefore, in this study, 60-90% of the data are randomly selected for training and then the remaining data are used for validation. In order to take the effects of the measurement errors into consideration, the data are corrupted by some additive noise. The results show that the proposed architecture improves the model performance under noisy and missing data conditions and that support vector machines can be powerful alternative in flood routing modeling

by

This report presents the studies carried out on two modifications suggested in the literature for Levenberg-Marquardt algorithm. The modifications are applicable to feed-forward neural networks. One modification [18], made on performance index, reduces computational complexity of the Levenberg-Marquardt algorithm, while the other one [17], made on calculation of the gradient information, improves convergence rate. These modifications have been performed on several benchmark problems. 1

Control and Parameter Estimation of PMSM by Runge-Kutta Model Based Predictive Control

12th International Symposium on Advanced Topics in Electrical Engineering (ATEE) -- MAR 25-27, 2021 -- Bucharest, ROMANIAIn this study, control and parameter estimation of a Permanent Magnet Synchronous Motor (PMSM) has been achieved by a relatively novel model predictive control method, which is referred to as the Runge-Kutta Model Based Predictive Control (RKMPC). Since PMSMs exhibit relatively nonlinear behavior and their parameter values are critical, they necessitate more robust control and parameter estimation than conventional control methods. Parameters of the PMSMs are subject to abrupt changes in load and temperature. These parameter fluctuations significantly affect the stability of the system. Therefore, in this study, beside the effective control of the system, an efficient parameter estimation has been established in the MATLAB/Simulink environment by RKMPC. The control and parameter estimation capability of RKMPC has been proven by simulation results

Parkinson’s Disease tremor classification – a comparison between Support Vector Machines and neural networks

Deep Brain Stimulation has been used in the study of and for treating Parkinson’s Disease (PD) tremor symptoms since the 1980s. In the research reported here we have carried out a comparative analysis to classify tremor onset based on intraoperative microelectrode recordings of a PD patient’s brain Local Field Potential (LFP) signals. In particular, we compared the performance of a Support Vector Machine (SVM) with two well known artificial neural network classifiers, namely a Multiple Layer Perceptron (MLP) and a Radial Basis Function Network (RBN). The results show that in this study, using specifically PD data, the SVM provided an overall better classification rate achieving an accuracy of 81% recognition