Determining parameter r using the IPARM model
Parameter Estimation of Weighted Erlang Distribution Using R Software
... scale parameter of Weighted Erlang distribution is offered for consideration under Squared Error Loss Function (SELF), Quadratic Loss Function (QLF) and entropy Loss Function (ELF) using Jeffrey`s, ...
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Model parameter estimation using coherent structure colouring
... Eulerian model descriptions is complicated by the nonlinear relationship between the underlying flow velocities and the paths taken by individual parcels of fluid or fluid ...
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Wave Parameter Hindcasting in a Lake Using the SWAN Model
... Wave Parameter Hindcasting in a Lake Using the SWAN Model ...spectral model called SWAN has been evaluated in the prediction of wave ...the model has been executed in a nonstationary ...
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Determining the optimal decision delay parameter for a linear equalizer
... − 1.263, 0.338, 0.799, 0.879, 0.799, 0.338, − 1.263. The above results indicate that d = 0 or d = 6 re- sults in a nonlinearly separable equalization problem with the worst BER performance, while the best BER performance ...
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Methods for Determining the Solubility Parameter of Additives for Lubricating Oils
... respectively R = ...solubility parameter values, 9 and another improved method for determining the Hansen solubility parameters were used to determine the above ...by using the intrinsic ...
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Gradient-based iterative parameter estimation for bilinear-in-parameter systems using the model decomposition technique
... Introduction Parameter estimation plays a major role in signal processing [1, 2], control system design ...bilinear-in-parameter model can be used to describe the block-oriented nonlinear system, ...
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Parameter estimation for a model of
... Langmuir model has an explicit solution whose form is well known, and so standard regression techniques are used to estimate the parameters of this ...obtained using a set of initial parameter ...
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Optimization of CCIR Pathloss Model Using Terrain Roughness Parameter
... error using least square method. The results show that the untuned CCIR model has a RMSE of ...CCIR model and the pathloss predicted by the standard deviation of elevation tuned CCIR model ...
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Parameter Estimation of Vehicle Handling Model Using Genetic Algorithm
... Some of the parameters are known or easily measurable. For example, geometrical properties, such as tread width and wheelbase, are known. However, there are some parameters that are unknown and directly immeasurable, ...
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Parameter Estimation of a Distributed Hydrological Model Using a Genetic Algorithm
... 2.2. Parameter Estimation Some of the parameters of the production function and heat budget can be calculated or estimated based on the known physical processes underlying these functions, whereas the rest must be ...
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Prediction blood pressure parameter by using neuro-fuzzy model
... a model that can predict the Mean Arterial ...the parameter that correlate with the blood pressure parameter based on Mean Arterial ...Neuro-Fuzzy model using MATLAB as the ...
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Parameter estimation of box-jenkins model using genetic algorithm
... Besides that, this method is able to identify the best model when there is given a set of data. Next, Box-Jenkins approach can handle with complex data patterns using relatively well specified rules. ...
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Optimization of Hata Pathloss Model Using Terrain Roughness Parameter
... pathloss model based on terrain roughness parameter is ...error using least square method. The results show that the untuned Hata model has a RMSE of ...Hata model and the pathloss ...
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Determining the form of ordinary differential equations using model inversion
... The model inversion approximation extracts parameters from within a nonlinear function so that they are exposed in a linear position convenient for further ...this model inversion is used to investigate ...
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Fixing the c Parameter in the Three-Parameter Logistic Model
... item parameter estimation procedure using the maximum likelihood estimation method often was unsuccessful in obtaining converged estimates when the true c-parameter value was large (> ...c- ...
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Model-Based Parameter Estimation for Fault Detection Using Multiparametric Programming
... presented approach. The proposed approach successfully estimates the model parameters and detects the faults through simple function evaluation of explicit functions. 1. INTRODUCTION Fault can be defined as an ...
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Identification of hydrological model parameter variation using ensemble Kalman filter
... Hydrological model parameters play an important role in the ability of model ...however, model parameters may vary with time un- der climate change and anthropogenic ...balance model (TWBM) by ...
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Process verification of a hydrological model using a temporal parameter sensitivity analysis
... dominant model processes (Saltelli et ...in model parameter values influences the variance of the model output ...direct model output instead of performance metrics, ...specific ...
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Development of health parameter model for risk prediction of CVD using SVM
... performed using health factors which are often based on the Framingham ...the model for the local ...linear model trained using local database was an improvement on Framingham model, ...
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Grid Parameter estimation using Model Predictive Direct Power Control
... III. D EAD -T IME C OMPENSATION In order to adapt the proposed inductance estimation method to practical converter implementation, the presence of dead-times in the devices switching needs to be taken into account. In ...
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