Results from Neural Network Modelling
Neural network modelling of non linear hydrological relationships
... Eight neural network models were developed; four full and partial emulations of the Xinanjiang Rainfall-Runoff Model; four further solutions developed on an identical set of inputs and a calculated runoff ...
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Artificial Neural Network Modelling for Polyethylene FSSW Parameters
... Figure 3. The eect of plunged depth on lap-shear tensile fracture load. shear fracture load. A sample dataset of laboratory experiments is shown in Table 2. The input-output data can be actual or normalized. It is ...
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Neural network modelling of the process of methylbutene dehydranation into isoprene
... mathematical modelling of the catalyst process and specification of the conditions of its commercial ...detailed modelling of chemical technological ...When modelling, a number of simplifications and ...
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Modular neural network modelling for long range prediction of an evaporator
... modular neural network model of a three-effect, falling-film ...each modelling a specific element of the overall ...modular neural model is demonstrated for long-range prediction by comparing ...
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Numerical and artificial neural network modelling of friction stir welding
... predicted results of 4 input method with MLP topology is given in Table 3-5, in which the MREs for all the test values are calculated and an averaged MRE value is mathematically calculated based on these ...
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Neural Network Modelling and Multi-Objective Optimization of EDM Process
... Conclusions From the main effect plots it can be concluded that for training MSE and R, 32 ...model. From ANOVA it can be conclude that ...the neural architecture was found insignificant for test MSE ...
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Pavement Condition Forecasting Through Artificial Neural Network Modelling
... the network is ...artificial neural network to produce outputs that are equal or close to ...artificial neural network is then capable of generating reasonable results given new ...
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Artificial Neural Network Modelling of Vibration in the Milling of AZ91D Alloy
... the modelling was performed by the semi-discreti- zation method which consists in transforming the models and equations of continuous func- tions into their discrete ...these results, it was found that the ...
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Product Cost Management Structures: a review and neural network modelling
... perspective neural networks have been shown to have very good modelling ...These results are for non-product cost financial data like market index forecasting (Walczak ...(working from the ...
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Layer-Recurrent Neural Network Modelling of Reactive Distillation Process
... gathered from the literature, Giwa and Karacan (2012a) used three different types of delayed neural network (Nonlinear AutoRegressive (NAR), Nonlinear AutoRegressive with eXogenous inputs (NARX) and ...
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Results of Fitted Neural Network Models on Malaysian Aggregate Dataset
... these results, BPNN-NARMA model performed worse than the BPNN-NAR ...used. From Table 1, over the input and error lags, if the network is assigned with fewer nodes or huge load of nodes, the ...
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Some thoughts on neural network modelling of micro-abrasion-corrosion processes
... RAN modelling results RAN has been used to model the micro-abrasion-corrosion process for two pairs of materials as given ...this network with the MLP in estimating the values of Kac and ...a ...
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Artificial Neural Network Modelling of Traffic Noise in Agra Firozabad Highway
... it results in high health risk having long term effect. From past few decades there has been a lot of research in this area but still it is a major challenge to minimize the effect due to noise and even ...
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SCALED CONJUGATE GRADIENT NEURAL NETWORK MODELLING FOR PREDICTION OF CBR OF SOILS
... Artificial neural networks (ANNs) are like processing system of human brain and consists of layers namely input, hidden and ...calculated results while targets are the results obtained in the real ...
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Artificial neural network for modelling rainfall-runoff
... runoff from 2003-2012. CONCLUSION In the present research, an Artificial Neural Network (ANN) was used to predict daily river runoff as a function of daily evapotranspiration and rainfall for the ...
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Neural Network Algorithm-based Fall Detection Modelling
... researcher from the previous ...presents results of modelling for fall detection system by using nonlinear autoregression neural network NARnet ...by network training function; ...
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Artificial neural network-based modelling for daylight evaluations
... Neural Network Validation Predictions for 10% of randomly selected data from ‘A’ were less than 1 DA away from the simulated DA level (Table ...yielded results close to the MAE, showing ...
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Modelling and simulation of surface roughness obtain from micro milling by using artificial neural network
... Using Neural Networks The increasing tendency to anticipate using artificial neural networks resulted in a remarkable increase in research activities in the recent ...Artificial neural networks are ...
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Artificial Neural Network Application in Modelling Revenue Returns from Mobile Payment Services in Kenya
... the Neural Network method is more precise than the regression ...of neural networks with four kinds of models in 283 firms: one-variable linear models, multi-variable linear models, one-variable ...
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Modelling Systemic Risk using Neural Network Quantile Regression
... on neural network quantile ...estimation results we model systemic risk spillover eects across banks by considering the marginal eects of the quantile regression ...risk network represented by ...
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