Predictive models by Neural network
DEVELOPMENT AND APPLICATION OF A STAGE GATE PROCESS TO REDUCE THE UNERLYING RISKS OF IT SERVICE PROJECTS
... of predictive models have been designed for ...these models have some limitations and drawbacks. Existing predictive models have not been well established for TBI ...existing ...
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Combining DFT and QSAR result for predicting the biological activity of the phenylsuccinimide derivatives
... In this work, we have relied on the same data-base studied by Takayama et al. (1983) for N-phenylsuccinimides (fig. 1) using several statistical tools: Principal Components Analysis (PCA), Multiple Linear Regression ...
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Comparison of hospital charge prediction models for gastric cancer patients: neural network vs decision tree models
... tree models to predict hospital charges on gastric cancer patients and comparing their predictive abilities, so as to shed new lights on methodology for the prediction of the hospital charge on gastric ...
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Reconciling Predictive Coding and Biased Competition Models of Cortical Function
... the predictive coding network, processing stages have been equated with cortical regions (Friston, 2005; Rao and Ballard, ...competition network requires feedback from one cortical area to the ...
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Consumer choice prediction : artificial neural networks versus logistic models
... PNN models. The classification results show that the neural network models are better precision on the out-of-sample forecast than the logistic ...
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Control of a Double -Effect Evaporator using Neural Network Model Predictive Controller
... dynamic models describing Multiple Effect Evaporator are nonlinear ODE’s and it makes the performance of conventional PID controller ...The Neural network based model predictive controller are ...
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Relating Simple Sentence Representations in Deep Neural Networks and the Brain
... deep models and brain regions; and (3) is it possible for deep re- current models to synthesize brain data so that they can effectively be used for brain data aug- ...deep network architectures, ...
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Hybrid Neural Network Controller Design for a Batch Reactor to Produce Methyl Methacrylate
... the neural network forward model to predict the dynamics behavior and the neural network inverse model to control the process integrated with the dynamic ...Both neural network ...
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Improving of Crystal Size Distribution Control Based on Neural Network Based Hybrid Model for Purified Terephthalic Acid Batch Crystallizer
... Model Predictive Control (MPC) is one of model based approaches which can handle most common process characteristics and industrial requirements in a satisfactory ...way, neural networks offer alternative ...
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Design of Model Predictive Control based Direct Neural Controller for Surge Tank Application
... via neural controller. In this paper the inclusion of dynamic neural models in predictive control for a benchmark nonlinear process, surge tank is ...A Neural network ...
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Single Phase Induction Motor Drive with Restrained Speed and Torque Ripples Using Neural Network Predictive Controller
... strict demands on control and that can be met by traditional techniques alone. Apart from PI control, a completely different controller design paradigm such as model based control is used, these control algorithms ...
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Quantitative structure activity relationship studies of anti-proliferative activity of some indole derivatives combining DFT calculations and statistical results
... Resulting models. Moreover, the neural network ANN results (R= 99, MAE= ...better predictive capability than the MLR and ...the predictive power of this model to explore and propose new ...
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Global solar radiation forecasting based on meteorological data using artificial neural network
... back-propagation neural network with Levenberg- Marquardt (LM) training algorithm and Gradient descent back propagation (GD) ...the neural network, three different artificial neural ...
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Deep Neural Network Language Models
... deep neural networks. We followed the feed-forward neural network architecture and made the network deeper with the addition of several lay- ers of ...
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Structural combination of neural network models
... A preliminary analysis suggested that an NN architecture with 16 inputs (corresponding to all lags considered) and 2 neurons would be the best choice. Such architecture is used here with the CB and GA procedures ...
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Convolutional Neural Network Language Models
... addition of convolutional layers clearly improves it even further. Concretely, we observe a solid 11-26% reduction of perplexity compared to the feed-forward network after using MLP Convolution, depending on the ...
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Neural Network Models of Categorical Perception
... Finally, an important question concerns the role of the au- ditory model vis ´a vis the input stimuli. What would hap- pen if we were to apply our analyses directly to the input patterns without the (rather complex) ...
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The JHU Machine Translation Systems for WMT 2016
... Preliminary results with this approach were in- conclusive. For example, on the Russian-English newstest2015, the BLEU score is 27.27 for 1-best vs. 27.31 for reranking. On German-English new- stest2015, the BLEU score ...
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Maximum Wind Energy Extraction by Using Neural Network Estimation and Predictive Control of Boost Converter
... artificial neural network (ANN) is used to estimate the wind speed based on the rotor speed and the output ...a predictive controller is used to maximize the efficiency of the boost ...In ...
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Novel Ensemble Neural Network Models for better Prediction using Variable Input Approach
... Teegavarapu and Chandramouli (2005): In this work, eight methods are compared for estimating missing rainfall data. Three methods are recommended in the research work, namely, coefficient of correlation weighing methods, ...
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