Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 31 for “"Feed-forward Neural Network"”.
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Feed-Forward Neural Network (FFNN) Based Optimization Of Air Handling Units: A State-Of-The-Art Data-Driven Demand-Controlled Ventilation Strategy
… an optimal estimation accuracy. In this study, feed-forward neural network algorithm (FFNN) was proposed to estimate the zone occupancy using CO2 concentrations, observed occupancy data and the zone schedule. The occupancy prediction result was then utilized to optimize supply fan operation of …
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A comparative study of neural network algorithms.
Various Neural network models are investigated for Optical Character Recognition application and a Multi-layer Feed forward neural network is trained using a Fast training algorithm. Then the fast training algorithm is compared with the delta rule training algorithm. The various neural network …
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Modelling functions from sample data with classification applications
… We consider two main types of estimators: the feed-forward neural network and the nearest neighbor method.
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Mass flow measurement of multi-phase mixtures by means of tomographic techniques
… [3,4]. In addition, it has been shown that neural networks can be used to perform this reconstruction task [3,4]. In particular, a single-layer feed-forward neural network with a 1-of-C output encoding can be trained to perform a three-phase image reconstruction. Further, a double-layer …
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An hybrid architecture for multi-layer feed-forward neural networks.
Multi-layer feed-forward neural networks have the capability to classify and generalize, which are not achievable with other methods. The complete exploitation of their potential to full limit requires efficient hardware implementation. The two main problems of hardware realization; easy long term …
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A stochastic ensemble forecast model for geosynchronous relativistic electron fluxes
… field, and the Fluxpred model is a multi-layer feed-forward neural network with electron flux and summed Kp as input. Individual model results were combined using a multivariate regression to produce significantly better predictive results than any of the individual models alone. A stochastic …
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Utilizing data mining techniques and ensemble learning to predict development of surgical site infections in gynecologic cancer patients
… and regression trees, random forest, feed forward neural network, k-nearest neighbor, and support vector machines with linear kernel. Weighted stacked generalization was implemented to improve upon the individual base level model’s performance. The chosen meta level classifiers were …
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Modeling neural connectivity of caenorhabditis elegans
… solve this task: Bayesian inference, artificial neural networks, and ODE reverse engineering. The goal is to apply these methods to data recorded from C. elegans neurons and estimate the connections between these. The top two performing methods on simulated data were feed-forward neural networks …
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Machine Learning Enabled-Localization in 5G and LTE Using Image Classification and Deep Learning
… to a MAE of 9.82m using one Convolutional Neural Network (CNN). The third dataset uses measurements from an LTE mobile communication system along with satellite images that take place at the University of Denmark. The results achieve a MAE of 9.45 m fort he heirchichal approach in …
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Improving the efficiency and accuracy of nocturnal bird Surveys through equipment selection and partial automation
… to one-dimensional vectors for training a feed-forward neural network.
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A Neural Network-Based Methodology for Generating Spectrum -Compatible Earthquake Accelerograms
A new neural network based methodology for generating artificial earthquake spectrum compatible accelerograms is proposed using the learning capabilities of neural networks to develop the knowledge of the inverse mapping directly from the response spectra to earthquake accelerograms. A two-stage …
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Detection and Recognition of U.S. Speed Signs from Grayscale Images for Intelligent Vehicles
… speed limit from the detected region using a feed forward neural network. The proposed method was experimented on a number of traffic images and the results show that the region characteristics are more immune to different noisy conditions such as partial occlusions, cluttered backgrounds and …
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Automated posture analysis for detecting learner's affective state
… on the chair. Using Gaussian Mixtures and feed-forward Neural Network algorithms, the system classifies the postures in real time. It achieves an overall accuracy of 87.6% when it is tested with children's postures that were not included in the training set. Also, the children's posture …
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Micro-optic elements for a compact opto-electronic integrated neural coprocessor
… for the Compact Opto-electronic Integrated Neural coprocessor (COIN coprocessor) project. The COIN coprocessor is an implementation of a feed forward neural network using free-space optical interconnects to communicate between neurons. Prior work on this project had assumed these …
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Neural Network Enhancement of Closed-Loop Controllers for Ill-Modeled Systems with Unknown Nonlinearities
… system, an adaptive technique, using a neural network, was developed. A neural network is placed in series between the output of the fixed-gain controller and the input into the plant. The weights are initialized to values that result in a unity gain across the neural network, which is …
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Artificial Neural Networks based Modeling and Analysis of Semi-Active Damper System
… velocity sensitive damper model using artificial neural networks and essentially provide a 'black-box' model which encapsulates the non-linear behavior of the damper. A feed-forward neural network was developed by testing a semi active damper on a shock dynamometer at CenTiRe for multiple …
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Quasi-objective Nonlinear Principal Component Analysis and applications to the atmosphere
… Analysis (NLPCA) using three-hidden-layer feed-forward neural networks can produce solutions that over-fit the data and are non-unique. These problems have been dealt with by subjective methods during the network training. This study shows that these problems are intrinsic due to the …
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Utilizing Oil-Soluble Tracers to Evaluate the Production Profile in Multistage Fractured Horizontal Wells
… at the most sensitising parameters. Finally, a feed-forward neural network model with back-propagation error algorithm was coded in Matlab to estimate the cumulative oil production ratio when there is a big database of known input parameters of the same reservoir. The comparison between …
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BOOLEAN AND BRAIN-INSPIRED COMPUTING USING SPIN-TRANSFER TORQUE DEVICES
… STT based neuron (or ‘spin-neuron’) in various neural network architectures, such as hierarchical temporal memory and feed-forward neural network, for performing “human-like” cognitive computing, which show more than two orders of lower energy consumption compared to state of the art CMOS …
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Peramalan jumlah kandungan elektron menggunakan kaedah suapan ke hadapan rangkaian neural di Semenanjung Malaysia
… and TEC Monitor (GISTM) receiver using feed forward neural network (NN). It also aims to investigate the TEC forecasting method for radio wave propagation value during both equinox and solstices periods. Two GISTM locations at Universiti Kebangsaan Malaysia, 2�550 N, 101�460 E and …
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