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 22 for “"back-propagation neural network"”.
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Prediction of Fatigue Life in 7075-T6 Aluminum from Neural Network Analysis of Acoustic Emission Data
… system, a Kohonen self-organizing map, and a back-propagation neural network, AE data from 7075-T6 aluminum specimens were used to classify failure mechanisms and predict the number of fatigue cycles to failure. AE waveforms were captured from 40 notched tensile specimens during the low-cycle …
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Neural Network Fatigue Life Prediction in 7075-T6 Aluminum from Acoustic Emission Data
… coupled with a Kohonen self organizing map and a back propagation neural network were used to perform the analysis. AE waveforms were recorded during fatigue cycling of twenty-four notched 7075-T6 aluminum specimens using broad-band piezoelectric transducers. A Kohonen self organizing map was used …
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Low Proof Load Prediction of Ultimate Strengths of Fiberglass/Epoxy I-Beams Using Acoustic Emission
… A second analysis was performed utilizing a back propagation neural network. The inputs to the network included a categorical variable for the epoxy type together with the amplitude frequencies from 30-100 dB. The optimized network contained two hidden layers having nine neurons apiece. Here …
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Neural Network Detection of Fatigue Crack Growth in Riveted Joints Using Acoustic Emission
… research was to demonstrate the capability of neural networks to discriminate between individual acoustic emission (AE) signals originating from crack growth and rivet rubbing (fretting) in aluminum lap joints. AE waveforms were recorded during tensile fatigue cycling of six notched and riveted …
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Improved cuckoo search based neural network learning algorithms for data classification
Artificial Neural Networks (ANN) techniques, mostly Back-Propagation Neural Network (BPNN) algorithm has been used as a tool for recognizing a mapping function among a known set of input and output examples. These networks can be trained with gradient descent back propagation. The algorithm is not …
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Acoustical and flow characteristics of a cough as an index of pulmonary function in the guinea pig
… were used to train a single neuron feed-forward back propagation neural network. The classification system was able to correctly discriminate between members of the high and low airway constriction groups with an accuracy of 0.946 and a sensitivity and specificity of 0.893.
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Combined bottom-hole pressure calculation procedure using multiphase correlations and artificial neural network models, A
Artificial neural network (ANN) techniques have been adopted to predict bottom-hole pressures and have proved to have better, or at a minimum equivalent prediction performance than conventional prediction methods such as multiphase correlations and mechanistic modeling. With the applied design, the …
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Neural Network Prediction of Math and Reading Proficiency as Reported in the Educational Longitudinal Study 2002 Based on Non-Curricular Variables
… to compare the performance of a three-layer back propagation neural network to that of traditional multiple linear regression in predicting math and reading proficiency from 103 non-curricular variables collected in the National Center for Educational Statistics' 2002 Educational Longitudinal …
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Neural network estimation of disturbance growth and flow field structure of spatially excited jets
Neural networks were applied to the estimation problem consisting of identifying both nearfield and quasi-farfield flow structures of a jet undergoing spatial mode excitation. The evolution of disturbances introduced by a spatially excited jet spans a linear and nonlinear regime in the downstream …
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A Hybrid Neural Network Architecture for Texture Analysis in Digital Image Processing Applications
A new hybrid neural network model capable of texture analysis in a digital image processing environment is presented in this thesis. This model is constructed from two different types of neural network, self-organisation and back-propagation. Along with a brief resume of digital image processing …
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Detecting and Modelling Stress Levels in E-Learning Environment Users
… tested, namely certainty factors, feedforward back-propagation neural network and adaptive neuro-fuzzy inference system. The best classifier was then integrated into the ITS stress inference engine, which is designed to decide necessary adaptation, and to provide analytical information of …
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Advancing profiling sensors with a wireless approach
… prior to its classification through a back-propagation neural network. Such a wireless detector configuration advances deployment options for N-IR, retro-reflective profiling sensors.
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A web-based intelligent learning environment for the teaching of industrial continuous quality improvement
… experience to its users. Two artificial neural network modules (a Fuzzy Adaptive Resonance Theory neural network and a back-propagation neural network) are implemented to facilitate the understanding of statistical tools and different types of variation in a realistic process.
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Fatigue Life Prediction of Edge-Welded Metal Bellows Using Neural Networks and Multiple Linear Regression
… Fourteen tests were used to train and test a back-propagation neural network for prediction of bellows cycle life. The input data consisted of a material identifier, AE parameter data consisting of the amplitude distribution (50-100 dB) of the first 250 hits, and the final cycle life. The …
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An intelligent system approach for predicting the risk of heart failure
… Fuzzy Inference System approach and Feed Forward Back Propagation Neural Network approach to develop intelligent systems based on some input parameters. There are so many factors that can affect the system of the heart. This research uses eleven major parameters to predict the risk of heart …
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Assessment of taxol activity: Bioavailability in human physiological fluids and QSAR of taxol analogues based on a neural network design
… and food industries. Chapter three describes a back-propagation neural network (BPNN) design for 50 taxol analogues. Initial system consists of 27 calculated structural descriptors, while the outputs are the measured antitumor activities against 4 types of cancer (breast, ovarian, lung and the …
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Revenue Management in High-Density Urban Parking Districts: Modeling and Evaluation
… model is developed that uses an artificial neural network procedure for online reservation decision-making. Next, the work evaluates whether the implementation of a parking RM system in a dense urban parking district (and thus avoiding "trial-and-error" behaviors exhibited by drivers) …
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A thesis on the application of neural network computing to the constrained flight control allocation problem
The feasibility of utilizing a neural network to solve the constrained flight control allocation problem is investigated for the purposes of developing guidelines for the selection of a neural network structure as a function of the control allocation problem parameters. The control allocation …
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The use of artificial intelligence techniques for power analysis
… An overview of the many different types of Neural Network has been carried out explaining terminology and methodology along with a number of techniques used for their implementation. Although the mathematical concepts are not new, many of them were recorded more than fifty years ago, the …
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Advanced Occupancy Measurement Using Sensor Fusion
… using a genetic based search. Finally, a back-propagation neural network model was adopted to fuse candidate multi-sensory features for estimation of occupancy levels. Several test cases were implemented to demonstrate and evaluate the effectiveness and feasibility of the proposed …
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