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 117 for “"Back propagation"”.
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A constructive learning algorithm based on back-propagation
… of a dynamic algorithm based on the Back-Propagation learning algorithm. The algorithm constructs a single hidden layer as the learning process proceeds using individual pattern error as the basis of unit insertion. This algorithm is applied to several problems of differing type and …
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Neuromorphic regulation of dynamic systems using back propagation networks
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1988.
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Using the modified back-propagation algorithm to perform automated downlink analysis
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.
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Predicting permeability and flow capacity distribution with back-propagation artificial neural networks
… well log data. This technology overcomes the drawbacks caused by the inherent heterogeneity of the reservoir and lack of sufficient cores or pressure transient tests, allowing to define reservoir characterization within an acceptable accuracy while maintaining costs low. The methodology used in …
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Development of self-adaptive back propagation and derivative free training algorithms in artificial neural networks
… are developed. They are defined as self-adaptive back propagation, multi-directional and restart ANN training algorithms. The descent direction in self-adaptive back propagation training is determined implicitly by a central difference approximation scheme, which chooses its step size according to …
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An improvement of back propagation algorithm using halley third order optimisation method for classification problems
Back Propagation (BP) has proven to be a robust algorithm for different connectionist learning problems which commonly available for any functional induction that provides a computationally efficient method. This algorithm utilises first order optimisation method namely Gradient Descent (GD) method …
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A Back Propagation Based Spiking Neural Network Approach for Intelligent Link Decisions In Satellite Communication
… neural network. The spiking network has inbuilt Back Propagation (BP) implemented in the framework, Nengo. The system managed to achieve better accuracy even when activation was provided in hidden layer instead of output layers. Tweaking the firing rates, epochs and batch size of the data might …
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Dispersive neural networks for adaptive signal processing
Back-propagation is a popular method for training feed-forward neural networks. This thesis extends the back-propagation technique to dispersive networks, which contain internal delay elements. Both the delays and the weights adapt to minimize the error at the output. Dispersive networks can …
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Improved cuckoo search based neural network learning algorithms for data classification
… 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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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 Enhancement of Closed-Loop Controllers for Ill-Modeled Systems with Unknown Nonlinearities
… performance of the system improves. However, the back propagation algorithm was developed to update the weights of the feed-forward neural network in the open loop. Although the back propagation algorithm converged the weights in the closed loop, it worked very slowly. Two new update algorithms …
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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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Environmental site characterization via artificial neural network approach
… various environmental sites. A static ANN with back-propagation algorithm was used to model the environmental containment at a hypothetical data-rich contaminated site. The performance of the ANN profiling model was then compared with eight known profiling methods. The comparison showed that the …
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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
… correct for variable specimen geometry and wave propagation effects. In order to determine the variation between individual signals of the same class, the normalized spectra were clustered onto a two-dimensional feature space using a Kohonen self organizing map (SOM). Then 132 crack growth and …
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A Hybrid Neural Network Architecture for Texture Analysis in Digital Image Processing Applications
… types of neural network, self-organisation and back-propagation. Along with a brief resume of digital image processing concepts, an introduction to neural networks is provided. This contains appropriate documentation of the neural networks and test evidence is also presented to highlight the …
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Uncovering Efficient Learning and Initialisation Algorithms for Neural Networks Using Evolutionary Algorithms and Theoretical Analyses
… evidence showed it is superior to the standard back-propagation algorithm. The vanishing gradient problem is a long-standing obstacle to the training of deep ANNs using sigmoid activation functions. The methods proposed in the literature to improve the situation are not very successful. This …
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Time Reversed Acoustics and applications to earthquake location and salt dome flank imaging
… sources and image subsurface structures. The back-propagation process of the TRA experiment can be divided into the acausal and causal time domain. Studying the acausal process of TRA enables us to locate the source, such as an earthquake, inside a medium. The causal domain allows us to create …
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