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 124 for “"backpropagation"”.
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Recursive backpropagation algorithm applied to a globally recurrent neural network
In general, recursive neural networks can yield a smaller structure than purely feedforward neural network in the same way infinite impulse response (IIR) filters can replace longer finite impulse response (FIR) filters. This thesis presents a new adaptive algorithm that trains recursive neural …
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Efficient Control of DC Servomotor Systems Using Backpropagation Neural Networks
… the operation of the model network using backpropagation learning technique. The proposed combination of the two neural networks will be able to deal with the nonlinear parameters and dynamic factors involved in the original servomotor system and hence generate the proper control of the …
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Implementation of a New Sigmoid Function in Backpropagation Neural Networks.
… the use of a new sigmoid activation function in backpropagation artificial neural networks (ANNs). ANNs using conventional activation functions may generalize poorly when trained on a set which includes quirky, mislabeled, unbalanced, or otherwise complicated data. This new activation function is …
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A backpropagation neural network in an address block classification system
… system developed at MITRE. The" system uses a backpropagation neural network trained to discriminate the frequency characteristics of address blocks from other candidates. The current system is trained on magazine flat mail.
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Enhanced Neural Network Training Using Selective Backpropagation and Forward Propagation
… algorithms on the two datasets. The selective backpropagation algorithm shows a reduction of up to 93.3% of backpropagations completed, and the selective forward propagation algorithm shows a reduction of up to 72.90% in forward propagations and backpropagations completed compared to baseline …
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An exploration of the robustness of traditional regression analysis versus analysis using backpropagation networks
… the robustness of regression analysis and backpropagation networks in conducting data analysis. Robustness is viewed as the degree to which a technique is insensitive to abnormalities in data sets, such as violations of assumptions. The central focus of regression analysis is the …
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Extraction of Arabic word roots: An Approach Based on Computational Model and Multi-Backpropagation Neural Networks
… is based on artificial neural network trained by backpropagation learning rule. In this proposed phase, we formulate the root extraction problem as a classification problem and the neural network as a classifier tool. This study demonstrates that a neural network can be effectively used to ex- …
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A study of using backpropagation and a new neural net algorithm for edge detecting in binary images
… : 1. the experimental results of using the basic backpropagation algorithm for edge detecting in binary images. 2. the development of a new neural computing algorithm and the results of applying it on edge detecting in binary images. 3. evaluation of the new algorithm. 4. comparison of the new …
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A Methodology for the Prediction of the Empennage In-Flight Loads of a General Aviation Aircraft Using Backpropagation Neural Networks
<p>Backpropagation neural networks have been used to predict strain resulting from the maneuver in-flight loads in the empennage structure of a Cessna 172P. The purpose of this research was to develop a methodology for the prediction of strain in the tail section of a general aviation aircraft and …
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Analisis penilaian kinerja karyawan untuk mengetahui kualitas kelayakan kerja menggunakan jaringan syaraf tiruan backpropagation: Studi kasus pada PG. Kebon Agung Malang
… karyawan menggunakan jaringan syaraf tiruan Backpropagation ini digunakan untuk mengambil keputusan kelayakan kualitas kerja pada karyawan yang berdasarkan kepada kriteria-kriteria penilaian yang telah ditetapkan oleh organisasi (perusahaan). Dalam pemrograman ini memiliki 23 variabel input …
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Evolving Neural Networks with HyperNEAT and Online Training
… The learning methods explored are: supervised backpropagation, reinforcement backpropagation, Hebbian learning, and temporal difference learning. These are compared against the baseline HyperNEAT algorithm with no online learning. Next, the methodology of applying online learning is extended in …
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An investigation of neural networks for image processing applications
… Three neural network models, namely the backpropagation network, the Hopfield network and the competitive network, are studied. First, the learning algorithms for backpropagation networks and competitive neural networks are studied and new algorithms are developed. The applications of …
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Neuronové sítě a genetické algoritmy
… NEAT porovnán s klasickými učícími metodami backpropagation (pro dopředné neuronové sítě) a backpropagation through time (pro rekurentní neuronové sítě) a to z hledika rychlosti učení, kvality odezvy sítě i jejich závislosti na velikosti sítě.
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The use of a neural network to recognize placental insufficiency from blood flow velocity waveforms in the umbilical cord
… associated with placental insufficiency. Eleven backpropagation neural networks have been developed and trained based on the waveforms that are generated from the foetal mathematical model (developed in previous research) at both ends of the cord. Only two networks trained successfully. These two …
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Neural networks for perpetual grouping
… way, it is shown how MLPs which are trained via backpropagation to perform individual grouping tasks, can be brought together into a novel, large scale network capable of determining the perceptual significance of the whole input pattern. Finally the applicability of such significance values for …
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An investigation into the applicability of neural networks to multi-performance measure dispatching in a dynamic, single machine shop
This thesis investigates the applicability of backpropagation neural networks to production order dispatching in a dynamic, single machine shop where the achievement of multiple performance measures is desired. There has been relatively little research done in this area so the objectives center …
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New algorithm for neural network data discrimination applied to Markarian 421 high energy gamma rays
… The learning time is found to be about 1/15 of backpropagation learning time for the parity problem. The algorithm is applied to cosmic high energy gamma ray detection and is further used for determining the spectral index of the Markarian 421 source.
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The functionality of spatial and time domain artificial neural models.
… and derived methods based on the Delta Rule, Backpropagation, Genetic Algorithms and associated evolutionary techniques. This new neural unit has been presented as a controllable and more highly functional alternative to previous models. The work on the Taylor Series neuron moved into …
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Riemannian Metric Learning via Optimal Transport
… efficiently optimize our model's objective using backpropagation. Using this learned metric, we can nonlinearly interpolate between probability measures and compute geodesics on the manifold. We show that metrics learned using our method improve the quality of trajectory inference on scRNA and …
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Ultimate Strength Prediction in Fiberglass/Epoxy Beams Subjected to Three-Point Bending Using Acoustic Emission and Neural Networks
… percent of the average ultimate load.</p> <p>A backpropagation neural network was constructed to predict the ultimate failure load using these AE amplitude distribution data. Architecturally, the network consisted of a 61 processing element input layer for each of the event frequencies, a 13 …
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