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 3009 for “"neural network"”.
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Neural network-based cost estimating
This thesis presents a neural network-based cost estimating method, developed for the generation of conceptual cost estimates for low-rise prefabricated structural steel buildings. Detailed cost estimating is current practice for this type of buildings, since cost estimators are often challenged by …
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Boolean Weightless Neural Network Architectures
… particular pertinence to the field of weightless neural networks. They have also been shown to have merit in their own right for the design of robust architectures. A major element of this is a collection of weightless Boolean sum and threshold techniques. These are fundamental building blocks …
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Improving Neural Network Classification Training
… a new set of general methods for improving neural network accuracy on classification tasks, grouped under the label of classification-based methods. The central theme of these approaches is to provide problem representations and error functions that more directly improve classification …
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Neural network-based material modeling
A neural network-based material modeling methodology for engineering materials is developed in this study. With this material modeling methodology, the stress-strain behavior of a material is captured within the distributed weight structure of a multilayer feedforward neural network trained …
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Neural network applications for finance
Thesis (M.S.)--Massachusetts Institute of Technology, Sloan School of Management, 1991.
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Deep Neural Network for Anomaly Detection
The rapid growth in diverse network devices (e.g., Internet of Things/IoT devices) and new cyber-physical systems (CPSs) services create new surfaces for cyberattacks. To safeguard these CPSs, anomaly detection (AD) that detects potential attacks/adversarial behaviors plays a pivotal role. This …
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Developing neural network applications using LabVIEW
Artificial Neural Networks (ANN) have gained tremendous popularity over the last few decades. They are considered as substitutes for classical techniques which have been followed for many years. Many neural network architectures and training algorithms have been developed so far. Different aspects …
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Neural network dynamics: a classical approach
Includes bibliographical references (pages 69-70).
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Artificial Neural Network-Based Robotic Control
<p>Artificial neural networks (ANNs) are highly-capable alternatives to traditional problem solving schemes due to their ability to solve non-linear systems with a nonalgorithmic approach. The applications of ANNs range from process control to pattern recognition and, with increasing importance, …
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Stable and symmetric convolutional neural network
DSpace SAF Submission Ingestion Package generated from Vireo submission #9397 on 2016-11-09 at 10:19:06
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Graph matching by graph neural network
Graph matching or network alignment refers to the problem of matching two correlated graphs. This thesis presents a deep Q learning based method, which represents the matching process by a graph neural network. By breaking the symmetry, the parameterized graph neural network is able to capture a …
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Inference neural network hardware acceleration techniques
… matrix multiplication. As a result, building a neural processing unit (NPU) beside the CPU to accelerate matrix multiplication is a popular approach. The NPU helps reduce the work done by the CPU, and often operates in parallel with the CPU, so in general, introducing the NPU gains performance. …
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Towards practical neural network meta-modeling
… efficient automated procedures for convolutional neural network (CNN) architecture search. We first introduce a novel approach for CNN architecture architecture using Q-learning, a popular value iteration algorithm from the reinforcement learning community for sequential decision problems. On the …
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Neural network based active structural control
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2000.
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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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Disentangling neural network representations for improved generalization
… increasingly broad perceptual capabilities of neural networks, applying them to new tasks requires significant engineering effort in data collection and model design. Generally, inductive biases can make this process easier by leveraging knowledge about the world to guide neural network design. …
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Optimizing relational search with embedded neural network
… partitioned full-text indexes and an embeddable neural network classifier in the query processing pipeline. The classifier is trained with self-supervision. It learns to optimize the partitioned indexes access pattern to accelerate query performance. Using textual features of user queries, the …
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