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 14 of 14 for “"Neural Network Learning"”.
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Neural Network Learning for Time-Series Predictions Using Constrained Formulations
… along with violation guided backpropagation to neural network learning for near noiseless time-series benchmarks, we achieve much improved prediction performance as compared to that of previous work, while using less parameters. For noisy time-series, such as financial time series, we have …
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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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Neuromorphic deep convolutional neural network learning systems for FPGA in real time
Deep Learning algorithms have become one of the best approaches for pattern recognition in several fields, including computer vision, speech recognition, natural language processing, and audio recognition, among others. In image vision, convolutional neural networks stand out, due to their …
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Characterizing the Energy Requirement of Computer Vision
The energy requirements of neural network learning are growing at a rapid rate. Increased energy demands have caused a global need to seek ways to improve energy efficiency of neural network learning. This thesis aims to establish a baseline on how adjusting basic parameters can affect energy …
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New algorithm for neural network data discrimination applied to Markarian 421 high energy gamma rays
A new neural network learning algorithm, called the Umbrella Algorithm, is developed and analysed. Its generalization, which does not exhibit over-specialisation, is observed in the EXOR problem and in an artificial data discrimination (Toy Data) problem. The learning time is found to be about 1/15 …
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PACKET FILTER APPROACH TO DETECT DENIAL OF SERVICE ATTACKS
… quantity of packets or connections to crash its network resources, bandwidth, equipment, or servers. Packet filtering methods are the most known way to prevent these attacks via identifying and blocking the spoofed attack from reaching its target. In this project, the extent of the DoS attacks …
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Information fusion schemes for real time risk assessment in adaptive control systems
… Flight Control System (IFCS) deploys a neural network for in-flight aircraft failure accommodation. Verification and validation (V&V) of adaptive systems is a challenging research problem. Our approach to V&V relies on real-time monitoring of neural network learning. Monitors detect …
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Learning, Reasoning, and Planning with Relational and Temporal Neural Networks
… an overview of a neuro-symbolic framework for learning, reasoning, and planning with relational and temporal neural networks. The key idea is to exploit a structural bias in neural network learning that enables us to describe complex relational-temporal events and actions. These structures form …
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Adaptive function modal learning neural networks
Modal learning method is a neural network learning term that refers to a single neural network which combines with more than one mode of learning. It aims to achieve more powerful learning results than a neural network combines with only one single mode of learning. This thesis introduces a novel …
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Neural Networks for Music Emotion Recognition and Social Tags Emotion Representation
… contributed to this area. With the emergence of neural networks, MER research has evolved from traditional machine learning methods combined with acoustic features to neural network learning methods combined with multi-source features. However, research gaps still exist in the following aspects. …
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Symbolic and connectionist machine learning techniques for short-term electric load forecasting
This work applies connectionist neural network learning techniques and symbolic machine learning techniques to the problem of short-term electric load forecasting. The short-term electric load forecasting problem considered here is the prediction of bus loads one day ahead. The forecast quantities …
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Achieving Near-Natural Locomotion in Transfemoral Amputees - A Control Theoretic Approach
Amputation of the lower limb is prescribed to address conditions such as trauma, vascular issues, tumors, neuropathy, frostbite, and complications from diabetes. Post-surgery, the individual has to be fitted with a prosthetic limb to regain mobility. While a good-fitting, well-designed prosthetic …
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Predikce deště z meteoradaru
Tato práce se zabývá předpovědí počasí s využitím meteoradarových snímků a některých dalších souvisejících faktorů prostřednictvím výpočetního modelu neuronové sítě. Klade si za cíl prozkoumat možnosti predikce pomocí tohoto modelu a experimentálně stanovit co nejúspěšnější konfiguraci modelu pro …