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.
Results
Showing 1 to 20 of 3053 for “"Neural Networks"”.
-
Fuzzy neural networks
… neurons are modeled with artificial neural networks (ANNs or NNs). Neural networks, mathematically speaking, are a system of linked parallel equations that are solved simultaneously and iteratively. Initial research can be found in papers by McCulloch-Pitts (1943), Hebb (1949), …
-
Evolutionary neural networks
To create neural networks that work, one needs to specify a structure and the interconnection weights between each pair of connected computing elements. The structure of a network can be selected by the designer depending on the application, although the selection of interconnection weights is a …
-
Reductions of ReLU neural networks to linear neural networks and their applications
Deep neural networks are the main subject of interest in the study of theoretical deep learning, which aims to rigorously explain the incredible performance of these function classes in practice. Although a lot are understood about deep linear network (neural network with all linear activations), …
-
Evolving Learning Neural Networks
… has long been used to modify the artificial neural network in order to perform classification tasks. However, the standard fully connected layered design is often inadequate when performing such tasks. We show that evolution can be used to design an artificial neural network that learns …
-
Salience-affected neural networks
… using an ANN, creating a salience-affected neural network (SANN). We adapt an ANN to embody the capacity to respond to an input salience signal and to produce a reverse salience signal during testing. The input salience signal applied during training to each node has the effect of varying …
-
Connectome-Constrained Artificial Neural Networks
In biological neural networks (BNNs), structure provides a set of guard rails by which function is constrained to solve tasks effectively, handle multiple stimuli simultaneously, adapt to noise and input variations, and preserve energy expenditure. Such features are desirable for artificial neural …
-
Counting with convolutional neural networks
In this work, we tackle the question: Can neural networks count? More precisely, given an input image with a certain number of objects, can a neural network tell how many are there? To study this, we create a synthetic dataset consisting of black and white images with variable numbers of white …
-
Detecting Pulsars with Neural Networks
… is presented in this thesis. I developed and neural-network-based pipeline capable of correcting for the (*a priori* unknown) interstellar dispersion while suppressing a wide range of RFI signals and system effects. A convolutional neural network using dilated convolutions dedisperses pulsar …
-
Model Updating Using Neural Networks
… The approach uses frequency domain data and a neural network to produce estimates of the parameters being updated, yielding a model representative of the measured data. Current iterative methods developed to solve the model updating problem rely on minimization techniques to find the set of …
-
Multidimensional Wavelets and Neural Networks
… and wavelets under the usage of convolutional neural networks. We start by recalling substantial fundamentals of ideals, modules, Fourier analysis, filterbanks and multiresolution analyses, where the mentioned concepts are already considered in an arbitrary dimensional setting to prepare the …
-
Evolving neural networks in classification
… inspired hybrid intelligent system - evolving neural networks - that can be used in data mining, especially in classification problems. This hybrid system employs computational intelligence methodologies, such as neural networks and genetic algorithms."--Abstract, page iii.
-
Component Neural Networks of Morality
… researchers have largely studied various neural components of morality - including emotion, value, and mentalizing - in isolation. This has resulted in an informal and disjointed model for the neural mechanisms of morality. This dissertation is concerned with more formally identifying …
-
Neural networks in control engineering
… is to investigate the viability of integrating neural networks into control structures. These networks are an attempt to create artificial intelligent systems with the ability to learn and remember. They mathematically model the biological structure of the brain and consist of a large number of …
-
Neural networks for perpetual grouping
… researchers have investigated the application of neural networks to visual recognition, with much of the emphasis placed on exploiting the network's ability to generalise. However, despite the benefits of such an approach it is not at all obvious how networks can be developed which are capable of …
-
Towards Robust Deep Neural Networks
Deep neural networks (DNNs) enable state-of-the-art performance for most machine learning tasks. Unfortunately, they are vulnerable to attacks, such as Trojans during training and Adversarial Examples at test time. Adversarial Examples are inputs with carefully crafted perturbations added to benign …
Page 1 of 153