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 22 for “"Feedforward neural network"”.
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A generalised feedforward neural network architecture and its applications to classification and regression
… that plays an important role in sensory neural information processing systems. It has been extensively used to model some important visual and cognitive functions. It equips neurons with a gain control mechanism that allows them to operate as adaptive non-linear filters. Shunting …
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Development and analysis of hierarchical feedforward neural network systems for classification of motor neurone disease based on magnetic resonance spectra
… of a pattern recognition system based on neural networks to correctly distinguish between motor neurone disease (MND) patients and controls when presented with a nuclear magnetic resonance (NMR) spectrum. The NMR spectra are pre-processed to obtain consistent data, and statistical …
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Temporal Neural Networks and Transient Analysis of Complex Engineering Systems
… Input and State (TIS) Gamma memory for neural network architectures for temporal processing. The novel LOGF neuron model extends the static neuron model by incorporating into it a short-term memory structure in the form of a digital gamma filter. A feedforward neural network made up of …
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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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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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A neural fuzzy approach to modeling the thermal behavior of power transformers
… Neuro-Fuzzy Inference System (ANFIS), Multilayer Feedforward Neural Network (MFNN) and Elman Recurrent Neural Network (ERNN) to modeling and prediction of the top and bottom-oil temperatures for the 8 MVA Oil Air (OA)-cooled and 27 MVA Forced Air (FA)-cooled class of power transformers. The models …
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Neural networks as a tool for statistical modeling
Neural networks are being used increasingly often as alternatives to traditional statistical models. As a result, their performance needs to be examined in a statistical framework. Following a brief overview of many types of neural networks, details concerning the implementation of the single …
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Recurrent convolutional neural networks as models of biological object recognition
Deep feedforward neural network models of vision dominate in both computational neuroscience and engineering. However, the primate visual system contains abundant recurrent connections. In this thesis, we investigate the addition of recurrent connections to the popular framework of convolutional …
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Bias correction of global circulation model outputs using artificial neural networks
… new bias correction approach using a three layer feedforward neural network to reduce the biases of climate variables (temperature and precipitation) over northern South America. Air and skin temperature, specific humidity, net longwave and shortwave radiation are used as inputs for the bias …
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Spiking Neural Network with Memristive Based Computing-In-Memory Circuits and Architecture
… There are two main types of Artificial Neural Networks (ANNs), Feedforward Neural Network (FNN) and Recurrent Neural Network (RNN). In this thesis we first study the types of RNNs and then move on to Spiking Neural Networks (SNNs). SNNs are an improved version of ANNs that mimic …
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Parameter identification for vector controlled induction motor drives using artificial neural networks and fuzzy principles
… possible convergence results using artificial neural networks and fuzzy logic systems. The thesis focuses mainly on identifying the rotor resistance, which is the most critical parameter for RFOC. Limitations of PI and fuzzy logic based estimators were identified. Artificial neural network …
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Artificial intelligence-enabled transaction prediction.
… when shopping online. This project designed a feedforward neural network (FNN) for binary classification to predict whether a transaction is made in the Santander customer transactions dataset. The input layer had 202 neurons, consisting of the data features, two hidden layers of 10 neurons …
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Percussion Based Detection Method for Localization of Pipe Inspection Gauge using Advanced Machine Learning Classification and Clustering Techniques.
… (Support Vector Machine, Convolutional Neural Network + Long-Short Term Memory Network, Feedforward Neural Network, gradient boosting, Gaussian Mixed Model and K-means clustering) to find cost effective localization techniques. Results showed that MFCC feature extraction and …
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Deep learning for DDoS attack detection in mobile edge computing
… faster response times. However, MEC like other networks is facing cyber security issues, particularly the Distributed Denial of Service attack (DDoS) which has become common in recent years. Compared to cloud environments, MEC networks have their own constraints, including limited resources and …
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Machine Learning Algorithms and Applications in Health Care
… are equivalent in terms of modeling power to neural networks. Specifically, given a neural network (feedforward, convolutional, or recurrent), we construct a decision tree with hyperplane splits that has identical in-sample performance. Building on previous research showing that given a …
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Choice Modeling and Assortment Optimization on the Transformer Model
… is defined through a trained transformer network. This leads to a new class of neural network-based discrete choice models, which we call transformer choice models. The universal approximation property of the transformer network ensures that our model can approximate any discrete choice …
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Locating senior walking frame users in crowded indoor environments
… generating image descriptors and a three layer feedforward neural network for producing location estimates. Shop fronts and their corresponding neighbourhood areas are used as classes for training the neural network. The performance of this approach that was evaluated in a real shopping centre …
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Multimedia Traffic Management over Software-Defined Networking
… traffic transmission, achieving efficient network management and ensuring quality of service for diverse video services become a challenging task. Meanwhile, the introduction of virtualisation through the integration of software defined networking allows for the effective control of traffic …
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Manufacturing of thermosetting polymers and composites using frontal polymerization: A numerical study
… To that effect, we first develop and implement a FeedForward Neural Network (NN) model in Python. We then train the model using the input data generated with normal distribution and corresponding output data extracted from the steady-state solver. Finally, in an `inverse approach', we utilize the …
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Process control of a laboratory combustor using neural networks
Active feedback and feedforward-feedback control systems based on static-trained feedforward multi-layer-perceptron (FMLP) neural networks were designed and demonstrated, by experiment and simulation, for selected species from a laboratory two stage combustor. These virtual controllers functioned …
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