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 Networks"”.
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Learning dynamics in feedforward neural networks
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.
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Modeling human vision using feedforward neural networks
… based on I-Theory [50], and uses convolutional neural networks. We investigate the explanatory power of this approach using the task of object recognition. We find that the model has important similarities with neural architectures and that it can reproduce human perceptual phenomena. This work …
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Sensitivity of Feedforward Neural Networks to Harsh Computing Environments
Neural Networks have proven themselves very adept at solving a wide variety of problems, in particular they accel at image processing. However, it remains unknown how well they perform under memory errors. This thesis focuses on the robustness of neural networks under memory errors, specifically …
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Application of multilayer feedforward neural networks to precipitation cell-top altitude estimation
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.
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Short-Term Wind Speed Time Series Forecasting Using Artificial Neural Networks
… power have become integral parts of modern power networks. Short-term wind speed prediction is crucial for smart grids, as it can help balance the demand and supply, as well as set the energy price in the market.</p> <p>In this thesis, we simulate and compare various neural network models for …
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Global Search Methods for Solving Nonlinear Optimization Problems
… problems, including (a) the learning of feedforward neural networks, (b) the design of quadrature-mirror-filter digital filter banks, (c) the satisfiability problem, (d) the maximum satisfiability problem, and (e) the design of multiplierless quadrature-mirror-filter digital filter banks. …
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Detection of Urban Damage Using Remote Sensing and Machine Learning Algorithms: Revisiting the 2010 Haiti Earthquake
… study evaluates the effectiveness of multilayer feedforward neural networks, radial basis neural networks, and Random Forests in detecting earthquake damage caused by the 2010 Port-au-Prince, Haiti 7.0 moment magnitude (Mw) event. Additionally, textural and structural features including entropy, …
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A neural network-based controller for a single-link flexible manipulator using the inverse dynamics approach
… for rigid-link manipulators, two multi-layer feedforward neural networks are developed to learn the nonlinearities of the system dynamics. The re-defined output scheme is used by feeding back this output to guarantee the minimum phase behavior of the resulting closed-loop system. No a priori …
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Autoregressive Neural Network Processes - Univariate, Multivariate and Cointegrated Models with Application to the German Automobile Industry
… Linear autoregressive processes are extended by neural networks to overcome the problem of nonlinearity. This idea is based on the universal approximation property of single hidden layer feedforward neural networks of Hornik (1993). Univariate Autoregressive Neural Network Processes (AR-NN) as …
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Online Non-linear Prediction of Financial Time Series Patterns
… online supervised learning are carried out on Feedforward Neural Networks (FNNs) using these features. The FNN output is a point prediction of measured time-series feature fluctuations (log differenced data) in the future (ex-post). Weight initializations for these networks are implemented with …
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Universal approximation of input-output maps and dynamical systems by neural network architectures
It is well known that feedforward neural networks can approximate any continuous function supported on a finite-dimensional compact set to arbitrary accuracy. However, many engineering applications require modeling infinite-dimensional functions, such as sequence-to-sequence transformations or …
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Forecasting Energy Consumption using Sequence to Sequence Attention models
… commonly used for energy forecasting, such as FeedForward Neural Networks, are not well-suited for interpreting the time dimensionality of a signal. Consequently, this thesis applies Sequence-to-Sequence (S2S) Recurrent Neural Networks (RNNs) with attention for electrical load forecasting. The …
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On efficient learning algorithms for neural networks.
… much attention recently is the class of neural networks. But despite the excitement generated by neural networks, learning in these systems has proven to be a difficult task. In this thesis, we investigate different ways and means to overcome the difficulty of training feedforward neural …
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Monitoring and control for NGL recovery plant
… energy consumption under typical disturbances. Feedforward neural networks (FFNs) were used for the development of soft sensors used in data-driven control schemes. Given the multitude of data made available by the process simulator, this work aims to develop a demethanizer digital twin that can …
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Investigations into controllers for adaptive autonomous agents based on artificial neural networks.
… of adaptive behaviour based on artificial neural networks. There are two distinct levels of enquiry. At the primary level, the initial aim was to design and implement a unified architecture integrating sensorimotor learning and overall control. This was intended to overcome shortcomings of …
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Deep learning methods for shear log predictions in the Volve field Norwegian North Sea
… cycle-skipping. This thesis discusses artificial neural networks (ANNs) for shear log predictions using data from the Volve field, in the Norwegian North Sea. In this thesis I use deep neural networks or feedforward neural networks, and I propose convolutional neural networks and recurrent neural …
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Non-equilibrium physics: from spin glasses to machine and neural learning
… examine the structure-function relationships in feedforward neural networks, the prototypical example of neural learning. Using replica theory, information theory, and optimal transport, we study the computational consequences of imposing connectivity constraints on the network, such as …
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I. Kinetic Modeling of Surface Reactions II. Computational Design of Organic Semiconductors
… model (LLVM) as a model system, we trained feedforward neural networks (FFNNs) on kinetic Monte Carlo (KMC) results at select values of rate constants and initial conditions. The ML moment closure (MLMC) gave drastic improvements in the simulated dynamics and descriptions of the dynamical …
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Multi-layer Optimization Aspects of Deep Learning and MIMO-based Communication Systems
… Both transmitter and receiver are designed as feedforward neural networks (FNN) and constellation diagrams are optimized to minimize the symbol error rate (SER) based on the channel characteristics. We first evaluate the SER in the presence of a constant Rayleigh-fading channel as a performance …
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Advancing Kinematic Control from Rigid Robots to Dynamic Bio-Inspired Systems for Adaptive Behaviours
… methods, such as Motor Babbling with feedforward neural networks, and Spontaneous Muscle Activations with Hebbian-based learning, the systems' dynamics induced statistical regularities between sensory and motor systems. This facilitated the learning of pseudo-inverse Jacobian …
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