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 11 of 11 for “"neural network applications"”.
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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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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 applications in the control of power electronic converters
Attempts have recently been made to apply Neural Networks to control systems where they are to deal with any modeling uncertainties that may exist. This thesis proposes the Neural Network controller as a viable alternative to the conventional and widely used PI regulator for the regulation of Power …
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Novel Neural Network Applications to Mode Choice in Transportation: Estimating Value of Travel Time and Modelling Psycho-Attitudinal Factors
… Another contribution of this thesis is a neural network (NN) for the estimation of choice models with latent variables as an alternative to DCMs. This issue arose from wanting to include in ML models not only level of service variables of the alternatives, and socio-economic attributes of …
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Neural Networks in Bioprocessing and Chemical Engineering
… fundamental principles and practical aspects of neural networks, focusing on their applications in bioprocessing and chemical engineering. This study introduces neural networks and provides an overview of their structures, strengths, and limitations, together with a survey of their potential and …
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Characterization of polygrama green photopolymer for Compact Optoelectronic Integrated Neural (COIN) coprocessor applications
… MIT to design Compact Optoelectronic Integrated Neural (COIN) co processor [13]. The choice of photopolymers is critical in determining the performance of COIN processors as we look at ways to increase the diffraction efficiency. The focus of this research was to optically characterize Polygrama …
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Sustainable Development: A Data Analysis Investigation for a New Conceptual Model, Case Study Comparison and Validation Techniques
… development. The second contribution uses neural network applications to validate the model. The third contribution focuses on adapting a new conceptual framework to describe sustainability for overall use by testing the new conceptual model on two cases studies using the same methodology …
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Characterization of containers in emerging applications: Microservices, FAAS and GPUS
… simple management and isolation of containerized applications. Docker is currently the most prominent container framework. This thesis utilizes Docker containers to create data center use cases with databases, web servers, graph analytics, Functions-as-a-Service, and GPU-accelerated stencil, …
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Gamma spectroscopy by artificial neural network coupled with MCNP
… important when analyzing bio-samples. Artificial neural network is an attractive technique for complex systems. Although there are neural network applications on spectral analysis, training by simulated data to analyze experimental data has not been made. This study offers an improvement on …
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Modeling, Simulation, and Optimization of large-Scale Commercial Desalination Plants
… the fundamental and practical aspects of neural networks and provides an overview of their structures, topology, strengths, and limitations. This study includes the neural network applications to prediction problems of large-scale commercial MSF and RO desalination plants in conjunction …
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Long-memory stochastic volatility model calibration using deep neural nets
… vanilla option. However, with the advent of neural networks, stochastic volatility models are becoming increasing tractable. The use of neural networks to learn the expectation function of the underlying stochastic volatility processes for calibration makes application of these more involved …