Meade, Andrew J., Jr.2009-06-032009-06-031994Penaranda, Guillermo. "A study of neural networks in thermal systems." (1994) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/13876">https://hdl.handle.net/1911/13876</a>.https://hdl.handle.net/1911/13876Neural networks have been found to be useful as a technique for the modeling of non-linear functions or processes that involve several variables. The primary goal of this thesis is to explore the feasibility of applying feedforward backpropagation neural networks in the optimization of multistage thermal systems. Basically, the idea consists of using neural networks as a function approximation technique for each stage of a multistage process. After the successful approximation, existing optimization methods are used to obtain the parameters that optimize the system. In addition, it is shown how feedforward backpropagation neural networks can be used in solving calculus of variation problems, by separating the process into discrete stages, thus forming a multistage process problem. Finally, parallel work was done in developing a faster deterministic training algorithm, as an alternative to the time consuming backpropagation training algorithm.138 p.application/pdfengCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.Mechanical engineeringArtificial intelligenceThesisTHESIS M.E. 1994 PENARANDA