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Title:Emulating human process control functions with neural networks
Author(s):Hall, James William
Doctoral Committee Chair(s):Lu, Stephen C-Y
Department / Program:Engineering, Agricultural
Engineering, Mechanical
Artificial Intelligence
Discipline:Engineering, Agricultural
Engineering, Mechanical
Artificial Intelligence
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Engineering, Agricultural
Engineering, Mechanical
Artificial Intelligence
Abstract:This investigation demonstrates that neural networks can perform some of the tasks in controlling complex systems that have been traditionally reserved for humans. Neural networks can be used to fuse different types of knowledge from many sources into a general process model. This technique allows process models to be formed for systems that are too complex to be modeled with conventional tools. By adding relatively few local measurements, a general process model can be calibrated into a numerically accurate local model of the process. This local model can then used for steady-state process optimization. The architectures and training techniques needed to produce neural networks capable of performing these functions are discussed. This technology was applied to the control of a complex system--a grain harvesting combine. Field tests of the harvesting process under neural network control demonstrated that the controller was robust and capable of exceeding the performance of expert human operators.
Issue Date:1992
Rights Information:Copyright 1992 Hall, James William
Date Available in IDEALS:2011-05-07
Identifier in Online Catalog:AAI9401931
OCLC Identifier:(UMI)AAI9401931

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