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Title:Temporal Neural Networks and Transient Analysis of Complex Engineering Systems
Author(s):Uluyol, Onder
Doctoral Committee Chair(s):Ragheb, Magdi
Department / Program:Nuclear Engineering
Discipline:Nuclear Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Engineering, Electronics and Electrical
Abstract:The developed LOGF neuron model can also be viewed as a Transformed 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 LOGF neurons can thus be used to model dynamic systems. A learning algorithm based upon the Backpropagation-Through-Time (BTT) approach is derived. It is applicable for training a general L-layer LOGF neural network. The spatial and temporal weights and parameters of the network are iteratively optimized for a given problem using the derived learning algorithm.
Issue Date:1998
Description:215 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1998.
Other Identifier(s):(MiAaPQ)AAI9912404
Date Available in IDEALS:2015-09-28
Date Deposited:1998

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