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Ragheb, Magdi Uluyol, Onder 2015-09-28T14:51:23Z 2015-09-28T14:51:23Z 10000-01-01 1998 1998 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. Made available in DSpace on 2015-09-28T14:51:23Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 9912404.pdf: 7278666 bytes, checksum: 4d1ac4b38832b98a0b4d107f16c7789b (MD5) Previous issue date: 1998 Embargo set by: Seth Robbins for item 87224 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 215 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1998. http://hdl.handle.net/2142/85943 (MiAaPQ)AAI9912404 eng Engineering, Electronics and Electrical Temporal Neural Networks and Transient Analysis of Complex Engineering Systems text Nuclear Engineering Nuclear Engineering University of Illinois at Urbana-Champaign Dissertation Ph.D.