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Wah, Benjamin W. Shang, Yi 2015-09-25T20:20:56Z 2015-09-25T20:20:56Z 10000-01-01 1997 1997 We show experimental results in applying Novel to solve nonlinear optimisation problems, including (a) the learning of feedforward neural networks, (b) the design of quadrature-mirror-filter digital filter banks, (c) the satisfiability problem, (d) the maximum satisfiability problem, and (e) the design of multiplierless quadrature-mirror-filter digital filter banks. Our method achieves better solutions than existing methods, or achieves solutions of the same quality but at a lower cost. Made available in DSpace on 2015-09-25T20:20:56Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 9812765.pdf: 12599327 bytes, checksum: d1e1e2c098a5d322df244206963caf34 (MD5) Previous issue date: 1997 Embargo set by: Seth Robbins for item 83187 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 288 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1997. http://hdl.handle.net/2142/81906 (MiAaPQ)AAI9812765 eng Computer Science Global Search Methods for Solving Nonlinear Optimization Problems text Computer Science Computer Science University of Illinois at Urbana-Champaign Dissertation Ph.D.