Most engineering systems are highly nonlinear in nature and thus one could not
develop efficient mathematical models for these systems. Artificial neural
networks, which are used in estimation, filtering, identification and control in
technical literature, are considered as universal modeling and functional
approximation tools. Unfortunately, developing a well trained monolithic type
neural network (with many free parameters/weights) is known to be a daunting
task since the process of loading a specific pattern (functional relationship) onto a
generic neural network is proven to be a NP-complete problem. It implies that if
training is conducted on a deterministic computer, the time required for training
process grows exponentially with increasing size of the free parameter space (and
the training data in correlation). As an alternative modeling technique for
nonlinear dynamic systems / this thesis proposed a general methodology for
structured neural network topologies and their corresponding applications are
realized. The main idea behind this (rather classic) divide-and-conquer approach is to employ a priori information on the process to divide the problem into its
fundamental components. Hence, a number of smaller neural networks could be
designed to tackle with these elementary mapping problems. Then, all these
networks are combined to yield a tailored structured neural network for the
purpose of modeling the dynamic system under study accurately. Finally,
implementations of the devised networks are taken into consideration and the
efficiency of the proposed methodology is tested on four different types of
mechanical systems.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12614735/index.pdf |
| Date | 01 September 2012 |
| Creators | Kilic, Ergin |
| Contributors | Dolen, Melik |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | Ph.D. Thesis |
| Format | text/pdf |
| Rights | Access forbidden for 1 year |
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