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http://hdl.handle.net/2080/669
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| DC Field | Value | Language |
|---|
| contributor.author | Vamsi Krishna, T | - |
| contributor.author | Satapathy, J K (Guide) | - |
| date.accessioned | 2008-04-24T07:00:22Z | - |
| date.available | 2008-04-24T07:00:22Z | - |
| date.issued | 2006 | - |
| identifier.citation | Development Of Novel Neuro-Fuzzy Techniques For Adaptive Systems, Thesis submitted in partial fulfillment of the requirements for the degree of Master of Technology by Research, to National Institute of Technology, Rourkela | en |
| identifier.uri | http://hdl.handle.net/2080/669 | - |
| description | Copyright for the article belongs to National Institute of Technology, Rourkela | en |
| description.abstract | Novel approaches for designing adaptive schemes based on neuro-fuzzy platform
have been developed. Two kinds of adaptive schemes namely, adaptive equalization and
system identification are implemented using the developed proposed techniques. The Radial
basis function (RBF) equalizer is chosen as a case study for adaptive equalization of the
digital communication channels. An efficient method for reducing the centers of a RBF
equalizer based on eigenvalue analysis is presented. The efficiency of the method is further
verified for RBF equalizers with decision feedback for tackling channels with overlapping
channel states. A comparative study between the proposed center reduction technique and
other center reduction techniques for the RBF equalizer is discussed. In another breakthrough
a parallel interpretation of the ANFIS (adaptive network based fuzzy inference systems)
architecture is proposed. This approach helps to investigate the role of the fuzzy inference
part and the sub-filter part of the ANFIS separately. The parallel interpretation of the ANFIS
redefines the opinion reserved for the fuzzy inference system, thereby allowing it to be
considered as a fuzzy weighted sub-filter network, with the weighting functions and the subfilter
units arranged parallely. This approach motivated in developing many novel schemes
for designing adaptive systems with application to system identification problems. Finally, the
limitations of the ANFIS architecture are discussed. These limitations are exploited to
develop neuro-fuzzy models similar to the ANFIS with the objective of reducing the number
of parameters in comparison to the ANFIS. The developed neuro-fuzzy models are compared
to the ANFIS in terms of the time required for learning and number of parameters to be
adapted. | en |
| format.extent | 2670950 bytes | - |
| format.mimetype | application/pdf | - |
| language.iso | en | - |
| publisher | National Institute of Technology, Rourkela | en |
| title | Development Of Novel Neuro-Fuzzy Techniques For Adaptive Systems | en |
| type | Thesis | en |
| Appears in Collections: | Thesis (MTech by Research)
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| thesis-final,,,,,.pdf | | 2608Kb | Adobe PDF | View/Open |
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