Adaptive Network based Fuzzy Inference System (ANFIS) as a Tool for System Identification with Special Emphasis on Training Data Minimization

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dc.contributor.author Buragohain, Mrinal
dc.date.accessioned 2015-09-16T10:20:04Z
dc.date.available 2015-09-16T10:20:04Z
dc.date.issued 2009
dc.identifier.other ROLL NO. 02610204
dc.identifier.uri http://gyan.iitg.ernet.in/handle/123456789/256
dc.description Supervisor: Chitralekha Mahanta en_US
dc.description.abstract Nearly two decades back nonlinear system identification consisted of several ad-hoc approaches which were restricted to a very limited class of systems. However, with the advent of the various soft computing methodologies like neural networks, the fuzzy logic and the genetic algorithm combined with modern structure optimization techniques, a wider class of systems can be handled at present. Complex systems may be of diverse characteristics and nature. These systems may be linear or nonlinear, continuous or discrete, time varying or time invariant, static or dynamic, short term or long term, central or distributed, predictable or unpredictable, ill or well defined. System outputs may be measurable or unmeasurable. Models of real systems are of fundamental importance in virtually all disciplines and hence there is a strong demand for advanced modeling, identification and controlling schemes. This is because models help in system analysis which in turn help to get a better understanding of the system for predicting or simulating a systemDs behavior. Also, system models facilitate application and validation of advanced techniques for controller design. Development of new processes and analysis of the existing ones along with their optimization, supervision, fault detection, and component diagnosis are all based on the models of the systems. As most of the real world systems are nonlinear in nature, an endeavor is made for modeling a nonlinear system in the present work. A linear system is considered to be a special case of the nonlinear system. The challenges involved in modeling, identification and control of a nonlinear system are too many and attempt has been made to tackle them by applying various soft computing methodologies. In most of the conventional soft computing methods the system modelling results are dependent on the number of training data used. It has been found that the modeling results improve as the number of training data increases. But in many complex systems the number of available training data are less and the generation of new data is also not cost effective. In such a scenario the system has to be modelled with the available data. The proposed modeling scheme hasD The results obtained by applying the proposed technique are comparable and in some cases superior to those obtained by using the conventional neuro-fuzzy model. D Comparable or superior results are obtained with this proposed model even though the number of data pairs used for system modeling here are less as compared to that used in the conventional methods. D It resulted in reduction of the number of computations involved. As the experiments were performed by using reduced number of specifically chosen data, the number of computations required to be performed also came down. been devised keeping such a possibility in mind. The results obtained by applying this proposed model are compared with the results obtained by using various statistical and genetic algorithm based fuzzy models and finally the relative merits and demerits involved with the respective models are discussed. The work embodied in the present thesis is concerned with optimal design of the conventionally existing soft computing based system models. The statistics based Full factorial design (FFD) and the V-fold cross validation technique are applied to... en_US
dc.language.iso en en_US
dc.relation.ispartofseries TH-0801;
dc.subject ELECTRONICS AND ELECTRICAL ENGINEERING en_US
dc.title Adaptive Network based Fuzzy Inference System (ANFIS) as a Tool for System Identification with Special Emphasis on Training Data Minimization en_US
dc.type Thesis en_US


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