Neural network methods in analysing and modelling time varying processes

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en Koskela, Timo 2012-02-10T09:08:41Z 2012-02-10T09:08:41Z 2003-12-12
dc.identifier.isbn 951-22-6818-3
dc.identifier.issn 1455-0474
dc.description.abstract Statistical data analysis is applied in many fields in order to gain understanding to the complex behaviour of the system or process under interest. For this goal, observations are collected from the process, and models are built in an effort to capture the essential structure from the observed data. In many applications, e.g. process control and pattern recognition, the modeled process is time-dependent, and thus modeling the temporal context is essential. In this thesis, neural network methods in statistical data analysis and especially in temporal sequence processing (TSP) are considered. Neural networks are a class of statistical models, applicable in many tasks from data exploration to regression and classification. Neural networks suitable for TSP can model time dependent phenomena, typically by utilizing delay lines or recurrent connections within the network. Recurrent Self-Organizing Map (RSOM) is an unsupervised neural network model capable of processing pattern sequences. The application of the RSOM with local models in temporal sequence prediction is presented. The RSOM is applied to divide the input pattern sequences into clusters, and local models are estimated corresponding to these clusters. In case studies, time series prediction problems are considered. Prediction results gained from the RSOM model show better performance than the model with conventional Self-Organizing Map. The RSOM can capture temporal context from the pattern sequence, which is useful in the presented prediction tasks. As another application, a neural network model for optimizing a Web cache is proposed. Web caches store recently requested Web objects, and are typically shared by many clients. A caching policy decides which objects are removed when the storage space is full. In the proposed approach a model predicts the value of each cache object by utilizing features extracted from the object. Only syntactic features are used, which enables efficient estimation and application of the model. The caching policy can be optimized based on the predicted values and a cost model designed according to the objectives of the caching. In a case study, different stages and decisions made during the data analysis and model building are presented. The results gained suggest that the proposed approach is useful in the application. en
dc.format.extent 113
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Helsinki University of Technology en
dc.publisher Teknillinen korkeakoulu fi
dc.relation.ispartofseries Helsinki University of Technology Laboratory of Computational Engineering publications. Report B en
dc.relation.ispartofseries 35 en
dc.subject.other Computer science en
dc.title Neural network methods in analysing and modelling time varying processes en
dc.type G4 Monografiaväitöskirja fi
dc.description.version reviewed en
dc.contributor.department Department of Electrical and Communications Engineering en
dc.contributor.department Sähkö- ja tietoliikennetekniikan osasto fi
dc.subject.keyword time series prediction en
dc.subject.keyword temporal sequence processing en
dc.subject.keyword neural networks en
dc.subject.keyword Web cache optimization en
dc.subject.keyword Self-Organizing Map en
dc.identifier.urn urn:nbn:fi:tkk-001101
dc.type.dcmitype text en
dc.type.ontasot Väitöskirja (monografia) fi
dc.type.ontasot Doctoral dissertation (monograph) en
dc.contributor.lab Laboratory of Computational Engineering en
dc.contributor.lab Laskennallisen tekniikan laboratorio fi

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