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Contribution Details
Type | Master's Thesis |
Scope | Discipline-based scholarship |
Title | A hybrid least-Squares support vector machines based local neuro-fuzzy model using a feed-forward artificial neural network for class membership weight generation |
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Institution | University of Zurich |
Faculty | Faculty of Business, Economics and Informatics |
Number of Pages | 47 |
Date | 2018 |
Abstract Text | Heterogeneous data spaces with multiple underlying regimes may present difficulties for a global supervised inference engine. A way of increasing prediction accuracy is to divide the input space among multiple models and aggregate predictions. This paper proposes a novel hybrid neuro-fuzzy model combining a feed-forward artificial neural network (ANN) for data space partition with weighted leastsquares support vector machines (LSSVM) as local inference engines. Model parameter are estimated via error backpropagation by using the derivatives of the LSSVM core equations with regard to the ANN final layer outputs. Empirical tests on benchmark time series show increased forecasting performance for some data sets compared to less flexible architectures but this comes at a significant increase in computational costs. |
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