Comparison of BPA and LMA Methods for Takagi - Sugeno type MIMO Neuro-Fuzzy Network to Forecast Electrical Load Time Series

Felix Pasila




Abstract


This paper describes an accelerated Backpropagation algorithm (BPA) that can be used to train the Takagi-Sugeno (TS) type multi-input multi-output (MIMO) neuro-fuzzy network efficiently. Also other method such as accelerated Levenberg-Marquardt algorithm (LMA) will be compared to BPA. The training algorithm is efficient in the sense that it can bring the performance index of the network, such as the sum squared error (SSE), Mean Squared Error (MSE), and also Root Mean Squared Error (RMSE), down to the desired error goal much faster than that the simple BPA or LMA. Finally, the above training algorithm is tested on neuro-fuzzy modeling and forecasting application of Electrical load time series.


Keywords


TS type MISO neuro-fuzzy network, accelerated Levenberg-Marquardt algorithm, accelerated Backpropagation algorithm, time-series forecasting

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The Journal is published by The Institute of Research & Community Outreach - Petra Christian University. It available online supported by Directorate General of Higher Education - Ministry of National Education - Republic of Indonesia.

©All right reserved 2016.Jurnal Teknik Elektro, ISSN: 1411-870X

 

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