Comparison of BPA and LMA methods for Takagi - Sugeno type MIMO Neuro-Fuzzy Network to forecast Electrical Load Time Series
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
Author: Felix Pasila
Journal Code: jptlisetrogg070002
