Petrus Santoso, S.T., M.Sc. Electrical Engineering Department, Petra Christian University, Indonesia
Editors
Ir. Murtiyanto Santoso, M.Sc. Telecommunication, Energy Management, & Digital Electronics; Electrical Engineering Department, Petra Christian University, Indonesia
Ir. Resmana Lim, M.Eng. Image Processing, Pattern Recognition, & Computer Network; Electrical Engineering Department, Petra Christian University, Indonesia
Ir. Emmy Hosea, M.Eng.Sc. Power System, Power Distribution, & Power Electronics; Electrical Engineering Department, Petra Christian University, Indonesia
Petrus Santoso, S.T., M.Sc. Telematics Application & Computer Network, Electrical Engineering Department, Petra Christian University, Indonesia
Hany Ferdinando, S.T., M.Sc. Control System & Signal Processing, Electrical Engineering Department, Petra Christian University, Indonesia
Felix Pasila, S.T., M.Sc. Control System, Intelligent Control, Modelling & Identification; Electrical Engineering Department, Petra Christian University, Indonesia
Reviewers
Dr. Jan F Broenink Embedded System, Twente Embedded System Initiative, Netherlands
Abraham Theodore Zuur, M.Sc. Biomedical, Center for Sensory Motor Interactions, Aalborg University, Denmark
Dr. Mark A H Verwoerd Intelligent System-Control Engineering Group, University of Twente, Netherlands
Manukid Parnichkun, Ph.D. Mechatronics, Robotics, Control System, Asian Institute of Technology, Thailand
Dr. Eng. Benyamin Kusumoputro Computer Vision, Information Technology FasiLKOM-UI, Indonesia
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.
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.