Backpropagation based training algorithm for Takagi-Sugeno type MIMO neuro-fuzzy network to forecast electrical load time series

Describes a backpropagation based algorithm that can be used to train the Takagi-Sugeno (TS) type multi-input multi-output (MIMO) neuro-fuzzy network efficiently. 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), down to the desired error goal much faster than that the simple backpropagation algorithm (BPA). Finally, the above training algorithm is tested on neuro-fuzzy modeling and forecasting application of electrical load time series.