The paper describes a Backpropagation based algorithm that can be used to train the Takagi-Sugeno (TS) type multi-input mnlti-ontput (MIMO) neuro-fuzzy network elfciently. m e training algorithm is elfcient in the sense that it can bring the performance index of the nehvorb 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.
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