MLPNNMulti-Layer Perceptron Neural Network
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Table 1: Results obtained from data analysis Sample Intervals Acceleration of Pumps Hz domain Result N/mm 0-2000, Pump #1 2000-4000 8000 and higher Sound Pump #2 0-2000, between 1000 Need to repair but 2000-10000 and 2000 it will work Pump #3 0-2000, 1000 and lower The pump will be 2000-4000 considered defective Table 2: The Results of the proposed method and MLPNN comparison.
2009) studied the use of MUTNN Multiple Temporal Units Neural Network and PENN Parallel Ensemble Neural Network for passenger flow prediction on the railway and considered these two methods to be more exact than the conventional MLPNN (Multi-Layer Perception Neural Network) one.