Improve an Efficiency of Feedforward Multilayer Perceptrons by Serial Training
نویسنده
چکیده
The Feedforward Multilayer Perceptrons network is a widely used model in Artificial Neural Network using the backpropagation algorithm for real world data. There are two common ways to construct Feedforward Multilayer Perceptrons network, that is, either taking a large network and then pruning away the irrelevant nodes or starting from a small network and then adding new relevant nodes. An Artificial Neural Network model is often avoided due to the large size of network and the training that would be too slow to be tolerable. For improving the efficiency and to provide accurate results on the basis of same behaviour data, a serial algorithm for the training of data is proposed that uses two data mining techniques, that is, cluster analysis, which partitions large dataset into similar n blocks and then these n blocks are inputted to Feedforward Multilayer Perceptrons network to perform serial training for improving the efficiency.
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