Optimization of the hidden layer of a multilayer perceptron with backpropagation (bp) network using hybrid k-means-greedy algorithm (kga) for time series prediction - Tan, James Yiaw Beng (Universiti Malaysia Sarawak (UNIMAS), 2012)
(Research into the field of artificial neural networks (ANN) is fast gaining interest in recent years, due to the fact that it is fast becoming a popular tool of choice in prediction of time series trends. This recent surge in its popularity can be attributed to the fact that ANN, especially a multilayer perceptron with backpropagation (BP) network that has the optimal number of neurons in its hidden layer would be able to predict with better accuracy unknown values of a time series that it is trained with, compared to other methods implemented to predict the same time series The drawback of using BP networks in time series prediction is that it is difficult and time-consuming to find the optimal number of neurons in its hidden layer to minimize the prediction error. We propose a model known as K-means-Greedy Algorithm (KGA) model in this research to overcome this serious drawback of the BP network. The proposed KGA model combines greedy algorithm withk-means++ clustering in this research to assist users in automating the finding of the optimal number of new-ons inside the hidden layer of the BP network. The evaluation results the proposed KGA model using several time series, namely the sunspot data, the Mackey-Glass time series, and electrical load forecasting using data from several econometric factors, as well as historical electricity demand data, show that the proposed KGA model is eflective in finding the optimal number ofneurons for the hidden layer of a BP network that is used to perform time series prediction.