Neuron-Network Level Problem Decomposition Method for Cooperative Coevolution of Recurrent Networks for Time Series Prediction

Abstract

The breaking down of a particular problem through problem decomposition has enabled complex problems to be solved efficiently. The two major problem decomposition methods used in cooperative coevolution are synapse and neuron level. The combination of both the problem decomposition as a hybrid problem decomposition has been seen applied in time series prediction. The different problem decomposition methods applied at particular area of a network can share its strengths to solve the problem better, which forms the major motivation. In this paper, we are proposing a problem decomposition method that combines neuron and network level problem decompositions for Elman recurrent neural networks and applied to time series prediction. The results reveal that the proposed method has got better results in few datasets when compared to two popular standalone methods. The results are better in selected cases for proposed method when compared to several other approaches from the literature.

Statistical Methods for Data mining Mathematics students' online presence

“…The smaller centers are a part of the larger campuses normally spread across remote locations or based on some regional countries' outer islands. The main campus, Laucala Campus, is located in Suva (Fiji Islands) and has the highest headcount as its administrative, academic, and commercial operations [23,25,29]. The main campus also coordinates and facilitates most courses and programmes in online and blended modes in the region through Moodle.…”

“…Information Communication Technologies (ICTs) such as smartphones [18], tablets [19,20] and emerging learning systems such as eLearning, mLearning [21][22][23][24][25] can help in reducing the difficulties that students face while studying mathematics [26]. Intelligent systems such as an online mathematical diagnostics tool can help identify students' mathematical literacy level [27].…”

Data Mining Students' performance in a Higher Learning Environment

“…The literature shows that data mining techniques such as the artificial neural network (ANN) and a combination of clustering and decision tree classification techniques for predicting and classifying students' academic performance seem to be mostly widely adopted by the many researchers [3], [10], [13], [14] and [25]. The rapid development and advancement of artificial intelligence and deep learning algorithms has provided another approach for intelligent classification and result prediction [26,27,24,28,29].…”