Masoud Mirmomeni

Learn More
A combination of singular spectrum analysis and locally linear neurofuzzy modeling technique is proposed to make accurate long-term prediction of natural phenomena. The principal components (PCs) obtained from spectral analysis have narrow band frequency spectra and definite linear or nonlinear trends and periodic patterns; hence they are predictable in(More)
This paper presents a methodology to select input variables for time series prediction. A main motivation is to find some proper input variables which describe the time series dynamics properly. It is shown that even when the choice of input variables is confined to the lagged values of the process to be predicted, a nonlinear analysis of the most(More)
1Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University College of Engineering, University of Tehran, Tehran, Iran 2Computer Engineering Department, Sharif University of Technology, Tehran, Iran 3Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran 4School of(More)
This paper presents a novel methodology for long term prediction of chaotic time series based on spectral analysis and neuro fuzzy modeling. A main motivation of using spectral analysis is to find some long term predictable components which describe the time series dynamics properly. In addition, this paper proposes a novel input variables selection(More)
Predicting future behavior of chaotic time series and systems is a challenging area in the literature of nonlinear systems. The prediction accuracy of chaotic time series is extremely dependent on the model and the learning algorithm. In addition, the generalization property of the proposed models trained by limited observations is of great importance. In(More)
The cyclic solar activity has significant effects on earth, climate, satellites and space missions. Several methods have been introduced for the prediction of sunspot number, which is a common measure of solar activity. In this study a co-evolutionary algorithm is presented for inferring the topology and parameters of a multilayered neural network with the(More)