Masoud Mirmomeni

Publications

• 0.79

  Impact points

  Recursive spectral analysis of natural time series based on eigenvector matrix perturbation for online applications

  M. Mirmomeni, C. Lucas, B.N. Araabi, B. Moshiri, M.R. Bidar

  Signal Processing, IET. 10/2011;

  Singular spectrum analysis (SSA) is a well-studied approach in signal processing. SSA has originally been designed to extract information from short noisy chaotic time series and to enhance the signal-to-noise ratio. SSA is good for offline applications; however, many applications, such as modelling... [more] Singular spectrum analysis (SSA) is a well-studied approach in signal processing. SSA has originally been designed to extract information from short noisy chaotic time series and to enhance the signal-to-noise ratio. SSA is good for offline applications; however, many applications, such as modelling, analysis, and prediction of time-varying and non-stationary time series, demand for online analysis. This study introduces a recursive algorithm called recursive SSA as a modification to regular SSA for dynamic and online applications. The proposed method is based on eigenvector matrix perturbation approach. After recursively calculating the covariance matrix of the trajectory matrix, R-SSA updates the eigenvalues and eigenvectors for new samples by considering the effect of the new sample as perturbation in the covariance matrix and its singular value decomposition. The eigenvalues and eigenvectors adapt simultaneously to track their true values as would be calculated from the current covariance matrix. Analysis of two well-known chaotic time series: Mackey-Glass and Lorenz chaotic time series and two natural time series: Darwin sea-level pressure and Sunspot number as non-stationary processes are considered in this study to examine the performance of the proposed recursive method. The obtained results depict the power of the proposed method in online spectral analysis of non-linear time-varying systems.

• Introducing an incremental learning method for fuzzy descriptor models to identify nonlinear singular systems

  M. Mirmomeni, C. Lucas, M. Shafiee, B.N. Araabi

  Control and Automation, 2008 16th Mediterranean Conference on; 07/2008

  Singular systems have been the subject of interest over the last two decades due to their many practical applications. But it has to be said that system identification of such system is still a challenging area because of the difficulty of identification of such systems for their complex structures.... [more] Singular systems have been the subject of interest over the last two decades due to their many practical applications. But it has to be said that system identification of such system is still a challenging area because of the difficulty of identification of such systems for their complex structures. In addition, it seems that by developing a useful method for identification of singular system, one can use the useful property of such systems in describing the natural complex phenomena. This paper presents a novel methodology for identifying nonlinear singular systems from empirical data. Singular systems are idealized models for systems with slow and quick modes of change. However, their identification is a challenging problem even for the linear case. A new learning method, generalized locally linear model tree (GLoLiMoT) algorithm is introduced. The contribution of this paper is to provide a method for adjusting the parameters of fuzzy descriptor model, e.g. the splitting ratio and the standard deviation, the number of locally linear neurons or the number of linear singular systems for the consequent part in fuzzy descriptor model as well as the order of the singular system. By these modifications an accurate model of nonlinear singular system is obtained which is compared with several other methods in two case studies. Results depict the power of the proposed approach in describing nonlinear complex phenomena.

• Forecasting Solar Activity Using Co-evolution of Models and Tests

  M. Mirmomeni, C. Lucas, B.N. Araabi, B. Moshiri

  Intelligent Systems Design and Applications, 2007. ISDA 2007. Seventh International Conference on; 11/2007

  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 topolog... [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 minimum of experimentation to the sunspot number time series which will be used as a predictor in predicting such phenomena. The algorithm synthesizes an explicit model directly from the observed data produced by intelligently generated tests. The algorithm is composed of two co-evolving populations; one population evolves candidate neural networks. The second population evolves informative tests that either extract new information from the hidden system or elicit desirable behavior from it. The fitness of candidate neural networks is their ability to explain behavior of the target chaotic system observed in response to all tests carried out so far; the fitness of candidate tests is their ability to make the models disagree in their predictions. The generality of this modeling-evaluation algorithm is demonstrated by applying the chosen model of this algorithm to predict sunspot number and the results depict the power of this training method which yields proper model to predict such chaotic time series.

• Input Variables Selection Using Mutual Information for Neuro Fuzzy Modeling with the Application to Time Series Forecasting

  M.M.R. Yousefi, M. Mirmomeni, C. Lucas

  Neural Networks, 2007. IJCNN 2007. International Joint Conference on; 09/2007

  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 ... [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 significant factors is crucial for improving the prediction quality. The proposed method is used to select the appropriate input variables for neuro fuzzy models utilized for time series prediction benchmark in NN3 competition as well as a second benchmark to show the generality of the claims. Results depict the effectiveness of the proposed method in proper input selection for neuro fuzzy models for prediction task.

• Long Term Prediction of Chaotic Time Series with the Aid of Neuro Fuzzy Models, Spectral Analysis and Correlation Analysis

  M. Mirmomeni, C. Lucas, B. Moshiri

  Neural Networks, 2007. IJCNN 2007. International Joint Conference on; 09/2007

  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, th... [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 criterion which is based on correlation analysis. The objective of this algorithm is to maximize relevance between inputs and output and minimizes the redundancy of selected inputs. After selecting input variables, a locally linear neuro fuzzy model is optimized for each of the principal components obtained from singular spectrum analysis, and the multi step predicted values are recombined to make the natural chaotic phenomenon. Two case studies are considered in this paper. The method has been applied to the long-term prediction of disturbance storm time (DST) as a solar activity indexes and one time series from neural forecasting competitions NN3. Results depict the power of the proposed method in long-term prediction of chaotic time series.

• Modulation identification using combined classifiers and co-evolution of classifiers and tests

  M. Mirmomeni, B. Moshiri, C. Lucas

  Information Fusion, 2007 10th International Conference on; 08/2007

  Nowadays, there are considerable attentions to combined classifier. Recently, the focus has been shifting from practical heuristic solutions of combination methods to give a methodological way of design. In this study a co-evolutionary algorithm is presented for this purpose. The algorithm synthesiz... [more] Nowadays, there are considerable attentions to combined classifier. Recently, the focus has been shifting from practical heuristic solutions of combination methods to give a methodological way of design. In this study a co-evolutionary algorithm is presented for this purpose. The algorithm synthesizes an explicit classifier directly from bserved data produced by intelligently generated tests. The algorithm is composed of two co-evolving populations; one population evolves candidate classifiers. The second population evolves informative tests that either extract new information from the pattern or elicit desirable behavior from it The fitness of candidate classifiers is their ability to classify in response to all tests carried out so far; the fitness of candidate tests is their ability to make the classifiers disagree in their classifications. The generality of this modeling-evaluation algorithm is demonstrated by applying the chosen classifier of this algorithm to identify modulation methods and results depict the power of this algorithm.