Nodes Selection Criteria for Fuzzy Cognitive Maps Designed to Model Time Series

2015 ◽  
pp. 859-870 ◽  
Author(s):  
Wladyslaw Homenda ◽  
Agnieszka Jastrzebska ◽  
Witold Pedrycz
Keyword(s):  
Time Series ◽  
Selection Criteria ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps ◽  
Model Time Series

Improved Fuzzy Cognitive Maps for Gene Regulatory Networks Inference Based on Time Series Data

2021 ◽  
Author(s):  
Marzieh Emadi ◽  
Farsad Zamani Boroujeni ◽  
jamshid Pirgazi
Keyword(s):  
Time Series ◽  
Compressed Sensing ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Time Series Data ◽  
Cognitive Maps ◽  
Boolean Networks ◽  
Fuzzy Cognitive Maps ◽  
Series Data ◽  
Gene Regulatory

Abstract Recently with the advancement of high-throughput sequencing, gene regulatory network inference has turned into an interesting subject in bioinformatics and system biology. But there are many challenges in the field such as noisy data, uncertainty, time-series data with numerous gene numbers and low data, time complexity and so on. In recent years, many research works have been conducted to tackle these challenges, resulting in different methods in gene regulatory networks inference. A number of models have been used in modeling of the gene regulatory networks including Boolean networks, Bayesian networks, Markov model, relational networks, state space model, differential equations model, artificial neural networks and so on. In this paper, the fuzzy cognitive maps are used to model gene regulatory networks because of their dynamic nature and learning capabilities for handling non-linearity and inherent uncertainty. Fuzzy cognitive maps belong to the family of recurrent networks and are well-suited for gene regulatory networks. In this research study, the Kalman filtered compressed sensing is used to infer the fuzzy cognitive map for the gene regulatory networks. This approach, using the advantages of compressed sensing and Kalman filters, allows robustness to noise and learning of sparse gene regulatory networks from data with high gene number and low samples. In the proposed method, stream data and previous knowledge can be used in the inference process. Furthermore, compressed sensing finds likely edges and Kalman filter estimates their weights. The proposed approach uses a novel method to decrease the noise of data. The proposed method is compared to CSFCM, LASSOFCM, KFRegular, ABC, RCGA, ICLA, and CMI2NI. The results show that the proposed approach is superior to the other approaches in fuzzy cognitive maps learning. This behavior is related to the stability against noise and offers a proper balance between data error and network structure.

Numerical and Linguistic Prediction of Time Series With the Use of Fuzzy Cognitive Maps

2008 ◽  
Vol 16 (1) ◽  
pp. 61-72 ◽  
Author(s):  
W. Stach ◽  
L.A. Kurgan ◽  
W. Pedrycz
Keyword(s):  
Time Series ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps

scholarly journals Modeling time series by aggregating multiple fuzzy cognitive maps

2021 ◽  
Vol 7 ◽  
pp. e726
Author(s):  
Tianming Yu ◽  
Qunfeng Gan ◽  
Guoliang Feng
Keyword(s):  
Time Series ◽  
Granular Computing ◽  
Bootstrap Method ◽  
Experimental Studies ◽  
Real Life ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps ◽  
Single Model ◽  
Modelling Methodology ◽  
The Bootstrap Method

Background The real time series is affected by various combinations of influences, consequently, it has a variety of variation modality. It is hard to reflect the variation characteristic of the time series accurately when simulating time series only by a single model. Most of the existing methods focused on numerical prediction of time series. Also, the forecast uncertainty of time series is resolved by the interval prediction. However, few researches focus on making the model interpretable and easily comprehended by humans. Methods To overcome this limitation, a new prediction modelling methodology based on fuzzy cognitive maps is proposed. The bootstrap method is adopted to select multiple sub-sequences at first. As a result, the variation modality are contained in these sub-sequences. Then, the fuzzy cognitive maps are constructed in terms of these sub-sequences, respectively. Furthermore, these fuzzy cognitive maps models are merged by means of granular computing. The established model not only performs well in numerical and interval predictions but also has better interpretability. Results Experimental studies involving both synthetic and real-life datasets demonstrate the usefulness and satisfactory efficiency of the proposed approach.

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