This paper proposes a fuzzy cognitive map scheme for real-time online data prediction. The fuzzy cognitive maps (FCMs) are constructed on the basis of abstracting a numerical time series into a limited number of nodes (concepts), and used as a modeling tool for predicting time series. By representing time series in terms of information granules constructed in the space of amplitude and change of amplitude of the time series, a fuzzy cognitive map is dynamically constructed by using the set of information granule, where the particle swarm optimization (PSO) is utilized to study the parameters. In order to find better weights in the global search process, each parameter of the particle swarm algorithm (PSO) is not set to a fixed value but adaptively changes. In this paper, a dynamic fuzzy C-means clustering algorithm is used to online adjust the cluster center and weight according to the impact of the incoming data at the current moment such that the model can capture real-time changes in the data information. The proposed approach is illustrated in detail by a series of experiments using a collection of publicly available data.
JiaW., ZhaoD., ZhengY., et al., A novel optimized GA-Elman neural network algorithm, Neural Computing & Applications (2017), 1–11.
4.
FroelichW., SalmeronJ.L., Evolutionary learning of fuzzy grey cognitive maps for the forecasting of multivariate: Interval-valued time series[J], International Journal of Approximate Reasoning55(6) (2014), 1319–1335.
5.
BargielaA., PedryczW., Granular computing, Handbook on Computational Intelligence: Volume 1: Fuzzy Logic, Systems, Artificial Neural Networks, and Learning Systems, (2016), pp. 43–66.
FanX., LeiY., WangY., Adaptive partition intuitionistic fuzzy time series forecasting model[J], Journal of Systems Engineering and Electronics28(3) (2017), 585–596.
8.
LuW., YangJ., LiuX., The hybrids algorithm based on fuzzy cognitive map for fuzzy time series prediction[J], Journal of Information & Computational Science11(2) (2014), 357–366.
9.
GuoJ., LiuZ., HuangW., et al., Short-term traffic flow prediction using fuzzy information granulation approach under different time intervals[J], IET Intelligent Transport Systems12(2) (2017), 143–150.
10.
YinS., JiangY., TianY., et al., A data-driven fuzzy information granulation approach for freight volume forecasting[J], IEEE Transactions on Industrial Electronics64(2) (2017), 1447–1456.
11.
TsadirasA., ZitopoulosG., Decision Making on Container Based Logistics Using Fuzzy Cognitive Maps[M]//, Engineering Applications of Neural Networks, Springer, Cham, 2015, pp. 347–357.
12.
CaseD.M., BlackburnT., StyliosC., Modelling Construction Management Problems with Fuzzy Cognitive Maps[M]// pp, Fuzzy Hybrid Computing in Construction Engineering and Management: Theory and Applications. Emerald Publishing Limited (2018), 413–449.
13.
VasslidesJ.M., JensenO.P., Fuzzy cognitive mapping in support of integrated ecosystem assessments: Developing a shared conceptual model among stakeholders[J], Journal of Environmental Management166 (2016), 348–356.
14.
StachW., KurganL.A., PedryczW., Numerical and linguistic prediction of time series with the use of fuzzy cognitive maps[J], IEEE Transactions on Fuzzy Systems16(1) (2008), 61–72.
15.
PedryczW., JastrzebskaA., HomendaW., Design of fuzzy cognitive maps for modeling time series[J], IEEE Transactions on Fuzzy Systems24(1) (2016), 120–130.
16.
PedryczW., HomendaW., From fuzzy cognitive maps to granular cognitive maps[J], IEEE Transactions on Fuzzy Systems22(4) (2014), 859–869.
17.
AlayaI., SolnonC., GhediraK., Ant Colony Optimization for Multi-Objective Optimization Problems[C]//, IEEE International Conference on Tools with Artificial Intelligence, 2017.
18.
BaiJ., LiuH., Multi-objective artificial bee algorithm based on decomposition by PBI method, Applied Intelligence45(4) (2016), 976–991.
19.
ZhangP., LiuH., DingY., Dynamic bee colony algorithm based on multi-species co-evolution, Applied intelligence40(3) (2014), 427–440.
20.
DuK.L., SwamyM.N.S., Particle swarm optimization[M]//, Search and Optimization by Metaheuristics, Birkhäuser, Cham, 2016, pp. 153–173.
21.
LopezJ., LanzariniL., BarivieraA.F., Variable population MOPSO applied to medical visits[J], Fuzzy Economic Review17(1) (2012), 3.
22.
SantanaP.J., LanzariniL., BarivieraA.F., Extraction of Knowledge with Population-Based Metaheuristics Fuzzy Rules Applied to Credit Risk[C]//, International Conference on Sensing and Imaging, Springer, Cham, 2018, pp. 153–163.
23.
AcamporaG., PedryczW., VitielloA., A competent memetic algorithm for learning fuzzy cognitive maps[J], IEEE Transactions on Fuzzy Systems23(6) (2015), 2397–2411.
24.
PedryczW., The design of cognitive maps: A study in synergy of granular computing and evolutionary optimization[J], Expert Systems with Applications37(10) (2010), 7288–7294.
25.
LiT., ZhangL., LuW., et al., Interval kernel Fuzzy C-Means clustering of incomplete data[J], Neurocomputing237 (2017), 316–331.
26.
ZhengY., JeonB., XuD., et al., Image segmentation by generalized hierarchical fuzzy C-means algorithm[J], Journal of Intelligent & Fuzzy Systems28(2) (2015), 961–973.
27.
JahromiA.T., ErM.J., LiX., et al., Sequential fuzzy clustering based dynamic fuzzy neural network for fault diagnosis and prognosis[J], Neurocomputing196 (2016), 31–41.
28.
MohrS.T., Master’s Project, Rensselaer Polytechnic Institute, The use and interpretation of fuzzy cognitive maps[J], 1997.
29.
SoltanolkotabiM., Learning relus via gradient descent[C]//, Advances in Neural Information Processing Systems, 2017, pp. 2007–2017.
30.
YuM., QuM., HuJ., A research method to capture design state based on multi-fuzzy cognitive mapping [J], Journal of Intelligent & Fuzzy Systems29(6) (2015), 2669–2675.
31.
SalmeronJ.L., Palos-SanchezP.R., Uncertainty propagation in fuzzy grey cognitive maps with Hebbian-like learning algorithms[J], IEEE Transactions on Cybernetics (2017).
32.
Nasiriyan-RadH., AmirkhaniA., NaimiA., et al., Learning fuzzy cognitive map with PSO algorithm for grading celiac disease[C]//, Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering (ICBME), 2016 23rd Iranian Conference on IEEE, 2016, pp. 341–346.
33.
ChatterjeeS., SarkarS., HoreS., et al., Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings[J], Neural Computing and Applications28(8) (2017), 2005–2016.
34.
BonyadiM.R., MichalewiczZ., Particle swarm optimization for single objective continuous space problems: A review[J], 2017.
YuD., LiD.F., MerigóJ.M., et al., Mapping development of linguistic decision making studies[J], Journal of Intelligent & Fuzzy Systems30(5) (2016), 2727–2736.
