Prediction of Fuzzy Sunspot Time Series By Using RBFANN

Main Article Content

Azher. A. Mohamad
Nazar.K.Hussein
Ibrahim. A. Morbat

Abstract

We are present in this paper a new modified method of prediction of  Sunspot time series after Fuzzify the data by using Fuzzy C-means clustering method (FCM). Our method consist of forecasting as first phase and prediction as a second phase. The accuracy of the results that we obtained is measured by M.S.E and we got a good results.

Downloads

Download data is not yet available.

Article Details

Section

Articles

How to Cite

Prediction of Fuzzy Sunspot Time Series By Using RBFANN. (2023). Tikrit Journal of Pure Science, 22(11), 106-112. https://doi.org/10.25130/tjps.v22i11.924

References

[1] L. A. Zadeh, Fuzzy sets, Information and Control,

vol. 8(1965), pp. 338-353.

[2] Q. Song, and B.S. Chissom, Fuzzy time series and

its models, Fuzzy Sets and Systems, vol. 54(1993a),

pp. 269-277.

[3] S. M. Chen, Forecasting enrollments based on

fuzzy time series, Fuzzy Sets and Systems, vol.

81(1996), pp. 311–319.

[4] Q. Song and B. S. Chissom, Forecasting

enrollments with fuzzy time series – part I, Fuzzy Sets

and Systems, vol. 54(1993), pp. 1–9.

[5] Q. Song and B. S. Chissom, Forecasting

enrollments with fuzzy time series – part II, Fuzzy

Sets and Systems, vol. 62(1994), pp. 1–8.

[6] S. M. Chen, Forecasting enrollments based on

high-order fuzzy time series, Cybernetics and

Systems, vol. 33(2002), no. 1, pp. 1–16, Jan.

[7] S. M. Chen and N. Y. Wang, Fuzzy forecasting

based on fuzzy-trend logical relationship groups,

IEEE Trans. Systems, Man, and Cybernetics, Part B:

Cybernetics, vol. 40(2010), no. 5 pp. 1343–1358.

[8] E. Egrioglu, C.H. Aladag, U. Yolcu, V.R. Uslu,

V.R., and M.A. Basaran, Finding an optimal interval

length in high order fuzzy time series, Expert

Systems with Applications, vol. 37(2010), pp. 5052-

5055.

[9] E. Egrioglu, C.H. Aladag, U. Yolcu, V.R. Uslu,

V.R., and M.A. Basaran, A new approach based on

the optimization of the length of intervals in fuzzy

time series, Journal of Intelligent and Fuzzy Systems,

vol. 22(2011), pp. 15-19.

[10] C.H. Cheng, G.W. Cheng, and J.W. Wang,

Multi-attribute fuzzy time series method based on

fuzzy clustering, Expert Systems with Applications,

vol. 34(2008), pp. 1235-1242.

[11] S.T. Li, Y.C. Cheng, and S.Y. Lin, A FCMbased

deterministic forecasting model for fuzzy time

series, Computers and Mathematics with

Applications, vol. 56(2008), no.12, pp.3052-3063.

[12] K. Huarng and T. H. K. Yu, The application of

neural networks to forecast fuzzy time series, Physica

A: Statistical Mechanics and its Applications, vol.

363(2006), no. 2, pp. 481-491.

[13] C. H. Aladag, M. A. Basaran, E. Egrioglu, U.

Yolcu and V. R. Uslu, Forecasting in high order

fuzzy times series by using neural networks to define

fuzzy relations, Expert Systems with Applications,

vol. 36(2009), no. 3, pp. 4228-4231.

[14] T. H. K. Yu and K. H. Huarng, a neural networkbased

fuzzy time series model to improve forecasting,

Expert Systems with Applications, vol. 37(2010), no.

4, pp. 3366-3372.

[15] E. Bas, V. Yolcu, E. Egrioglu, and C.H. Aladag,

a fuzzy time series forecasting method based on

operation of union and feedforward artificial neural

network, American Journal of Intelligent Systems,

vol. 5(2015), no. 3, pp.81-91.

[16] J.C. Bezdek, Pattern recognition with fuzzy

objective function algorithms, Plenum Press, N.Y.,

1981.

[17] P.M. Seneha and A.U. Bapat, clustering

algorithms for radial basis function neural network,

Transaction on Electrical and Electronic Engineering,

vol. 1(2013), no.1, pp.113-116.

[18] C.M. Bishop, Neural Networks for Pattern

Recognition, Oxford university press, 1995.

[19] W. Pedrycz, Conditional Fuzzy C-means,

Pattern Recognition Letters, vol. 17(1996), no. 2, pp.

625-632.

Similar Articles

You may also start an advanced similarity search for this article.