Diğer Makaleler

51- Egrioglu E., Fildes R., Bas E., Recurrent fuzzy time series functions approaches for forecasting, Granular Coputing,Accepted Manuscript, 2021.

50- Atila Göktaş, Erol Eğrioğlu & Rukiye Dağalp (2019) Special Issue: 11th International Statistics Days Conference, Communications in Statistics: Case Studies, Data Analysis and Applications, 5:3, 167-167, DOI: 10.1080/23737484.2019.1649391.

49- Bas E., Yolcu U., Egrioglu, 2020, Intuitıonistic Fuzzy Time Series Functions Approach For Time Series Forecasting, Granular Computing, Accepted Paper.

48- Egrioglu E., Yolcu U., Bas E., 2019,  Intuitionistic high order fuzzy time series forecasting method based on pi-sigma artificial neural networks trained by artificial bee colony, Granular Computing, 4,4,639-654.

47-           Bas E., Egrioglu E., Yolcu U., Grosan C., 2019, Type 1 fuzzy function approach based on ridge regression for forecasting, Granular Computing, 4, 4, 629-637.

46- Akdeniz E., Egrioglu E., Bas E., Yolcu U. (2018) Recurrent Pi-Sigma Neural Network, Journal of Artificial Intelligence and Soft Computing Research, 8(2),121-132.

45- Yolcu, U., Jin, Y., Egrioglu, E. An ensemble of single multiplicative neuron models for probabilistic prediction (2017), 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, art. no. 7849975.

44- Eygi Erdoğan B., Egrioglu E., Akdeniz E. (2017),  Support Vector Machines vs Multiplicative Neuron Model Neural Network in Prediction of Bank Failures.  American Journal of Intelligent Systems, 7(5), 125-131.

42- Bas E., Egrioglu E., Uslu V.R.,(2017) Shrinkage Parameters for Each Explanatory Variable Found Via Particle Swarm Optimization in Ridge Regression, Peertechz J Comput Sci Eng 2(1): 012-020.

41- Bas E., Yolcu U., Egrioglu E., Cagcag Y. O., Dalar A.Z., Single Multiplicative Neuron Model Artificial Neuron Network Trained by Bat Algorithm for Time Series Forecasting, American Journal of Intelligent Systems 2016, 6(3): 74-77.

40- Kolay E., Tunç T., Egrioglu E., Classification with Some Artificial Neural Network Classifiers Trained a Modified Particle Swarm Optimization, American Journal of Intelligent Systems, 2016, 6(3): 59-65.

39- Aladag C.H., Yolcu U., Egrioglu E., I. Burhan Turksen, (2016), Type-1 fuzzy time series function method based on binary particle swarm optimization, International Journal of Data Analysis Techniques and Strategies, 8(1), 2-13.

38- Egrioglu E., Bas E., Aladag C.H., Yolcu U., (2016), Probabilistic Fuzzy Time Series Method Based onArtificial Neural Network, American Journal of Intelligent Systems,6(2), 42-47.

37- Egrioglu E., Aladag C.H., Yolcu U., Dalar A.Z., A Hybrid High Order Fuzzy Time Series Forecasting Approach Based on PSO and ANNs Methods, American Journal of Intelligent Systems 2016, 6(1): 22-29.

36- Buyuk C., Gunduz K., Egrioglu E., The Prevalence of Bifid Mandibular Condyle Detected on Cone Beam Computed Tomography Images in a Turkish Population, Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology 03/2015; 119(3):e127. DOI: 10.1016/j.oooo.2014.07.107, 2015.

35- Midilli M., Büyük C., Acıkgöz A., Egrioglu E., Incidence of Soft Tissue Calcifications of the Neck Region: A Retrospective Analysis on Cone Beam Computed Tomography, Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology 03/2015; 119(3):e142-e143. DOI: 10.1016/j.oooo.2014.07.175 , 2015.

35- Erol Egrioğlu, Cagdas Hakan Aladag, Ufuk Yolcu, Ali Zafer Dalar, A New Hybrid Fuzzy Time Series Forecasting Approach Based on Intelligent Optimization, American Journal of Intelligent Systems, 2015, 5(4): 97-108.

34- Cem Kocak, Erol Egrioglu, Ufuk Yolcu, Recurrent Type Fuzzy Time Series Forecasting Method Based on Artificial Neural Networks, American Journal of Operational Research, 2015;  5(5): 111-124.

33- Bas E., Yolcu U., Egrioglu E., Aladag Ç.H. , A Fuzzy Time Series Forecasting Method Based on Operation of Union and Feed Forward Artificial Neural Network, American Journal of Intelligent Systems 2015, 5(3): 81-91.

31- Çelenli A.Z., Egrioglu E., Çorba B.S., Imkb 30 indeksini oluşturan hisse senetleri için parçacik sürü optimizasyonu yöntemlerine dayali portföy optimizasyonu, Doğuş üniversitesi dergisi, 16 (1) 2015, 25-33.

30- Kocak C, Egrioglu E., Yolcu U., Aladağ C.H., Computing Fuzzy Cronbach Alpfa Reliability Coefficient for Fuzzy Survey Data, American Journal of Intelligent Systems, 4(5), 204-213, 2014.

29- Uslu V.R., Egrioglu E., Bas E., Finding Optimal Value for the Shrinkage Parameter in Ridge Regression via Particle Swarm Optimization, American Journal of Intelligent Systems, 4 (4), 142-147,2014.

28- Egrioglu E., Aslan Y., Aladag C.H., A new fuzzy time series method based on artificial bee colony algorithm, Turkish Journal of Fuzzy Systems, 5(1), 59-77, 2014.

27- Aladag C.H., Turksen I.B., Dalar A. Z., Egrioglu E., Yolcu U., Application of type-1 fuzzy functions approach for time series forecasting, Turkish Journal of Fuzzy Systems, 5(1), 1-9, 2014.

26- Bulut E., Egrioglu E., A new partial least square method based on Elman Neural Network, American Journal of Intelligent Systems, 4(4), 154-158, 2014.

25- Dalar A.Z., Egrioglu E., Yolcu U., Ilter D., Gundogdu O., An investigation of Differencing Effect in Multiplicative Neuron Model Artificial Neural Network for Istanbul Stock Exchange Time Series Forecasting, American Journal of Intelligent Systems, 2014, 4(1), 15-19.

24- Egrioglu E., Ozdemir B., Lagged Variables Selection For Fuzzy Time Series Models By Using Binary Particle Swarm Optimization, Aloy Journal of Soft Computing and Applications, 1(1), 2014.

23- Egrioglu E., Aladag C. H., Yolcu U., Bas E., A New Adaptive Network Based Fuzzy Inference System For Time Series Forecasting, Aloy Journal of Soft Computing and Applications, 2(1), 2014.

22- Bas E. , Uslu V.R., Yolcu U., Egrioglu E., A Fuzzy Time Series Approach Using De/Best/1 Mutation Strategy Of Differential Evolution Algorithm, Aloy Journal of Soft Computing and Applications, 2(2), 2014.

21- Bas E., Uslu V.R., Yolcu U., Egrioglu E.,  A Fuzzy Time Series Analysis Approach by Using Differential Evolution Algorithm Based on the Number of Recurrences of Fuzzy Relations, American Journal of Intelligent Systems 2013, 3(2): 75-82 DOI: 10.5923/j.ajis.20130302.04

20- Oner Y., Tunc T., Egrioglu E., Atasoy Y., Comparisons of Logistic Regression and Artificial Neural Networks in Lung Cancer Data, American Journal of Intelligent Systems 2013, 3(2): 71-74 DOI: 10.5923/j.ajis.20130302.03.

19- Uslu V.R., Bas E., Yolcu U, Egrioglu E., A New Fuzzy Tıme Serıes Analysıs Approach By Usıng Dıfferentıal Evolutıon Algorıthm And Chronologıcally Determıned  Weıghts, Journal of Social and Economic Statistics, 2(1), 18-30,  2013.

18- Egrioglu E., Aladağ C.H., Yolcu U., Corba B.S., Cagcag O., Fuzzy Time Series Method Based On Multiplicative Neuron Model and Membership Values, American Journal of Intelligent Systems,2013, 3(1), 33-39.

17- Cagcag O., Yolcu U, Egrioglu E.,Aladag C.H., A Novel Seasonal Fuzzy Time Series Method to the Forecasting of Air Pollution Data in Ankara, American Journal of Intelligent Systems 2013, 3(1): 13-19.

16- Bulut E., Yolcu U , Tasmektepligil Y.G. , Egrioglu E., The Use of Partial Least Squares Regression and Feed Forward Artificial Neural Networks Methods in Prediction Vertical and Broad Jumping of Young Football Players, World Applied Sciences Journal 21 (4): 572-577, 2013.

15- Uslu V.R., Yolcu U.,Egrioglu E., , Aladag Ç.H., Başaran M.A., Yüksek Dereceli Bulanık Zaman Serisi Yaklaşımı İle Türkiye Enflasyon Öngörüsü, Dokuz Eylül Üniversitesi İktisadi İdari Bilimler Fakültesi Fakülte Dergisi, Cilt 27, Sayı 1, 2012.

14- Aladag, C. H., Egrioglu, E., Kadılar C., Improvement in forecasting accuracy using the hybrid model of ARFIMA and Feed Forward neural network, American Journal of Intelligence Systems, Volume:2, Number:2, March, pp. 12-17, DOI: 10.5923/j.ajis.20120202.02.

13- Alpaslan F., Egrioglu E., Aladağ C.H., Tring E., A Statistical Research On Feed Forward Neural Networks For Forecasting Time Series, American Journal of Intelligence Systems, 2012, 2(3): 21-25, DOI: 10.5923/j.ajis.20120203.02.

12- Egrioglu E., A New Time Invariant Fuzzy Time Series Forecasting Method Based On Genetic Algorithm, Advances in Fuzzy Systems, Volume 2012 (2012), Article ID 785709, 6 pages.

11- Gündüz K., Açıkgöz A., Egrioglu E, Radiologic Investigation of Prevalence, associated Pathologies and Dental Anomalies of Non-third Molar Impacted Teeth in Turkish Oral Patientes, The Chinese Journal of Dental Research, Volume 14, Number 2, 2011.

10- Alpaslan F., Cagcag Ö., Egrioglu E., Reduction of patient waiting time by simulation in Ondokuz Mayıs University medical faculty department of neurosurgery, Scientific Research and Essays, Vol 6(5), pp. 1063-1070, 2011.

9- Egrioglu, E., Aladag, C.H., Kadilar, C. (2011), The CSS and Two Staged Methods for Parameter Estimation in SARFIMA Models, Journal of Probability and Statistics, Special Issue: Advances in Applied Econometrics, Volume 2011 (2011), Article ID 691058, 11 pages doi:10.1155/2011/691058 (Current Index to Statistics (CIS))

8- Alpaslan F., Erilli N.A., Yolcu U., Eğrioğlu E., Aladağ Ç.H., Bulanık Kümelemede En Uygun Küme Sayısının Yapay Sinir Ağları ve Diskriminant Analizi ile Belirlenmesi, Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 10. Ekonometri ve İstatistik Sempozyumu Özel Sayısı, 25, 475-488, 2011.

7- Aladağ Ç.H., Eğrioğlu E., Günay S., Yolcu U., Yüksek Dereceli Bulanık Zaman Serisi Modeli ve İMKB Uygulaması, Anadolu Üni. Bilim ve Teknoloji Dergisi, 11(2), 95-101, 2010.

6- Erilli N.A., Eğrioğlu E., Yolcu U., Aladağ C.H., Uslu V.R., Türkiye Enflasyonunun İleri ve Geri Beslemeli Yapay Sinir Ağlarınıın Melez Yaklaşımı ile Öngörüsü, Doğuş Üniversitesi Dergisi, 11 (1) 2010, 42-55, (Ebsco host, JEL, DOAJ, TUBITAK ULAKBIM Social Science Database ).

5- Eğrioğlu E. ve Günay S., Mevsimsel Kesirli Bütünleşik Akgürültü Sürecinde Otokorelasyon Regresyonu Yöntemi, İstatistik Araştırma Dergisi, 05,01,75-83, (2007).

4- Eğrioğlu E. ve Günay S., Otoregresif Hareketli Ortalamalar Süreçlerinde Tersinir Sıçramalı Markov Zinciri Monte Carlo Yöntemi İle Bayesci Model Seçimi, Istatıstık Araştırma Dergisi, 05,02,20-29, (2007).

3- Eğrioğlu E. ve Günay S., Uzun Dönem Bağımlı Normal Akgürültü Sürecinde Fraksiyonel Fark Parametresinin Bayes Tahmini, Anadolu Üniversitesi Bilim ve Teknoloji Dergisi, Cilt/Vol.:7-Sayı/No:2:387-391, (2006).

2- Cengiz M.A., Eğrioğlu E., Otoregresif modellerin Bayes Analizinin hava kirliliği verilerine uygulanması, İstatistik Araştırma Dergisi, 04,01,1-12,(2005).

1- Eğrioğlu, E., Günay, S. “Uzun Dönem Bağımlı Normal Akgürültü Sürecinde Otokorelasyon Regresyonu ile Parametre Tahmini”, Anadolu Üniversitesi Bilim ve Teknoloji Dergisi, Cilt 6/ Sayı 1: 61-66, (2005).