Buradasınız

Esnek Hesaplamada Sinirsel Bulanık Sinerjiyi Temel Alan Sistemler ve Yaklaşımlar Üzerine Bir İnceleme (Derleme)

A Review on the Approaches and Systems Based on Neuro Fuzzy Synergism in the Soft Computing (Review)

Journal Name:

Publication Year:

Abstract (2. Language): 
Hybridization of artificial neural network (ANN) and fuzzy logic (FL) has drawn the attention of researchers in various studies of scientific and engineering field due to the requirements of adaptive intelligent system for solving of real-world problems. Genetic algorithm (GA) has been frequently used to optimize the problem solutions. ANN imitate the work principles of human brain, and realize the learning via using the samples in training process. FL converts the linguistic expressions to rules in a rule base via using given rules and membership functions. When ANN works in conjunction with FL to fill lacks, high performance systems are obtained. The learning ability can be added to FL-based systems via ANN usage. In neuro-fuzzy systems (NFSs), the ability of flexibility, speed and adaptivity can be fused to FL component through ANN component. In our study, 51 studies in the literature about NFSs are systematically reviewed. These studies are based on the hybridization of ANN and FL components. As can be seen from the survey, the approaches based on the adaptive neural fuzzy inference system (ANFIS) are much more used than other neuro-fuzzy systems’studies in the literature. We made a conclusion over example works in the literature.
Abstract (Original Language): 
Yapay Sinir Ağları (Artificial Neural Network, YSA) ve Bulanık Mantık (Fuzzy Logic, BM) melezleştirmesi gerçek dünya problemlerinin çözümünde uyarlanabilir zeki sistemlere olan ihtiyaç nedeniyle çeşitli bilimsel ve mühendislik alanındaki çalışmalarda araştırmacıların ilgisini çekmektedir. Problemlerin çözümünde sıklıkla eniyileme için Genetik Algoritma (Genetic Algorithm, GA) kullanılmaktadır. YSA, insan beyninin çalışma prensibini taklit ederek, eğitim sürecindeki örneklerin kullanımı sayesinde öğrenimini gerçekleştirir. BM, sözel ifadeleri verilen kurallar ve üyelik fonksiyonları kullanarak kural tabanındaki kurallara çevirmektedir. YSA ve BM birbirlerinin eksikliklerini giderdiklerinde başarımı daha yüksek sistemler elde edilmektedir. Bulanık sistemlere sinir ağı ile öğrenme yeteneği kazandırılabilmektedir. Sinirsel bulanık sistemlerde (SBS), BM bileşenine esneklik, hız ve uyarlanabilirlik gibi özellikler YSA bileşeni sayesinde kaynaştırılmaya çalışılmaktadır. Bu çalışmada, YSA ve BM bileşenlerinin melezlenmesiyle elde edilmiş literatürdeki SBS’lerle ilgili 51 adet çalışma sistematik olarak incelenmiştir. Yapılan literatür incelenmesinde Uyarlanabilir Sinirsel Bulanık Çıkarım Sistemini (Adaptive Neural Fuzzy Inference System, ANFIS) temel alan yaklaşımların diğer SBS’lere göre daha fazla sayıda çalışmada kullanıldığı görülmektedir. Literatürdeki örnek çalışmalar üzerinden değerlendirme yapılmıştır.
54
86

REFERENCES

References: 

[1] Elmas, Ç., 2007. Yapay Zekâ Uygulamaları, Seçkin Yayıncılık, Ankara.
[2] Tektaş, M., Akbaş, A., Topuz V., 2002. Yapay Zekâ Tekniklerinin Trafik Kontrolünde
Kullanılması Üzerine Bir İnceleme, 1. Uluslararası Trafik ve Yol Güvenliği Kongresi,
Gazi Üniversitesi, Ankara, 551-559.
[3] Nabiyev, V. V., 2010. Yapay Zeka İnsan – Bilgisayar Etkileşimi, Seçkin Yayıncılık,
Ankara.
[4] Negnevitsky, M., 2005. Artificial Intelligence: A Guide to Intelligent Systems, Addison
Wesley, Harlow, İngiltere.
[5] Ardıl, E., 2009. Esnek Hesaplama Yaklaşımı ile Hata Kestirimi, Doktora Tezi, Trakya Ü.
Fen Bilimleri Enstitüsü, Edirne.
[6] Ham, F. M., Kostanic, I., 2000. Principles of Neurocomputing for Science and
Engineering, Mc Graw Hill Co., U.S.A..
[7] Üstüntaş, T., Müftüoğlu, O., Şen, Z., 2006. Dijital Fotogrametride Yapısal Görüntü
Eşleştirme, İTÜ Dergisi., 5(1), 75-82.
[8] Karlik, B., Karan, O., Okatan, A., 2005. OMX-GR Alıcısı Ve Yapay Sinir Ağı
Kullanılarak Koku Algılama Sisteminin Gerçek Zamanlı İncelenmesi, Signal Processing
and Communications Applications Conference, Kayseri, Türkiye, 676 – 679.
[9] Öztemel, E., 2003. Yapay Sinir Ağları, Papatya Yayıncılık, İstanbul.
[10] Haykin, S., 2009. Neural Networks and Learning Machines (Third Edition), Pearson
Prentice Hall, New Jersey.
FALCON
Çalışma Adeti: 5
Yüzde Oranı: %9,8
Yıl Aralığı:
1997-2012
GARIC
Çalışma Adeti: 5
Yüzde Oranı: %9,8
Yıl Aralığı:
1992-2008
NEFCON
Çalışma Adeti: 5
Yüzde Oranı: %9,8
Yıl Aralığı:
1998-2005
FINEST
Çalışma Adeti: 1
Yüzde Oranı: %2
Yıl Aralığı: 1999
FUN
Çalışma Adeti: 5
Yüzde Oranı: %9,8
Yıl Aralığı:
2000-2007
SONFIN
Çalışma Adeti: 6
Yüzde Oranı:
%11,8
Yıl Aralığı:
2005-2013
FNN
Çalışma Adeti: 6
Yüzde Oranı:
%11,8
Yıl Aralığı:
2001-2012
EFuNN
Çalışma Adeti: 5
Yüzde Oranı: %9,8
Yıl Aralığı:
2001-2010
ANFIS
Çalışma Adeti: 13
Yüzde Oranı:
%25,5
Yıl Aralığı:
2007-2012
Melez Sinirsel Bulanık Sistem Tasarımlarının Kategorik
Alana Dağılımı
FALCON GARIC NEFCON FINEST FUN SONFIN FNN EFuNN ANFIS
Esnek Hesaplamada Sinirsel Bulanık Sinerjiyi Temel Alan Sistemler
81
[11] Aksel, T., 2006. SuPred: Yapay Sinir Ağları ve Saklı Markov Model kullanarak Protein
İkincil Yapı Tahmin Yöntemi, Signal Processing and Communications Applications,
Antalya, 1-4.
[12] Özkaya, N., Sağıroğlu, Ş., 2005. Parmakizi Resimlerinin Yapay Sinir Ağları ile
Temizlenmesi ve İyileştirilmesi, Elektrik-Elektronik-Bilgisayar Mühendisliği 11. Ulusal
Kongresi ve Fuarı, İstanbul, 531-537.
[13] Eroğul, O., Sipahi, M. E., Tunca, Y., Vurucu, S., 2009. Down Sendromlu Hastaların
Görüntü Analizi İle Ayırt Edilmesi, Biomedical Engineering Meeting (BIYOMUT),
İzmir, 1-4.
[14] Ekşi, Z., Dandıl, E., Çakıroğlu, M., 2012. Bilgisayar Destekli Kırık Kemik Tespiti, Signal
Processing and Communications Applications Conference (SIU), Muğla, 1-4.
[15] Akkoyun, S., ve Akkoyun, N., 2014. Unbihexium Elementinin E4, M4, E5 ve E5
Geçişleri İçin Dönüşüm Katsayılarının Yapay Sinir Ağları ile Tahmin Edilmesi,
Cumhuriyet Üniversitesi Fen Fakültesi Fen Bilimleri Dergisi, 35(1), 58-68.
[16] Şen, Z., 2009. Mühendislikte Bulanık (Fuzzy) Mantık ile Modellenme Prensipleri, Su
Vakfı Yayınları, İstanbul.
[17] Wang, L., 1996. A Course in Fuzzy Systems and Control, Prentice-Hall International,
International Edition.
[18] Yılmaz, M., Arslan, E., 2005. Bulanık Mantığın Jeodezik Problemlerin Çözümünde
Kullanılması, 2. Mühendislik Ölçmeleri Sempozyumu, İ.T.Ü., İstanbul, 512-522.
[19] Vieira, J., Dias, F., Mota, A., 2004. Neuro-Fuzzy Systems: A Survey, WSEAS
Transactions on Systems, 2(3), 414-419.
[20] Şen, Z., 2001. Bulanık Mantık ve Modelleme İlkeleri, Bilge Yayıncılık, İstanbul.
[21] Margarit, G., Tabasco, A., 2011. Ship Classification in Single-Pol SAR Images Based on
Fuzzy Logic, Geoscience and Remote Sensing, 49(8), 3129-3138.
[22] Çelebi, A. T., Güllü, M. K., Ertürk, S., 2011. Görüntüleme Sonarı ile Yakalanan
Görüntülerde Bulanık Mantık Temelli Engel Tespiti, Signal Processing and
Communications Applications Conference (SIU), Antalya, 920-923.
[23] Payıdar, A., Doğan, E., 2010. Farklı Yöntemler Kullanılarak Geliştirilen Trafik Kaza
Tahmin Modelleri ve Analizi, Int. J. Eng. Research & Development, 2(1), 16-22.
[24] Küçüktezcan, F., 2008. Genetik Algoritma ile Optimize Edilmiş Bulanık Güç Sistemi
Kararlı Kılıcısının Sistem Kararlılığına Etkisi, Yüksek Lisans Tezi, İ.T.Ü. Fen Bilimleri
Enstitüsü, İstanbul.
[25] Senthilkumar, K. S., Bharadwaj, K. K., 2009. Hybrid Genetic-Fuzzy Approach to
Autonomous Mobile Robot, Technologies for Practical Robot Applications, Woburn, 29-
34.
[26] Aksungur, S., Kavlak, K., 2009. Scara Robotun Engelli Ortamda Çarpışmasız
Hareketinin Yapay Sinir Ağları Ve GA Kullanılarak Gerçekleştirilmesi, Selçuk
Üniversitesi Teknik Bilimler Meslek Yüksekokulu Teknik-Online Dergi, 8(2), 112-126.
[27] Cortes, P., Larraneta, J., Onieva, L., 2003. Genetic Algorithm for Controllers in Elevator
Groups: Analysis and Simulation During Lunchpeak Traffic”, 7 th Int. Work-Conference
on Artificial and Natural Neural Networks (IWANN), Menorca, İspanya, 59-174.
[28] Altun, A. A., 2007. Esnek Hesaplama Yöntemleri İle Otomatik Parmakizi Tanıma,
Doktora Tezi, Selçuk Ü. Fen Bilimleri Enstitüsü, Konya.
[29] Tan, L. P, Lotfi, A., Lai, E., Hull, J.B., 2004. Soft computing applications in dynamic
model identification of polymer extrusion process, Applied Soft Computing, 4(4), 345-
355.
[30] Yardımcı, A., 2009. Applications of Soft Computing to Medical Problems, Intelligent
Systems Design and Applications, Pisa, İtalya, 614-619.
[31] Yardımcı, A., 2009. Soft computing in medicine, Applied Soft Computing, 9(3), 1029-
1043.
Koray AKI ve Bahadır KARASULU
82
[32] Verma, B., Zhang, P., 2007. A novel neural-genetic algorithm to find the most significant
combination of features in digital mammograms, Applied Soft Computing, 7(2), 612–
625.
[33] Aliev, R. A., Aliev, R. R., 2001. Structure and Constituents of Soft Computing, Soft
Computing And Its Applications, World Scientific, 3-4.
[34] Civalek, Ö., Ülker M., 2004. Dikdörtgen Plakların Doğrusal Olmayan Analizinde Yapay
Sinir Ağı Yaklaşımı, İMO Teknik Dergi, 3171-3190.
[35] Zhang, Z., Zhan, C., 2004. Agent-Based Hybrid Intelligent Systems, J. G. Carbonell and
J. Siekman, Springer, Çin.
[36] Kahramanlı, H., 2009. Hibrit Sinirsel Bulanık Ağını Kullanarak Bir Sınıflandırma ve
Kural Çıkarma Sisteminin Geliştirilmesi, Doktora Tezi, Selçuk Ü., Fen Bilimleri
Enstitüsü, Konya.
[37] Caner, M., Gülseren, U. (2011) Genetik Algoritma ile Fuzzy PSS’in Kural Tablosu
Optimizasyonu, AKÜ Fen Bilimleri Dergisi, 83-92.
[38] Sodré, E. A., Motta, W. S., Alencar, B. S., 2009. A Hybrid Intelligent System For Power
System Security Assessment, XIII ERIAC, Puerto Iguazú, Arjantin, 1-8.
[39] Güler, İ., Polat, H., Ergün, U., 2005. Combining Neural Network and Genetic Algorithm
for Prediction of Lung Sounds, Journal of Medical Systems, 29(3), 217-231.
[40] Benamrane, N., Aribi, A., Kraoula L., 2006. Fuzzy Neural Networks and Genetic
Algorithms for Medical Images Interpretation, Geometric Modeling and Imaging--New
Trends, Londra, İngiltere, 259-264.
[41] Benamrane, N., Freville, A., Nekkache, R., 2005. A Hybrid Fuzzy Neural Networks fort
he Detection of Tumors in Medical İmages, American Journal of Applied Sciences, 2(4),
892-896.
[42] Raja, K. B., Madheswaran, M., Thyagarajah K., 2008. A Hybrid Fuzzy-Neural System
for Computer-Aided Diagnosis of Ultrasound Kidney Images Using Prominent Features,
Journal of Medical Systems, 32(1), 65-83.
[43] Pena-Reyes, C. A., Sipper M., 1999. A fuzzy-genetic approach to breast cancer
Diagnosis, Artificial Intelligence in Medicine, 17(2), 131-155.
[44] Das, A., Bhattacharya, M., 2009. A Study on Prognosis of Brain Tumors Using Fuzzy
Logic and Genetic Algorithm Based Techniques, Bioinformatics, Systems Biology and
Intelligent Computing, Shanghai, Çin, 348-351.
[45] Jacobsen, H. .A., 1998. A generic Architecture for Hybrid Intelligent Systems, In
Proceedings of The IEEE Fuzzy Systems, U.S.A., 709-714.
[46] Karaköse, M., Akın, E., 2005. Akıllı Sistem Tasarımı İçin Yumuşak Hesaplama Çatısı,
Elektrik-Elektronik-Bilgisayar Mühendisliği 11. Ulusal Kongresi ve Fuarı, İstanbul.
[47] Sumathi, S., Surekha, P., 2010. Computational Intelligence Paradigms: Theory &
Applications using MATLAB, CRC Press Taylor & Francis Group, N.W., U.S.A.
[48] Avcı, D., Akpolat, Z. H., 2006. Speech recognition using a wavelet packet adaptive
network based fuzzy inference system, Expert Systems with Applications, 31(3), 495-
503.
[49] Varol, Y., Avcı, D., Koca, A. ve Oztop, H. F., 2007. Prediction of flow fields and
temperature distributions due to natural convection in a triangular enclosure using ANFIS
and ANN, International Communications in Heat and Mass Transfer, 34(7), 887-896.
[50] Abraham, A., 2001. Neuro Fuzzy Systems: State-of-the-art Modeling Techniques,
Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence Lecture
Notes in Computer Science, 269-276.
[51] Das, A., Mahua, B,. 2008. GA Based Neuro Fuzzy Techniques for Breast Cancer
Identification, International Machine Vision and Image Processing Conference, Portrush,
U.K., 136-141.
[52] Huan, M,, Che, H., Huang, J., 2007. Glaucoma Detection Using Adaptive Neuro-Fuzzy
Inference System, Expert Systems with Applications, 32(2), 458–468.
Esnek Hesaplamada Sinirsel Bulanık Sinerjiyi Temel Alan Sistemler
83
[53] Juuso, E., 2004. Integration of intelligent systems in development of smart adaptive
systems, International Journal of Approximate Reasoning, 35(3), 307–337.
[54] Özekes, S., Osman, O., Ucan, O. N., 2008. Nodule Detection in a Lung Region that’s
Segmented with Using Genetic Cellular Neural Networks and 3D Template Matching
with Fuzzy Rule Based Thresholding, Korean Journal Radiology, 9(1), 1-9.
[55] Kodogiannis, V.S., 2004. Computer-aided Diagnosis in Clinical Endoscopy using Neuro-
Fuzzy Systems, IEEE International Conference on Fuzzy Systems, 3, Budapeşte,
Macaristan, 1425-1429.
[56] Xing, J., Xiao, D., 2007. BFALCON Generalization Capability Improvement Based on
PCA Initialization, Third International Conference Natural Computation, Haikou, Çin,
398-402.
[57] Lin, C. J., Lin, C. T., 2002. An ART-Based Fuzzy Adaptive Learning Control Network,
IEEE Transactions on Fuzzy Systems, 5(4), 477-496.
[58] Viloria, A., Chang, C., Pineda, M.C., Viloria, J., 2012. Estimation of susceptibility to
landslides using neural networks based on the FALCON-ART model, 11th International
Conference on Machine Learning and Applications (ICMLA), Boca Raton, U.S.A., 655-
660.
[59] Kulalı, G.M., Gevher, M., Erkmen, A.M., Erkmen, İ. (2002) Intelligent gait synthesizer
for serpentine robots, IEEE International Conference on Robotics and Automation
(ICRA), Washington, DC, U.S.A., 1513-1518.
[60] Berenji, H.R., Khedkar, P., 1992. Learning and Tuning Fuzzy Logic Controllers Through
Reinforcements, IEEE Transactions on Neural Networks, 3(5), 724-740.
[61] Nauck, D., Kruse, R., 1998. A Neuro-Fuzzy Approach to Obtain Interpretable Fuzzy
Systems for Function Approximation, IEEE World Congress on Computational
Intelligence Fuzzy Systems Proceedings, Anchorage, U.S.A., 1106-1111.
[62] Amaral, J.F.M. , Vellasco, M.M., Tanscheit, R., Pacheco, M.A.C. (2001) A Neuro-Fuzzy-
Genetic System for Automatic Setting of Control Strategies, International Conference
IFSA World Congress and 20th NAFIPS, Vancouver, BC, Canada, 1553-1558.
[63] Arellano-Cardenas, O., Moreno-Cadenas, J.A., Gómez-Castañeda, F., Flores-Nava,
L.M., 2005. CMOS Cells with Continuously Adjustable Parameters for Implementation
of Fuzzy and Neurofuzzy Systems, 2nd International Conference on Electrical and
Electronics Engineering (ICEEE) and XI Conference on Electrical Engineering (CIE),
México City, Meksika, 378-381.
[64] Tano, S., Oyama, T., Arnould, T., 1996. Deep combination of fuzzy inference and neural
network in fuzzy inference software- FINEST, International Journal of Fuzzy Set and
System, 82(2), 151-160.
[65] Dehuri, S., Cho, S. B., 2011. Knowledge Mining Using Intelligent Agents, Imperial
College Press, Londra, İngiltere, 262-266.
[66] Tian, W., Qiao, Y., Ma, Z., 2007. A New Scheme for Off-line Signature Verification
Using DWT and Fuzzy Net, Eighth ACIS International Conference on Software
Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing,
Qingdao, Çin, 30-35.
[67] Schnitman, L., Yoneyama, T., 2000. An Efficient Implementation of a Learning Method
for Mamdani Fuzzy Models, Sixth Brazilian Symposium on Neural Networks
Proceedings, Rio de Janeiro, RJ, Brezilya, 38-43.
[68] Lin, C. T., Chao, W. H., Chen, Y. C., Liangt, S. F., 2005. Adaptive feature extractions in
an EEG-based alertness estimation system, IEEE International Conference on Systems,
Man, and Cybernetics (SMC), Hawaii, U.S.A., 2096-2101.
[69] Liu, S. H., Chang, K. M., Wang, J. J., 2011. An Autometic System for ECG Arrhythmias
Classification, IEEE International Conference on Systems, Man, and Cybernetics (SMC),
Anchorage, U.S.A., 2290-2294.
[70] Buckley, J. J., Hayashi, Y., 1994. Fuzzy neural networks: A survey, Fuzzy Sets and
Systems, 66(1), 1-13.
Koray AKI ve Bahadır KARASULU
84
[71] Lam, K. C., Hu, Tiesong, Ng, S. Thomas, Skitmore, Martin, Cheung, S. O., 2001. A
Fuzzy Neural Network Approach For Contractor Prequalification, Construction
Management and Economics, 19(2), 175-188.
[72] Mohseni, S.A., Shooredeli, M. A., 2007. Decoupled sliding-mode with fuzzy neural
network controller for EHSS velocity control, International Conference on Intelligent and
Advanced Systems (ICIAS), Kuala Lumpur, Malezya, 7-11.
[73] Kumar, A.V., Aruna, Reddy, M.V., 2011. A Fuzzy Neural Network for Speech
Recognition, ARPN Journal of Systems and Software, 1(9), 284-290.
[74] Chang, P.-C., Fan, C.-Y., Hsieh J. C., 2009. A Weighted Evolving Fuzzy Neural
Network for Electricity Demand Forecasting, First Asian Conference on Intelligent
Information and Database Systems (ACIIDS), Dong Hoi, Vietnam, 330-335.
[75] Woodford, B. J., 2010. Automatic Optimization of Pruning in Evolving Fuzzy Neural
Networks Using an Entropy Measure, International Joint Conference on Neural Networks
(IJCNN), Barcelona, Brezilya, 1-7.
[76] Yücel A., Güneri A., 2010. Tedarikçi Seçimi Problemine Uyarlanabilir Ağ Yapısına
Dayalı Bulanık Çıkarım Sisteminin Uygulanması, Journal of Engineering and Natural
Sciences, 224-234.
[77] Ankışhan, H., Efe, M., 2011. Eşzamanlı Konum Belirleme ve Harita Oluşturmada
Ayarlanabilir Bulanık Mantık Destekli Kalman Filtre Yaklaşımı, Signal Processing and
Communications Applications Conference, Antalya, 266-270.
[78] Altug, S., Chow, M., 1997. Comparative Analysis of Fuzzy Inference Systems
Implemented on Neural Structures, International Conference on Neural Networks,
Houston, TX, U.S.A., 426-43.
[79] Altug, S., Chow, M., Trussell, H. J., 1999. Fuzzy Inference Systems Implemented on
Neural Architectures for Motor Fault Detection and Diagnosis, IEEE Transactions on
Industrial Electronics, 46(6), 1069-1079.
[80] Hong, I., Oh, J., Yoo, H.-J., 2011. 1.15mW Mixed-Mode Neuro-Fuzzy Accelerator For
Keypoint Localization in Image Processing, Circuits and Systems (MWSCAS), Seoul,
Güney Kore, 1-4.
[81] Baştürk, A., Yüksel, M. E., 2007. Adaptive Neuro-Fuzzy Inference System For Speckle
Noıse Reduction in Sar Images, 9th International Symposium on Signal Processing and
Its Applications (ISSPA), Sharjah, Dubai, 1-4.
[82] Abhilash, R.H., Chauhan, S., 2012. Respiration-Induced Movement Correlation for
Synchronous Noninvasive Renal Cancer Surgery, IEEE Transactions on Ultrasonics,
Ferroelectrics and Frequency Control, 59(7), 1478-1486.
[83] Quah, XH., Quek, C., Leedham, G., 2002. Pattern Classification Using Fuzzy Adaptive
Learning Control Network And Reinforcement Learning, International Conference on
Neural Information Processing (ICONIP), Singapur, 1439-1443.
[84] Quah, XH., Quek, C., Leedham, G., 2005. Reinforcement Learning Combined With A
Fuzzy Adaptive Learning Control Network (FALCON-R) for Pattern Classification,
Pattern Recognition, 38(4), 513–526.
[85] Tan, T. Z., Quek, C., Ng, G. S., 2005. Ovarian Cancer Diagnosis Using Complementary
Learning Fuzzy Neural Network, IEEE International Joint Conference on Neural
Networks ( IJCNN), Montreal, Kanada, 3034-3039.
[86] Sagha, H., Shouraki, S. B., Khasteh, H., Dehghani, M., 2008. Real-Time IDS Using
Reinforcement Learning, Second International Symposium on Intelligent Information
Technology Application, Shanghai, Çin, 593-597.
[87] Sagha, H., Shouraki, S. B., Khasteh, H., Kiaei, A. A., 2008. Reinforcement Learning
Based on Active Learning Method”, Second International Symposium on Intelligent
Information Technology Application, Shanghai, Çin, 598-602.
[88] Zhou, C., Meng, Q., 2000. Reinforcement Learning with Fuzzy Evaluative Feedback for a
Biped Robot, International Conference on Robotics and Automation (ICRA), San
Francisco, U.S.A., 3829-3834.
Esnek Hesaplamada Sinirsel Bulanık Sinerjiyi Temel Alan Sistemler
85
[89] Nurnberger, A., Kruse, R., 1998. Neuro-Fuzzy Techniques under MATLAB/SIMULINK
Applied to a Real Plant, International Conference on Fuzzy Systems Proceedings,
Anchorage, U.S.A., 572-57.
[90] Lara-Rojo, F., Sanchez, E.N., Cuevas, E.V., 1999. Real-Time Neurofuzzy Control for an
Underactuated Robot, International Joint Conference on Neural Networks, Washington,
U.S.A., 2220-2225.
[91] Lin, H.-W., Lu, H.-F., 2007. Capital Budgeting with Fuzzy Net Present Value Criterion,
Second International Conference on Innovative Computing, Information and Control
(ICICIC), Kumamoto, Japonya, 761-764.
[92] de Almeida Neves, E.M., Borelli, J.E., Gonzaga, A., 2000. Target Search by Bottom-Up
and Top-Down Fuzzy Information, 13th Brazilian Symposium on Computer Graphics and
Image Processing, Gramado, Brezilya, 60-66.
[93] Kim, C.-J., Park, M.-S., Topalov, A. V., 2007. Unifying Strategies of Obstacle Avoidance
and Shooting for Soccer Robot Systems, International Conference on Control,
Automation and Systems (ICCAS), Seoul, Güney Kore, 207-211.
[94] Lin, C. T., Chung, I F., Ko, L. W., Chen, Y. C., Liang, S. F., Duann, J. R., 2007. EEGBased
Assessment of Driver Cognitive Responses in a Dynamic Virtual-Reality Driving
Environment, IEEE Transactions on Biomedical Engineering, 54(7), 1349-1352.
[95] Lin, C.-T., Tsai, S.-F., Ko, L.-W., 2013. EEG-Based Learning System for Online Motion
Sickness Level Estimation in a Dynamic Vehicle Environment, IEEE Transactions on
Neural Networks and Learning Systems, 24(10), 1689-1700.
[96] Juang, C.-F., Chen, L.-T., 2008. Moving object recognition by a shape-based neural fuzzy
network, Neurocomputing, 71(13), 2937-2949.
[97] Quek, C., Irawan, W., Ng, E.Y.K., 2010. A novel brain-inspired neural cognitive
approach to SARS thermal image analysis, Expert Systems with Applications, 37(4),
3040-3054.
[98] Li, R. P., Mukaidono, M., Turksen, I. B., 2002. A Fuzzy Neural Network For Pattern
Classification and Feature Selection, Fuzzy Sets and Systems, 130(1), 101-108.
[99] Jassar, S., Liao, Z., Zhao, L., 2011. A Recurrent Neuro-Fuzzy System And its Application
in Inferential Sensing, Applied Soft Computing, 11(3), 2935-2945.
[100] Farzi, S., 2012. Training of Fuzzy Neural Networks via Quantum-Behaved Particle
Swarm Optimization and Rival Penalized Competitive Learning, The International Arab
Journal of Information Technology, 9(4), 306-313.
[101] Abraham, A., Nath, B. (2001) A neuro-fuzzy approach for modelling electricity demand
in Victoria, Applied Soft Computing, 1(2), 127-138.
[102] Kasabov, N., 2006. Adaptation and interaction in dynamical systems: Modelling and rule
discovery through evolving connectionist systems, Applied Soft Computing, 6(3), 307–
322.
[103] Ng, G.S., Murali, T., Wahab, A., Sriskanthan, N., 2004. Classification of handwritten
digits using evolving fuzzy neural network, International Conference on Control
Automation, Robotics and Vision (ICARCV), Kunming, Çin, 1410-1415.
[104] Görgel, P., Sertbas, A., Ucan, O.N., 2012. A fuzzy inference system combined with
wavelet transform for breast mass classification, International Conference on
Telecommunications and Signal Processing (TSP), Prag, Çek Cumhuriyeti, 644-647.
[105] Choubey, A., Sinha, G.R., Choubey, S., 2011. A hybrid filtering technique in medical
image denoising: Blending of neural network and fuzzy inference, International
Conference on Electronics Computer Technology (ICECT), Kanyakumari,
Hindistan,170-177.
[106] Xu, W., Li, L., Xu, P., 2007. A New ANN-based Detection Algorithm of the Masses in
Digital Mammograms, IEEE International Conference on Integration Technology (ICIT),
Shenzhen, Çin, 26-30.
Koray AKI ve Bahadır KARASULU
86
[107] Noor, N.M., Khalid, N.E.A., Hassan, R., Ibrahim, S., Yassin, I.M., 2010. Adaptive
Neuro-Fuzzy Inference System for brain abnormality segmentation, IEEE Control and
System Graduate Research Colloquium (ICSGRC), Shah Alam, Malezya, 68-70.
[108] Fazeli, S., Naghibolhosseini, M., Bahrami, F., 2008. An Adaptive Neuro-Fuzzy Inference
System for Diagnosis of Aphasia, International Conference on Bioinformatics and
Biomedical Engineering (ICBBE), Shanghai, Çin, 535-538.
[109] Rezatofighi, S.H., Roodaki, A., Ahmadi Noubari, H., 2008. An enhanced segmentation of
blood vessels in retinal images using contourlet, Annual International Conference of the
IEEE Engineering in Medicine and Biology Society (EMBS), Vancouver, Kanada, 3530-
3533.
[110] Costa, E. P., Lorena, A. C., Carvalho, A. C. P. L. F., Freitas, A. A., 2007. A Review of
Performance Evaluation Measures for Hierarchical Classifiers, Association for the
Advancement of Artificial Intelligence, 1-6.
[111] Sokolova, M., Japkowicz, N., Szpakowicz, S., 2006. Beyond Accuracy, F-score and
ROC: a Family of Discriminant Measures for Performance Evaluation, American
Association for Artificial Intelligence, 1015-1021.
[112] Yang, Y., 1999. An Evaluation of Statistical Approaches to Text Categorization,
Information Retrieval, 69-90.
[113] Nazmy, T. M., EL-Messiry, H., AL-Bokhity B., 2010. Adaptive Neuro-Fuzzy Inference
System For Classification OF ECG Signals, Informatics and Systems (INFOS), Cairo,
Mısır, 1-6.
[114] Karahoca, A., Karahoca, D., Kara, A. (2009) Diagnosis of Diabetes by using Adaptive
Neuro Fuzzy Inference Systems, Soft Computing, Computing with Words and
Perceptions in System Analysis, Decision and Control, Famagusta, Kıbrıs, 1-4.

Thank you for copying data from http://www.arastirmax.com