Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Neural Computing and Applications
Erschienen in: Neural Computing and Applications 2/2013

01.08.2013 | Original Article

PSO-optimized modular neural network trained by OWO-HWO algorithm for fault location in analog circuits

verfasst von: Mansour Sheikhan, Amir Ali Sha’bani

Erschienen in: Neural Computing and Applications | Ausgabe 2/2013

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Fault diagnosis of analog circuits is a key problem in the theory of circuit networks and has been investigated by many researchers in recent decades. In this paper, an active filter circuit is used as the circuit under test (CUT) and is simulated in both fault-free and faulty conditions. A modular neural network model is proposed in this paper for soft fault diagnosis of the CUT. To optimize the structure of neural network modules in the proposed scheme, particle swarm optimization (PSO) algorithm is used to determine the number of hidden layer nodes of neural network modules. In addition, the output weight optimization–hidden weight optimization (OWO-HWO) training algorithm is employed, instead of conventional output weight optimization–backpropagation (OWO-BP) algorithm, to improve convergence speed in training of the neural network modules in proposed modular model. The performance of the proposed method is compared to that of monolithic multilayer perceptrons (MLPs) trained by OWO-BP and OWO-HWO algorithms, K-nearest neighbor (KNN) classifier and a related system with the same CUT. Experimental results show that the PSO-optimized modular neural network model which is trained by the OWO-HWO algorithm offers higher correct fault location rate in analog circuit fault diagnosis application as compared to the classic and monolithic investigated neural models.
Vorheriger Artikel RETRACTED ARTICLE: Artificial neural networks application to predict the compressive damage of lightweight geopolymer
Nächster Artikel Application of non-normal p-norm trapezoidal fuzzy number in reliability evaluation of electrical substations

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Parten C, Saeks R, Pap R (1991) Fault diagnosis and neural networks. In: The proceedings of the IEEE international conference on systems, man and cybernetics, pp 1517–1521
2.
Catelani M, Gori M (1996) On the application of neural networks to fault diagnosis of electronic analog circuits. Measurement 17:73–80CrossRef
3.
El-Gamal MA, Abu El-Yazeed MF (1999) A combined clustering and neural network approach for analog multiple hard fault classification. J Elec Testing Theory Appl 14:207–217CrossRef
4.
El-Gamal MA, Abdulghafour M (2003) Fault isolation in analog circuits using a fuzzy inference system. Comput Electr Eng 29:213–229MATHCrossRef
5.
Ma H-G, Zhu X-F, Ai M-S, Wang J-D (2007) Fault diagnosis for analog circuits based on chaotic signal excitation. J Franklin Inst 344:1102–1112MATHCrossRef
6.
Cui J, Wang Y (2011) A novel approach of analog circuit fault diagnosis using support vector machine classifier. Measurement 44:281–289CrossRef
7.
8.
Qu H, Xu W, Yu Y (2007) Design of neural network output layer in fault diagnosis of analog circuit. In: The proceedings of the IEEE international conference on electronic measurement and instruments, pp 3-639–3-642
9.
Fan B, Xue P, Liu J, Dong M (2009) Analog circuit fault diagnosis based on neural network and fuzzy logic. In: The proceedings of control and decision conference, pp 199–202
10.
Gu Y, Hu Z, Liu T (2010) Fault diagnosis for analog circuits based on support vector machines. In: The proceedings of international conference on wireless networks and information systems, pp 197–200
11.
Zuo L, Hou L, Wu W, Wang J, Geng S (2009) Fault diagnosis of analog IC based on wavelet neural network ensemble. Lecture Notes Comput Sci 5553:772–779CrossRef
12.
Chruszczyk L (2011) Fault diagnosis of analog electronic circuits with tolerances in mind. In: The proceedings of international conference on mixed design of integrated circuits and systems, pp 496–501
13.
Epstein BR, Czigler M, Miller SR (1993) Fault detection and classification in linear integrated circuits: an application of discrimination analysis and hypothesis testing. IEEE Trans Comput-Aided Design 12:102–113CrossRef
14.
Catelani M, Fort A, Alippi C (2002) A fuzzy approach for soft fault detection in analog circuits. Measurement 32:73–83CrossRef
15.
El-Gamal MA (1997) A knowledge-based approach for fault detection and isolation in analog circuits. In: The proceedings of international conference on neural networks, vol 3, pp 1580–1584
16.
Catelani M, Fort A (2002) Soft fault detection and isolation in analog circuits: some results and a comparison between a fuzzy approach and radial basis function networks. IEEE Trans Instrum Meas 51:196–202CrossRef
17.
Kalpana P, Gunavathi K (2008) Wavelet based fault detection in analog VLSI circuits using neural networks. Appl Soft Comput 8:1592–1598CrossRef
18.
Isermann R (1997) Supervision, fault-detection and fault diagnosis methods: an introduction. Control Eng Prac 5:639–652CrossRef
19.
Jantos P, Grzechca D, Golonek T, Rutkowski J (2008) Heuristic methods to test frequencies optimization for analogue circuit diagnosis. Bull Polish Acad Sci Tech Sci 56:29–38
20.
Fenton W, McGinnity TM, Maguire LP (2001) Fault diagnosis of electronic systems using intelligent techniques: a review. IEEE Trans Syst Man Cybern C Appl Rev 31:269–281CrossRef
21.
Erdogan ES, Ozev S, Cauvet P (2008) Diagnosis of assembly failures for system-in-package RF tuners. In: The proceedings of the IEEE international symposium on circuits and systems, pp 2286–2289
22.
Somayajula SS, Sanchez-Sinencio E, Pineda de Gyvez J (1996) Analog fault diagnosis based on ramping power supply current signature clusters. IEEE Trans Circuits Syst II Analog Digit Signal Process 43:703–712CrossRef
23.
Maidon Y, Jervis BW, Dutton N, Lesage S (1997) Diagnosis of multifaults in analogue circuits using multilayer perceptrons. Proc Inst Electr Eng-Circuits Devices Syst 144:149–154CrossRef
24.
Chakrabarti S, Cherubal S, Chatterjee A (1999) Fault diagnosis for mixed-signal electronic systems. In: The proceedings of the IEEE aerospace conference, pp 169–179
25.
Cota EF, Negreiros M, Carro L, Lubaszewski M (2000) A new adaptive analog test and diagnosis system. IEEE Trans Instrum Meas 49:223–227CrossRef
26.
Simeu E, Mir S (2005) Parameter identification based diagnosis in linear and nonlinear mixed-signal systems. In: The proceedings of international workshop on mixed-signals test, pp 140–147
27.
Ke H, Stratigopoulos HG, Mir S (2010) Fault diagnosis of analog circuits based on machine learning. In: The proceedings of the European conference and exhibition on design, automation and test, pp 1761–1766
28.
Kyziol P, Grzechca D, Rutkowski J (2009) Multidimensional search space for catastrophic faults diagnosis in analog electronic circuits. In: The proceedings of the European conference on circuit theory and design, pp 555–558
29.
Tadeusiewicz M, Sidyk P, Halgas S (2007) A method for multiple fault diagnosis in dynamic analogue circuits. In: The proceedings of the European conference on circuit theory and design, pp 834–837
30.
Chruszczyk L, Rutkowski J (2008) Excitation optimization in fault diagnosis of analog electronic circuits. In: The proceedings of the IEEE workshop on design and diagnostics of electronic circuits and systems, pp 1–4
31.
Nissar AI, Upadhyaya SJ (1999) Fault diagnosis of mixed signal VLSI systems using artificial neural networks. In: The proceedings of the southwest symposium on mixed-signal design, pp 93-98
32.
33.
Long B, Huang J, Tian S (2008) Least squares support vector machine based analog-circuit fault diagnosis using wavelet transform as preprocessor. In: The proceedings of international conference on communications, circuits and systems, pp 1026–1029
34.
Zhou L-F, Shi Y-B, Zhang W (2009) Soft fault diagnosis of analog circuit based on particle swarm optimization. J Electron Sci Technol China 7:358–361
35.
Yuan T, Xie Y, Chen G (2009) Analog circuit fault diagnosis using higher order cumulants. In: The proceedings of international conference on electronic measurement and instruments, vol 4, pp 1023–1027
36.
Xiao Y, Feng L (2012) A novel linear ridgelet network approach for analog fault diagnosis using wavelet-based fractal analysis and kernel PCA as preprocessors. Measurement 45:297–310CrossRef
37.
Bo F, Peng X, Junjie L, Ming D (2009) Analog circuit fault diagnosis based on neural network and fuzzy logic. In: The proceedings of Chinese conference on control and decision, pp 199–202
38.
Tang J, Shi Y, Jiang D (2009) Analog circuit fault diagnosis with hybrid PSO-SVM. In: The proceedings of the IEEE circuits and systems international conference on testing and diagnosis, pp 1–5. doi:10.​1109/​CAS-ICTD.​2009.​4960778
39.
Li H, Zhang Y (2009) An algorithm of soft fault diagnosis for analog circuit based on the optimized SVM by GA. In: The proceedings of international conference on electronic measurement and instruments, vol 4, pp 1023–1027
40.
Stosović MA, Litovski V (2010) Hierarchical approach to diagnosis of electronic circuits using ANNs. In: The proceedings of 10th symposium on neural network applications in electrical engineering, pp 117–122
41.
Liu H, Chen G, Song G, Han T (2009) Analog circuit fault diagnosis using bagging ensemble method with cross-validation. In: The proceedings of international conference on mechatronics and automation, pp 4430–4434
42.
Grasso F, Luchetta A, Manetti S, Piccirilli MC (2010) Symbolic techniques in neural network based fault diagnosis of analog circuits. In: The proceedings of international workshop on symbolic and numerical methods, modeling and applications to circuit design, pp 1–4
43.
Li X, Zhang Y, Wang S, Zhai G (2011) A method for analog circuits fault diagnosis by neural network and virtual instruments. In: The proceedings of international workshop on intelligent systems and applications, pp 1–5
44.
Rutkowski G (1992) A neural approach to fault location in nonlinear DC circuits. In: The proceedings of international conference on artificial neural networks, pp 1123–1126
45.
Yu S, Jervis B, Eckersall K, Bell I, Hall A, Taylor G (1994) Neural network approach to fault diagnosis in CMOS opamps with gate oxide short faults. Electron Lett 30:695–696CrossRef
46.
Fanni A, Giua A, Sandoli E (1996) Neural networks for multiple fault diagnosis in analog circuits. In: Neural networks theory, technology and applications (IEEE technology update series), pp 745–752
47.
Liu H, Chen G, Jiang S, Song G (2008) A survey of feature extraction approaches in analog circuit fault diagnosis. In: The proceedings of the IEEE Pacific-Asia workshop on computational intelligence and industrial application, pp 676–680
48.
Catelani M, Fort A (2000) Fault diagnosis of electronic analog circuits using a radial basis function network classifier. Measurement 28:147–158CrossRef
49.
Litovski V, Andrejević M, Zwolinski M (2006) Analogue electronic circuit diagnosis based on ANNs. Microelectron Reliab 46:1382–1391CrossRef
50.
Mohammadi K, Seyyed Mahdavi SJ (2008) On improving training time of neural networks in mixed signal circuit fault diagnosis applications. Microelectron Reliab 48:781–793CrossRef
51.
Xiao Y, He Y (2011) A novel approach for analog fault diagnosis based on neural networks and improved kernel PCA. Neurocomputing 74:1102–1115CrossRef
52.
Chen HH, Manry MT, Chandrasekaran H (1996) A neural network training algorithm utilizing multiple sets of linear equations. In: The proceedings of Asilomar conference on signals, systems and computers, pp 1166–1170
53.
Sheikhan M, Sha’bani AA (2009) Fast neural intrusion detection system based on hidden weight optimization algorithm and feature selection. World Appl Sci J 7 (Special Issue of Computer and IT):45–53
54.
Bandler JW (1985) Fault diagnosis of analog circuits. Proc IEEE 73:1279–1325CrossRef
55.
Xiao Y, Feng L (2012) A novel neural-network approach of analog fault diagnosis based on kernel discriminant analysis and particle swarm optimization. Appl Soft Comput 12:904–920CrossRef
56.
He W, Wang P (2010) Analog circuit fault diagnosis based on RBF neural network optimized by PSO algorithm. In: The proceedings of international conference on intelligent computation technology and automation, vol 1, pp 628–631
57.
Yin S, Chen G, Xie Y (2006) Wavelet neural network based fault diagnosis in nonlinear analog circuits. J Syst Eng Electron 17:521–526CrossRef
58.
Luo H, Wang Y, Cui J (2011) A SVDD approach of fuzzy classification for analog circuit fault diagnosis with FWT as preprocessor. Expert Syst Appl 38:10554–10561CrossRef
59.
Czaja Z, Zielonko R (2004) On fault diagnosis of analogue electronic circuits based on transformations in multi-dimensional spaces. Measurement 35:293–301CrossRef
60.
He Y, Zhu W (2009) Fault diagnosis of nonlinear analog circuits using neural networks and multi-space transformations. Lect Notes Comput Sci 5553:714–723CrossRef
61.
Seyyed Mahdavi SJ, Mohammadi K (2009) Evolutionary derivation of optimal test sets for neural network based analog and mixed signal circuits fault diagnosis approach. Microelec Reliab 49:199–208CrossRef
62.
Jain V, Pillai GN, Gupta I (2011) Fault diagnosis in analog circuits using multiclass relevance vector machine. In: The proceedings of international conference on emerging trends in electrical and computer technology, pp 641–643
63.
Jantos P, Grzechca D, Golonek T, Rutkowski J (2007) Gene expression programming-based method of optimal frequency set determination for purpose of analogue circuits’ diagnosis. Adv Soft Comput 45:794–801CrossRef
64.
Bilski P, Wojciechowski JM (2007) Automated diagnostics of analog systems using fuzzy logic approach. IEEE Trans Instrum Meas 56:2175–2185CrossRef
65.
Shen L, Tay FEH, Qu L, Shen Y (2000) Fault diagnosis using rough sets theory. Comput Ind 43:61–72CrossRef
66.
Hu M, Wang H, Hu G, Yang S (2007) Soft fault diagnosis for analog circuits based on slope fault features and BP neural networks. Tsinghua Sci Technol 12:26–31CrossRef
67.
Wang L, Liu Y, Li X, Guan J, Song Q (2010) Analog circuit fault diagnosis based on distributed neural network. J Comput 5:1747–1754
68.
Luo H, Wang Y, Lin H, Jiang Y (2012) Module level fault diagnosis for analog circuits based on system identification and genetic algorithm. Measurement (Article in Press, doi:10.​1016/​j.​measurement.​2011.​12.​010, Published online 8 Jan 2012)
69.
Deng Y, He Y, Sun Y (2000) Fault diagnosis of analog circuits with tolerances using artificial neural networks. In: The proceedings of the IEEE Asia-Pacific conference on circuits and systems, pp 292–295
70.
Mohammadi K, Mohseni Monfared AR, Molaei Nejad A (2002) Fault diagnosis of analog circuits with tolerances by using RBF and BP neural networks. In: The proceedings of student conference on research and development, pp 317–321
71.
Han B, Wu H (2009) Based on compact type of wavelet neural network tolerance analog circuit fault diagnosis. In: The proceedings of international conference on information engineering and computer science, pp 1–4. doi:10.​1109/​ICIECS.​2009.​5363065
72.
Kugarajah T, Zhang Q (2005) Mutidimensional wavelet frames. IEEE Trans Neural Netw 6:1552–1556CrossRef
73.
Yuan L, He Y, Huang J, Sun Y (2010) A new neural-network-based fault diagnosis approach for analog circuits by using kurtosis and entropy as a preprocessor. IEEE Trans Instrum Meas 59:586–595CrossRef
74.
He Y, Tan Y, Sun Y (2004) Wavelet neural network approach for fault diagnosis of analogue circuits. IEEE Proc Circ Devices Syst 151:379–384CrossRef
75.
Lee CM, Ko CN (2009) Time series prediction using RBF neural networks with a nonlinear time-varying evolution PSO algorithm. Neurocomputing 73:449–460CrossRef
76.
Yu J, Wang S, Xi L (2008) Evolving artificial neural networks using an improved PSO and DPSO. Neurocomputing 71:1054–1060CrossRef
77.
Leung SYS, Tang Y, Wong WK (2011) A hybrid particle swarm optimization and its application in neural networks. Experts systems with applications (Article in Press, doi:10.​1016/​j.​eswa.​2011.​07.​028, Published online 22 July 2011)
78.
Luitel B, Venayagamoorthy GK (2010) Quantum inspired PSO for the optimization of simultaneous recurrent neural networks as MIMO learning systems. Neural Netw 23:583–586CrossRef
79.
Zhang JR, Zhang J, Lok TM, Lyu MR (2007) A hybrid particle swarm optimization-back propagation algorithm for feedforward neural network training. Appl Math Comput 185:1026–1037MATHCrossRef
80.
Shen W, Guo X, Wu C, Wu D (2011) Forecasting stock indices using radial basis function neural networks optimized by artificial swarm algorithm. Knowl-Based Syst 24:378–385CrossRef
81.
Li J, Liu X (2011) Melt index prediction by RBF neural network optimized with an MPSO-SA hybrid algorithm. Neurocomputing 74:735–740CrossRef
82.
Li M, He Y, Yuan L (2010) Fault diagnosis of analog circuit based on wavelet neural networks and chaos differential evolution algorithm. In: The proceedings of international conference on electrical and control engineering, pp 986–989
83.
Sharkey AJ (1996) On combining artificial neural networks. Connect Sci 8:299–313CrossRef
84.
Sharkey AJ (1997) Modularity, combining and artificial neural nets. Connect Sci 9:3–10CrossRef
85.
Gomi H, Kawato M (1993) Recognition of manipulated objects by motor learning with modular architecture networks. Neural Netw 6:485–497CrossRef
86.
Lee T (1991) Structure level adaptation for artificial neural networks. Kluwer, The NetherlandsCrossRef
87.
Osherson DN, Weinstein S, Stoli M (1990) Modular learning. In: Computational Neuroscience, pp 369–377
88.
Kosslyn SM (1994) Image and brain. MIT Press, Cambridge
89.
Haykin S (1994) Neural networks, a comprehensive foundation. Macmillan College Publishing Company, UKMATH
90.
Jordan MI, Jacobs RA (1991) Task decomposition through competition in a modular connectionist architecture: the what and where vision tasks. Cogn Sci 15:219–250CrossRef
91.
French RM (1999) Catastrophic forgetting in connectionist networks. Trends Cogn Sci 3:128–135CrossRef
92.
Auda G, Kamel M (1998) Modular neural networks classifiers: a comparative study. J Intell Rob Syst 21:117–129CrossRef
93.
Raafat H, Rashwan M (1993) A tree structured neural network. In: The proceedings of international conference on document analysis and recognition, pp 939–942
94.
Auda G, Kamel M, Raafat H (1996) Modular neural network architecture for classification. In: The proceedings of the IEEE international conference on neural networks, vol 2, pp 1279–1284
95.
Auda G, Kamel M, Raafat H (1995) Modular neural network architecture for classification. In: The proceedings of the IEEE international conference on neural networks, vol 3, pp 1240–1243
96.
Waibel A (1989) Modular construction of time-delay neural networks for speech recognition. Neural Comput 1:39–46CrossRef
97.
Jacobs RA, Jordan MI, Nowlan SJ, Hinton GE (1991) Adaptive mixtures of local experts. Neural Comput 3:79–87CrossRef
98.
Becker S, Hinton GE (1993) Learning mixture-models of spatial coherence. Neural Comput 5:267–277CrossRef
99.
Alpaydin E, Jordan MI (1996) Local linear perceptrons for classification. IEEE Trans Neural Netw 7:788–792CrossRef
100.
Tan Y, He Y, Fang G (2007) Hierarchical neural networks method for fault diagnosis of large-scale analog circuits. Tsinghua Sci Technol 12:260–265CrossRef
101.
Sartori MA, Antsaklis PJ (1991) A simple method to derive bounds on the size and to train multilayer neural networks. IEEE Trans Neural Netw 2:467–471CrossRef
102.
Rohani K, Chen MS, Manry MT (1992) Neural subnet design by direct polynomial mapping. IEEE Trans Neural Netw 3:1024–1026CrossRef
103.
Werbos P (1988) Backpropagation: past and future. In: The proceedings of the IEEE international conference on neural networks, pp 343–353
104.
Scalero RS, Tepedelenlioglu N (1992) A fast new algorithm for training feedforward neural networks. IEEE Trans Signal Process 40:202–210CrossRef
105.
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: The proceedings of the IEEE international conference on neural networks, vol 4, pp 1942–1948
106.
Shi Y, Eberhart R (1998) Parameter selection in particle swarm optimization. In: The proceedings of international conference on evolutionary programming, pp 591–601
107.
Engelbrecht AP (2007) Computational intelligence-an introduction, 2nd edn. Wiley, Chapter 16, pp 289–357
108.
Eberhart RC, Simpson PK, Dobbins RW (1996) Computational intelligence PC tools, 1st edn. Academic Press Professional
109.
Riedmiller M, Braun H (1993) A direct adaptive method for faster backpropagation learning: The RPROP algorithm. In: The proceedings of the IEEE international conference on neural networks, pp 586–591
110.
Abu El-Yazeed MF, Mohsen AAK (2003) A preprocessor for analog circuit fault diagnosis based on Prony’s method. Int J Electro Commun 57:16–22CrossRef
Metadaten
Titel
PSO-optimized modular neural network trained by OWO-HWO algorithm for fault location in analog circuits
verfasst von
Mansour Sheikhan
Amir Ali Sha’bani
Publikationsdatum
01.08.2013
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 2/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-012-0947-9

Weitere Artikel der Ausgabe 2/2013

Neural Computing and Applications 2/2013 Zur Ausgabe

Original Article

RETRACTED ARTICLE: Mass classification method in mammograms using correlated association rule mining

Original Article

Utilizing ANFIS for prediction water absorption of lightweight geopolymers produced from waste materials

Original Article

Estimation of pressuremeter modulus and limit pressure of clayey soils by various artificial neural network models

Original Article

A novel solution procedure for fuzzy fractional heat equations by homotopy analysis transform method

Original Article

Artificial neural networks for prediction compressive strength of geopolymers with seeded waste ashes

Original Article

An adaptive class pairwise dimensionality reduction algorithm