A meta-cognitive interval type-2 fuzzy inference system and its projection based learning algorithm

Evolving Systems

Volumn 5, Issue 4, 2014, Pages 219-230

  Subramanian, Kartick ^a   Das, Ankit Kumar ^a   Sundaram, Suresh ^a   Ramasamy, Savitha ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Attention deficiency hyperactivity disorder; Classification; Interval type 2 fuzzy systems; Meta cognition; Projection based learning; Self regulation

Indexed keywords

BENCHMARKING; CLASSIFICATION (OF INFORMATION); ERRORS; FUZZY INFERENCE; FUZZY NEURAL NETWORKS; FUZZY SYSTEMS; INFERENCE ENGINES; LEARNING SYSTEMS; NETWORK LAYERS; OBJECT ORIENTED PROGRAMMING; SUPPORT VECTOR MACHINES;

HYPERACTIVITY DISORDER; INTERVAL TYPE-2 FUZZY; META COGNITIONS; PROJECTION BASED LEARNING; SELF REGULATION;

LEARNING ALGORITHMS;

EID: 84911968214     PISSN: 18686478     EISSN: 18686486     Source Type: Journal    
DOI: 10.1007/s12530-013-9102-9     Document Type: Article

References (70)

1. Abiyev RH, Kaynak O, Alshanableh T, Mamedov F (2011) A type-2 neuro-fuzzy system based on clustering and gradient techniques applied to system identification and channel equalization. Appl Soft Comput 11(1):1396–1406
2. Angelov P (2011) Fuzzily connected multimodel systems evolving autonomously from data streams. Syst Man Cybern Part B: Cybern IEEE Trans 41(4):898–910
3. Angelov P, Filev P (2004a) An approach to online identification of Takagi-Sugeno fuzzy models. IEEE Trans Syst Man Cybern Part B: Cybern 34(1):484–498
4. Angelov P, Filev P (2004b) An approach to online identification of Takagi-Sugeno fuzzy models. IEEE Trans Syst Man Cybern Part B: Cybern 34(1):484–498
5. Angelov P, Filev P (2005) Simpl_eTS: a simplifed method for learning evolving Takagi-Sugeno fuzzy models. IEEE Int Conf Fuzzy Syst:1068–1073
6. Angelov P, Lughofer E, Zhou X (2008) Evolving fuzzy classifiers using different model architectures. Fuzzy Sets Syst 159(23):3160–3182
7. Ashburner J (2007) A fast diffeomorphic image registration algorithm. NeuroImage 38(1):95–113
8. Babu G, Suresh S (2013a) Meta-cognitive RBF network and its projection based learning algorithm for classification problems. Appl Soft Comput 13(1):654–666
9. Babu G, Suresh S (2013b) Sequential projection based metacognitive learning in a radial basis function network for classification problems. IEEE Trans Neural Netw Learn Syst 24(2):194–206
10. Banaschewski T, Becker K, Scherag S, Franke B, Coghill D (2010) molecular genetics of attention-deficit/hyperactivity disorder: an overview. Eur Child Adolesc Psychiatry 19(3):237–257
11. Blake C, Merz C (1998) UCI repository of machine learning databases. http://archive.ics.uci.edu/ml/. Department of Information and Computer Sciences, University of California, Irvine
12. Bledsoe JC, Semrud-Clikeman M, Pliszka SR (2011) Neuroanatomical and neuropsychological correlates of the cerebellum in children with attention-defcit/hyperactivity disorder-combined type. J Am Acad Child Adolesc Psychiatry 50(6):593–601
13. Castillo O, Melin P (2012) A review on the design and optimization of interval type-2 fuzzy controllers. Appl Soft Comput 12(4):1267–1278
14. Castro JR, Castillo O, Melin P, Rodriguez-Diaz A (2009) A hybrid learning algorithm for a class of interval type-2 fuzzy neural networks. Inf Sci 179(13):2175–2193
15. Chang C, Lin C (2003) LIBSVM: a library for support vector machines. http://www.csie.ntu.edu.tw/cjlin/libsvm/. National Taiwan University, Taiwan, Deptartment of Computer Science and Information Engineering
16. Cherkasova LHMV (2009) Neuroimaging in attention defcit hyperactivity disorder: beyond the frontostriatal circuitry. Can J Psychiatry 54(10):651–664
17. Cortese S (2012) The neurobiology and genetics of attention-deficit/hyperactivity disorder (ADHD): what every clinician should know. Eur J Paediatr Neurol 16(5):422–433
18. Frodl T, Skokauskas N (2012) Meta-analysis of structural MRI studies inchildren and adults with attention defcit hyperactivity disorder indicates treatment efects. Acta Psychiatr Scand 125(2):114–126
19. Frodl T, Stauber J, Schaaff N, Koutsouleris N, Scheuerecker J, Ewers M, Omerovic M, Opgen-Rhein M, Hampel H, Reiser M, Moller HJ, Meisenzahl E (2010) Amygdala reduction in patients with ADHD compared with major depression and healthy volunteers. Acta Psychiatr Scand 121(2):111–118
20. Giedd JN, Rapoport JL (2010) Structural MRI of pediatric brain development: what have we learned and where are we going? Can J Psychiatry 67(5):728–734
21. Huang G, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B: Cybern 42(2): 513–529
22. Ivanov I, Bansal R, Hao X, Zhu H, Kellendonk C, Miller L (2010) Morphological abnormalities of the thalamus in youths with attention defcit hyperactivity disorder. Am J Psychiatry 167(4):397–408
23. Jack CR, Petersen RC, Brien PCO, Tangalos EG (1992) Mr-based hippocampal volumetry in the diagnosis of alzheimer’s disease. Neurology 42(1):183–8
24. Jang J (1993) ANFIS: adaptive network based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685
25. Juang CF (2008) A self-evolving interval type-2 fuzzy neural network with online structure and parameter learning. IEEE Trans Fuzzy Syst 16(6):1411–1424
26. Juang CF (2010) An interval type-2 fuzzy neural network with support vector regerssion for noisy regression problems. IEEE Trans Fuzzy Syst 18(4):686–699
27. Juang CF, Lin CT (2002) An on-line self-constructing neural fuzzy inference network and its applications. IEEE Trans Fuzzy Syst 10(2):144–154
28. Kayacan E, Oniz Y, Aras AC, Kaynak O, Abiyev R (2011) A servo system control with time varying and nonlinear load conditions using type-2 tsk fuzzy neural system. Appl Soft Comput 11(8):5735–5744
29. Leite D, Ballini R, Costa P, Gomide F (2012) Evolving fuzzy granular modeling from nonstationary fuzzy data streams. Evol Syst 3(2):65–79
30. Liang Q, Mendel J (2000) Interval type-2 fuzzy logic systems: Theory and design. IEEE Trans Fuzzy Syst 8(5):535–550
31. Lucas L, Centeno T, Delgado M (2007) General type-2 fuzzy inference systems: analysis, design and computational aspects. In: Fuzzy Systems Conference, pp 1–6
32. Lughofer E (2008) FLEXFIS: a robust incremental learning approach for evolving takagi sugeno fuzzy models. Fuzzy Syst IEEE Trans 16(6):1393–1410
33. Lughofer E (2012) A dynamic split-and-merge approach for evolving cluster models. Evol Syst 3(3):135–151
34. Lughofer E, Angelov P (2011) Handling drifts and shifts in on-line data streams with evolving fuzzy systems. Appl Soft Comput 11(2):2057–2068
35. Maciel L, Lemos A, Gomide F, Ballini R (2012) Evolving fuzzy systems fro pricing fixed income options. Evol Syst 3(1):5–18
36. Mahanand BS, Savitha R, Suresh S (2013) Computer aided diagnosis of ADHD using brain magnetic resonance images. LNCS 8272:386–395
37. Mendel J (2007a) Advances in type-2 fuzzy sets and systems. Inf Sci 177(1):84–110
38. Mendel J (2007b) Type-2 fuzzy sets and systems: an overview. IEEE Comput Intell Mag 2(1):20–29
39. Milham PM, Damien F, Maarten M, Stewart HM (2012) The ADHD- 200 consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience. Front Syst Neurosci 6:1–5
40. Nelson TO, Narens L (1990) Metamemory: A theoretical framework and new findings. Psychol Learn Mot 26(C):125–173
41. Polanczyk G, Lima MSD, Horta BL, Biederman J, Rohde LA (2007) The worldwide prevalence of ADHD: a systematic review and metaregression analysis. Am J Psychiatry 164(6):942–948
42. Rong H, Sundararajan N, Huang G, Saratchandran P (2006) Sequential adaptive fuzzy inference system SAFIS for nonlinear system identification and prediction. Fuzzy Sets Syst 157(9):1260–1275
43. Rong HJ, Huang GB, Sundararajan N, Saratchandran P (2009) Online sequential fuzzy extreme learning machine for function approximation and classification problems. IEEE Trans Syst Man Cybern Part B: Cybern 39(4):1067–1072
44. Rubio JJ (2009) SOFMLS: online self-organizing fuzzy modified least-squares network. Fuzzy Syst IEEE Trans 17(6):1296–1309
45. Rubio JJ (2012) Modified optimal control with a back propagation network for robotic arms. Control Theory Appl IET 6(14):2216–2225
46. Rubio JJ (2014) Evolving intelligent algorithms for the modelling of brain and eye signals. Appl Soft Comput 14(B):259–268
47. Rubio JJ, Humberto Perez-Cruz J (2014) Evolving intelligent system for the modelling of nonlinear systems with dead-zone input. Appl Soft Comput 14(B):289–304
48. Sateesh Babu G, Suresh S (2012) Meta-cognitive neural network for classification problems in a sequential learning framework. Neurocomputing 81(1):86–96
49. Sateesh Babu G, Suresh S (2013) Parkinson’s disease prediction using gene expression—a projection based learning meta-cognitive neural classifier approach. Expert Syst Appl 40(5):1519–1529
50. Savitha R, Suresh S, Sundararajan N (2012a) A meta-cognitive learning algorithm for a fully complex-valued relaxation network. Neural Netw 32(Special Issue):209–218
51. Savitha R, Suresh S, Sundararajan N (2012b) Metacognitive learning in a fully complex valued radial basis function neural network. Neural Comput 24(5):1297–1328
52. Savitha R, Suresh S, Sundararajan N (2013) Projection based fast learning fully complex-valued relaxation neural network. IEEE Trans Neural Netw Learn Syst 24(4):529–541
53. Shaw P, Lerch J, Greenstein D, Sharp W, Clasen L, Evans A, Giedd J, Castellanos F, Rapoport J (2006) Longitudinal mapping of cortical thickness and clinical outcome in children and adolescents with attention-defcit/hyperactivity disorder. Arch Gen Psychiatry 63(5):540–549
54. Song Q, Kasabov N (2002) Dynamic evolving neuro-fuzzy inference system DENFIS: Online learning and application for time-series prediction. IEEE Trans Fuzzy Syst 10(2):144–154
55. Subramanian K, Suresh S (2012a) Human action recognition using meta-cognitive neuro-fuzzy inference system. Int J Neural Sys 22(6):1250028 (15)
56. Subramanian K, Suresh S (2012b) A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system. Appl Soft Comput 12(11):3603–3614
57. Subramanian K, Suresh S (2013) A projection based learning algorithm for Meta-Cognitive Neuro-Fuzzy Inference system. In: Fuzzy systems (FUZZ), 2013 IEEE International Conference, pp 1–8
58. Subramanian K, Savitha R, Suresh S (2012a) Complex-valued neuro-fuzzy inference system for wind prediction. In: International Joint Conference on Neural Networks(IJCNN), Brisbane, pp 1–7
59. Subramanian K, Savitha R, Suresh S, Mahanand B (2012b) Complex-valued neuro-fuzzy inference system based classifier. In: Swarm, evolutionary, and memetic computing. Springer, Berlin Heidelberg, pp 348–355
60. Subramanian K, Savitha R, Suresh S (2013a) A meta-cognitive interval type-2 fuzzy inference system classifier and its projection based learning algorithm. In: Evolving and adaptive intelligent systems (EAIS), 2013 IEEE Conference, pp 48–55
61. Subramanian K, Suresh S, Sundararajan N (2013b) A metacognitive neuro-fuzzy inference system (mcfis) for sequential classification problems. Fuzzy Syst IEEE Trans 21(6):1080–1095
62. Subramanian K, Savitha R, Suresh S (2014) A complex-valued neuro-fuzzy inference system and its learning mechanism. Neurocomputing 123:110–120
63. Suresh S, Babu RV, Kim HJ (2009) No-reference image quality assessment using modified extreme learning machine classifier. Appl Soft Comput 9(2):541–552
64. Suresh S, Dong K, Kim H (2010) A sequential learning algorithm for self-adaptive resource allocation network classifier. Neurocomputing 73(16–18):3012–3019
65. Suresh S, Savitha R, Sundararajan N (2011) A sequential learning algorithm for complex valued self regulating resource allocation network- CSRAN. IEEE Trans Neural Netw 22(7):1061–1072
66. Suresh S, Sundararajan N, Saratchandran P (2008) Risk sensitive loss functions for sparse multi-category classification problems. Inf Sci 179(21):2621–2638
67. Tavoosi J, Badamchizadeh MA (2012) A class of type-2 fuzzy neural networks for nonlinear dynamical system identification. Neural Comput Appl 23(3–4):707–717
68. Vazquez DM, Rubio JJ, Pacheco J (2012) Characterisation framework for epileptic signals. Image Process IET 6(9):1227–1235
69. Wu D (2012) An overview of alternative type-reduction approaches for reducing the computational cost of interval type-2 fuzzy logic controllers. In: IEEE Intl. Conf. on Fuzzy Systems, pp 1–8
70. Zhang T (2003) Statistical behavior and consistency of classification methods based on convex risk minimization. Ann Stat 32(1):56–85