Skip to main content
SpringerLink
Log in

A comprehensive survey on functional link neural networks and an adaptive PSO–BP learning for CFLNN

  • KES 2008
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Functional link neural network (FLNN) is a class of higher order neural networks (HONs) and have gained extensive popularity in recent years. FLNN have been successfully used in many applications such as system identification, channel equalization, short-term electric-load forecasting, and some of the tasks of data mining. The goals of this paper are to: (1) provide readers who are novice to this area with a basis of understanding FLNN and a comprehensive survey, while offering specialists an updated picture of the depth and breadth of the theory and applications; (2) present a new hybrid learning scheme for Chebyshev functional link neural network (CFLNN); and (3) suggest possible remedies and guidelines for practical applications in data mining. We then validate the proposed learning scheme for CFLNN in classification by an extensive simulation study. Comprehensive performance comparisons with a number of existing methods are presented.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Haykin S (1999) Neural networks—a comprehensive foundation. Prentice Hall, Englewood Cliffs

    MATH  Google Scholar 

  2. Zhang GP (207) Avoiding pitfalls in neural network research. IEEE Trans Syst Man Cybern Part C Appl Rev 37(1):3–16

    Google Scholar 

  3. Anders U, Korn O (1999) Model selection in neural networks. Neural Netw 12:309–323

    Google Scholar 

  4. Benitez JM, Castro JL, Requena I (1997) Are artificial neural networks black boxes? IEEE Trans Neural Netw 8(5):1156–1164

    Google Scholar 

  5. Cheng B, Titterington D (1994) Neural networks: a review from a statistical perspective. Stat Sci 9(1):2–54

    MATH  MathSciNet  Google Scholar 

  6. McCulloch W, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 7:115–133

    MathSciNet  Google Scholar 

  7. Giles CL, Maxwell T (1987) Learning invariance, and generalization in a higher order neural networks. Appl Opt 26(23):4972–4978

    Google Scholar 

  8. Belli MR, Conti M, Crippa P, Turchetti C (1999) Artificial neural networks as approximators of stochastic processes. Neural Netw 12(4–5):647–658

    Google Scholar 

  9. Castro JL, Mantas CJ, Benitez JM (2000) Neural networks with a continuous squashing function in the output are universal approximators. Neural Netw 13(6):561–563

    Google Scholar 

  10. Funahashi K (1989) On the approximate realization of continuous mappings by neural networks. Neural Netw 2:183–192

    Google Scholar 

  11. Andrews R, Diederich J, Tickle AB (1995) Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowl Based Syst 8(6):373–389

    Google Scholar 

  12. Castro JL, Requena I, Benitez JM (2002) Interpretation of artificial neural networks by means of fuzzy rules. IEEE Trans Neural Netw 13(1):101–116

    Google Scholar 

  13. Setiono R, Leow WK, Zurada J (2002) Extraction of rules from artificial neural networks for nonlinear regression. IEEE Trans Neural Network 13(3):564–577

    Google Scholar 

  14. Setiono R, Thong JYL (2004) An approach to generate rules from neural networks for regression problems. Eur J Oper Res 155:239–250

    MATH  Google Scholar 

  15. Gish H (1990) A probabilistic approach to the understanding and training of neural network classifiers. In: Proc IEEE international conference acoustic, speech signal process 3:1361–1364

  16. Zhang GP (2000) Neural networks for classification: a survey. IEEE Trans Syst Man Cybern C 30(4):451–462

    Google Scholar 

  17. Michie D, Spiegelhalter DJ, Taylor CC (1994) Machine learning, neural and statistical classification. Ellis Horwood, New York

    MATH  Google Scholar 

  18. Adya M, Collopy F (1998) How effective are neural networks at forecasting and prediction? A review and evaluation. J Forecast 17:481–495

    Google Scholar 

  19. Callen JL, Kwan CCY, Yip PCY, Yuan Y (1996) Neural network forecasting of quarterly accounting earnings. Int J Forecast 12:475–482

    Google Scholar 

  20. Church KB, Curram SP (1996) Forecasting comsumers’ expenditure: a comparison between econometric and neural network models. Int J Forecast 12:255–267

    Google Scholar 

  21. Connor JT, Martin RD, Atlas LE (1994) Recurrent neural networks and robust time series prediction. IEEE Trans Neural Netw 51(2):240–254

    Google Scholar 

  22. Cottrell M, Girard B, Girard Y, Mangeas M, Muller C (1995) Neural modeling for time series: a statistical stepwise method for weight elimination. IEEE Trans Neural Netw 6(6):1355–1364

    Google Scholar 

  23. Faraway JJ, Chatfield C (1998) Time series forecasting with neural networks: a comparative study using the airline data. Appl Stat 47:231–250

    Google Scholar 

  24. Fletcher D, Goss E (1993) Forecasting with neural networks—an application using bankruptcy data. Inf Manag 24:159–167

    Google Scholar 

  25. Gorr WL (1994) Research prospective on neural network forecasting. Int J Forecast 10:1–4

    Google Scholar 

  26. Hippert HS, Pedreira CE, Souza RC (2001) Neural networks for short-term forecasting: a review and evaluation. IEEE Trans Power Syst 16(1):44–55

    Google Scholar 

  27. Hu MY, Zhang GP, Jiang CX, Patuwo BE (1999) A cross-validation analysis of neural network out-of-sample performance in exchange rate forecasting. Decis Sci 30:197–216

    Google Scholar 

  28. Kaastra I, Boyd M (1996) Designing a neural network for forecasting financial and economic time series. Neurocomputing 10:215–236

    Google Scholar 

  29. Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications. Environ Model Softw 15:101–124

    Google Scholar 

  30. Park YR, Murray TJ, Chen C (1996) Predicting sun spots using a layered perceptron neural network. IEEE Trans Neural Netw 7(2):501–505

    Google Scholar 

  31. Qi M, Zhang GP (2001) An investigation of model selection criteria for neural network time series forecasting. Eur J Oper Res 132:666–680

    MATH  Google Scholar 

  32. Kracha KA, Wagner U (1999) Applications of artificial neural networks in management science: a survey. J Retail Consum Serv 6:185–203

    Google Scholar 

  33. Wong BK, Bodnovich TA, Selvi Y (1997) Neural network applications in business: a review and analysis of the literature (1988–1995). Decis Support Syst 19:301–320

    Google Scholar 

  34. Flood I, Kartam N (1994) Neural network in civil engineering-I: principles and understanding. J Comput Civil Eng 8(2):131–148

    Google Scholar 

  35. Lu CN, Wu HT, Vemuri S (1993) Neural network based short term load forecasting. IEEE Trans Power Syst 8(1):336–342

    Google Scholar 

  36. Lisboa PJG (2002) A review of evidence of health benefit from artificial neural networks in medical intervention. Neural Netw 15:11–39

    Google Scholar 

  37. Protney LG, Watkins MP (2000) Foundations of clinical research: applications to practice. Prentice-Hall, Princeton

    Google Scholar 

  38. Hosseini-Nezhad SM, Yamashita TS, Bielefeld RA, Krug SE, Pao YH (1995) A neural network approach for the determination of interhospital transport mode. Comput Biomed Res 28(4):319–334

    Google Scholar 

  39. Tawfik H, Liatsis P (1997) Prediction of non-linear time series using higher order neural networks. In: Proceeding IWSSIP1997 conference, Poznan, Poland

  40. Kaita T, Tomita S, Yamanaka J (2002) On a higher order neural network for distortion invariant pattern recognition. Pattern Recognit Lett 23:977–984

    MATH  Google Scholar 

  41. Ghosh J, Shin Y (1992) Efficient higher-order neural networks for classification and function approximation. Int J Neural Syst 3:323–350

    Google Scholar 

  42. Minsky M, Papert S (1969) Perceptrons. The MIT Press

  43. Widrow B, Hoff ME (1960) Adaptive switching circuits. IRE WESCON Convention Record, pp 96–104

  44. Widrow B, Lehr M (1990) 30 years of adaptive neural networks: perceptron, madaline, and back-propagation. Proc IEEE 78(9):1415–1442

    Google Scholar 

  45. Cover TM (1965) Geometrical and statistical properites of systems of linear inequalities with applications in pattern recognition. IEEE Trans Electron Comput 14:326–334

    MATH  Google Scholar 

  46. Hornik K et al (1989) Multi-layer feed-forward networks are universal approximators. Neural Netw 2:359–366

    Google Scholar 

  47. Giles CL, Maxwell T (1987) Learning, invariance and generalization in higher-order neural networks. Appl Opt 26(23):4972-4978

    Google Scholar 

  48. Pao YH (1989) Adaptive pattern recognition and neural network. Addison-Wesley, Reading, MA

    Google Scholar 

  49. Venkatesh SS, Baldi P (1991) Programmed interactions in higher order neural networks: maximal capacity. J Complex 7:316–337

    MATH  MathSciNet  Google Scholar 

  50. Antyomov E, Pecht OY (2005) Modified higher order neural network for invariant pattern recognition. Pattern Recognit Lett 26:843–851

    Google Scholar 

  51. Misra BB, Dehuri S (2007) Functional link neural network for classification task in data mining. J Comput Sci 3(12):948–955

    Google Scholar 

  52. Mirea L, Marcu T (2002) System identification using functional link neural networks with dynamic structure. 15th Triennial World Congress, Barcelona, Spain

  53. Cass R, Radl B (1996) Adaptive process optimization using functional link networks and evolutionary algorithms. Control Eng Pract 4(11):1579–1584

    Google Scholar 

  54. Pao Y-H, Philips SM (1995) The functional link net learning optimal control. Neurocomputing 9:149–164

    MATH  Google Scholar 

  55. Shin Y, Ghosh J (1995) Ridge polynomial networks. IEEE Trans Neural Netw 6(2):610–622

    Google Scholar 

  56. Shin Y, Ghosh J (1992) Approximation of multivariate functions using ridge polynomial networks. In: Proceedings of international joint conference on neural networks II, pp 380–385

  57. Voutriaridis C, Boutalis YS, Mertzios G (2003) Ridge polynomial networks in pattern recognition. 4th EURASIP conference focused on video/image processing and multimedia communications, Croatia, pp 519–524

  58. Shin Y, Ghosh J (1991) The pi-sigma networks: an efficient higher order neural network for pattern classification and function approximation. In: Proceedings of international joint conference on neural networks I, pp 13–18

  59. Shin Y, Ghosh J (1992) Computationally efficient invariant pattern recognition with higher order pi-sigma networks. The University of Texas at Austin, Tech. Report

  60. Shin Y, Ghosh J (1991) Realization of boolean functions using binary pi-sigma networks. In: Proceedings of conference on artificial neural networks in engineering, St. Louis

  61. Hussain AJ, Liatsis P (2002) Recurrent pi-sigma networks for DPCM image coding. Neurocomputing 55:363–382

    Google Scholar 

  62. Xiong Y et al (2007) Training pi-sigma network by on-line gradient algorithm with penalty for small weight update. Neural Comput 19:3356–3368

    MATH  Google Scholar 

  63. Iyoda EM et al (2007) Image compression and reconstruction using pi t -sigma neural networks. Soft Comput 11:53–61

    MATH  Google Scholar 

  64. Hussain AJ et al (2008) Physical time series prediction using recurrent pi-sigma neural networks. Int J Artif Intell Soft Comput 1(1):130–145

    MathSciNet  Google Scholar 

  65. Nie Y, Deng W (2008) A hybrid genetic learning algorithm for pi-sigma neural network and the analysis of its convergence. In: Proceedings of fourth international conference on natural computation, IEEE Press, pp 19–23

  66. Zhu Q, Cai Y, Liu L (1999) A global learning algorithm for a RBF network. Neural Netw 12:527–540

    Google Scholar 

  67. Li M, Tian J, Chen F (2008) Imrpoving multiclass pattern recognition with a co-evolutionary RBFNN. Pattern Recognit Lett 29:392–406

    Google Scholar 

  68. Dybowski R (1998) Classification of incomplete feature vectors by radial basis function networks. Pattern Recognit Lett 19:1257–1264

    MATH  Google Scholar 

  69. Leonardis A, Bischof H (1998) An efficient MDL based construction of RBF networks. Neural Netw 11:963–973

    Google Scholar 

  70. Chen S, Wu Y, Luk BL (1999) Combined genetic algorithm optimization and regularized orthogonal least square learning for radial basis function networks. IEEE Tran Neural Netw 10(5):1239–1243

    Google Scholar 

  71. Lee YC, Doolen G, Chen HH, Sun GZ, Maxwell T, Lee HY, Giles CL (1986) Machine learning using a higher order correlation network. Physica 22D:276–306

    MathSciNet  Google Scholar 

  72. Peretto P, Niez JJ (1986) Long-term memory storage capacity of multiconnected neural networks. Biol Cybern 54:5363

    Google Scholar 

  73. Psaltis D, Park CH (1986) Nonlinear discriminant functions and associative memories. In: Denker JS (ed) Neural networks for computing. Amererican Institute of Physics, New York, pp 370–375

  74. Gardner E (1987) Multiconnected neural-network models. J Phys A Math Gen 20:3453–3464

    Google Scholar 

  75. Abbott LF, Arian Y (1987) Storage capacity of generalized networks. Phys Rev A 36:5091–5094

    Google Scholar 

  76. Kamp Y, Hasler M (1990) Recursive neural networks for associative memory. Wiley, New York

    MATH  Google Scholar 

  77. Horn D, Usher M (1988) Capacities of multiconnected memory models. J Phys France 49:389–395

    Google Scholar 

  78. Guillermo V (1998) A distributed approach to neural network simulation program. Master thesis, The University of Texas at E1 Paso, TX

  79. Zurada JM (1992) Introduction to artificial neural system. West Publishing Company, St. Paul, MN

    Google Scholar 

  80. Beale R, Jackson T (1991) Neural computing: an introduction. Hilger, Philadelphia, PA

    Google Scholar 

  81. Haring B, Kok JN (1995) Finding functional links for neural networks by evolutionary computation. In: Van de Merckt T et al (eds) BENELEARN1995, proceedings of the fifth Belgian–Dutch conference on machine learning, Brussels, Belgium, pp 71–78

  82. Panagiotopoulos DA et al (1999) Planning with a functional neural network architecture. IEEE Trans Neural Netw 10(1):115–127

    Google Scholar 

  83. Patra JC et al (1999) Identification of non -linear dynamic systems using functional link artificial neural networks. IEEE IEEE Trans Syst Man Cyber Part B Cybern 29(2):254–262

    Google Scholar 

  84. Sierra A, Macias JA, Corbacho F (2001) Evolution of Functional Link Networks. IEEE Tranas Evol Comput 5(1):54–65

    Google Scholar 

  85. Marcu T, Koppen-Seliger B (2004) Dynamic functional link neural networks genetically evolved applied to system identification. In: Proceedings of ESANN’2004, Bruges (Belgium), pp 115–120

  86. Patra JC, Pal NR (1995) A functional link neural network for adaptive channel equalization. Signal Process 43:181–195

    MATH  Google Scholar 

  87. Zhao H, Zhang J (2008) Functional link neural network cascaded with Chebyshev orthogonal polynomial for non-linear channel equalization. signal Process 88:1946–1957

    MATH  Google Scholar 

  88. Haring et al (1997) Feature selection for neural networks through functional links found by evolutionary computation. In: Liu X et al (eds) Adavnces in intelligent data analysis (IDA-97). LNCS 1280:199–210

  89. Patra JC et al (2000) Modelling of an intelligent pressure sensor using functional link artificial neural networks. ISA Trans 39:15–27

    Google Scholar 

  90. Dehuri S et al (2008) Genetic feature selection for optimal functional link neural network in classification. In: Fyfe C et al (eds) IDEAL 2008, LNCS 5326:156–163

  91. Majhi B et al (2005) An improved scheme for digital watermarking using functional link artificial neural network. J Comput Sci 1(2):169–174

    Google Scholar 

  92. Patra JC et al (2008) Functional link neural networks-based intelligent sensors for Harsh Environments. Sens Transducers J 90:209–220

    Google Scholar 

  93. Dash PK et al (1999) A functional link neural network for short term electric load forecasting. J Intell Fuzzy Syst 7:209–221

    MathSciNet  Google Scholar 

  94. Krishnaiah D et al (2008) Application of ultrasonic waves coupled with functional link neural network for estimation of carrageenan concentration. Int J Phys Sci 3(4):90–96

    Google Scholar 

  95. Sing SN, Srivastava KN (2002) Degree of insecurity estimation in a power system using functional link neural network. ETEP 12(5):353–359

    Google Scholar 

  96. Abu-Mahfouz I-A (2005) A comparative study of three artificial neural networks for the detection and classification of gear faults. Int J Gen Syst 34(3):261–277

    MATH  MathSciNet  Google Scholar 

  97. Hu Y-C, Tseng F-M (2007) Functional-link net with fuzzy integral for bankruptcy prediction. Neurocomputing 70:2959–2968

    Google Scholar 

  98. Park GH, Pao YH (2000) Unconstrained word-based approach for off-line script recognition using density based random vector functional link net. Neurocomputing 31:45–65

    Google Scholar 

  99. Hu Y-C (2008) Functional link nets with genetic algorithm based learning for robust non-linear interval regression analysis. Neurocomputing. doi:10.1016/J.neucom.2008.07.002

  100. Chen CLP et al (1998) An incremental adaptive implementation of functional link processing for function approximation, time series prediction, and system identification. Neurocomputing 18:11–31

    Google Scholar 

  101. Weng W-D, Yen CT (2004) Reduced decision feed-back FLANN non-linear channel equaliser for digital communication systems. IEE Proc Commun 151(4):305–311

    Google Scholar 

  102. Hussain A et al (1997) A new adaptive functional link neural network based DFE for overcoming co-channel interference. IEEE IEEE Trans Commun 45(11):1358–1362

    Google Scholar 

  103. Patra JC et al (1999) Non-linear channel equalization for QAM signal constellation using artificial neural networks. IEEE Tranasactions on Systems, Man, Cybernetics-Part B: Cybernetics 29(2):262–271

    Google Scholar 

  104. Purwar S et al (2007) On-line system identification of complex systems using Chebyshev neural networks. Appl Soft Comput 7:364–372

    Google Scholar 

  105. Weng W-D et al (2007) A channel equalizer usi ng reduced decision feedback Chebyshev function link artificial neural networks. Inf Sci 177:2642–2654

    MATH  Google Scholar 

  106. Patra JC et al (2002) Non-linear dynamic system identification using Chebyshev functional link artificial neural networks. IEEE Trans Syst Man Cybern Part B Cybern 32(4):505–511

    MathSciNet  Google Scholar 

  107. Fogel DB (2000) Evolutionary computation: towards a new philosophy of machine intelligence. IEEE Press, New York

    Google Scholar 

  108. Pearson DW et al (eds) (1995) Artificial neural networks and genetic algorithms. Springer Verlag

  109. Suzuki J (1995) A Markov chain analysis on simple genetic algorithms. IEEE Trans Syst Man Cybern 25(4):6–659

    Google Scholar 

  110. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks. Pisacataway, NJ, pp 1942–9148

  111. Schaffer JD, Whitley D, Eshelman LJ (1992) Combinations of genetic algorithms and neural networks: a survey of the state of the art. In: Proceedings of international workshop on combinations of genetic algorithms and neural networks pp 1–37

  112. Davidor Y (1990) Epistasis variance: suitability of a representation to genetic algorithms. Complex Syst 4:368–383

    Google Scholar 

  113. Eshelman LJ, Schaffer JD (1993) Real coded genetic algorithms and interval schemata. In: Whitley LD (ed) Foundation of genetic algorithms. Morgan Kaufmann, San Mateo, pp 187–202

  114. Muhlenbein H, Schlierkamp-Voosen D (1993) Predictive models for the breeder genetic algorithm I. Continuous parameters optimization. Evol Comput 1(1):24–49

    Google Scholar 

  115. Schutte JF, Groenwold AA (2005) A study of global optimization using particle swarms. J Glob Optim 31(1):93–108

    MATH  MathSciNet  Google Scholar 

  116. Ali MM, Kaelo P (2008) Improved particle swarm algorithms for global optimization. Appl Math Comput 196:578–593

    MATH  MathSciNet  Google Scholar 

  117. Yu J, Wang S, Xi L (2008) Evolving artificial neural networks using an improved PSO and DPSO. Neurocomputing 71:1054–1060

    Google Scholar 

  118. Da Y, Ge XR (2005) An improved PSO-based ANN with simulated annealing technique. Neurocomput Lett 63:527–533

    Google Scholar 

  119. Blake CL, Merz CJ (1998) UCI repository of machine learning databases. http://www.ics.uci.edu/mlearn/MLRepository.html

  120. Pao Y-H, Phillips SM, Sobajic DJ (1992) Neural-net computing and intelligent control systems. Int J Control 56(2):263–289

    MATH  MathSciNet  Google Scholar 

  121. Hornik K (1991) Approximation capabilities of multilayer feed-forward networks. Neural Netw 4:251–257

    Google Scholar 

  122. Smith KA, Gupta JND (2002) Neural networks in business: techniques and applications. Idea Group, Hershey, PA

    Google Scholar 

  123. Lee TT, Jeng JT (1998) The Chebyshev polynomial based unified model neural networks for function approximations. IEEE Trans Syst Man Cybern Part B 28:925–935

    Google Scholar 

  124. Namatame A, Veda N (1992) Pattern classification with Chebyshev neural network. Int J Neural Netw 3:23–31

    Google Scholar 

  125. Klasser MS, Pao YH (1988) Characteristics of the functional link net: a higher order delta rule net. IEEE proceedings of 2nd annual international conference on neural networks, San Diago, CA

  126. Pao YH, Takefuji Y (1992) Functional link net computing: theory, system, architecture and functionalities. IEEE Comput, pp 76–79

  127. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Morgan Kaufmann, San Mateo

  128. Kennedy J, Eberhart RC (1999) The particle swarm: social adaptation in information processing systems. In: Corne D, Dorigo M, Glover F (eds) New ideas in optimization. McGraw–Hill, Cambridge, UK, pp 379–387

  129. Shi Y, Eberhart RC (1998) A modified particle swarm optimizer. In: Proceedings of the IEEE international conference on evolutionary computation. IEEE Press, Pisacataway, NJ, pp 69–73

  130. Shi Y, Eberhart RC (1998) Parameter selection in particle swarm optimization. Evolutionary Programming VII, LNCS, Springer, Berlin 1447:591–600

  131. Forie PC, Groenwold AA (2002) The particle swarm optimization algorithm in size and shape optimization. Struct Multidiscipl Optim 23(4):259–267

    Google Scholar 

  132. Clerc M, Kennedy J (2002) The particle swarm explosion, stability and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73

    Google Scholar 

  133. Zhang JR et al (2007) A hybrid particle swarm optimization-back-propagation algorithm for feed-forward neural network training. Appl Math Comput 185:1026–1037

    MATH  Google Scholar 

  134. Ratnaweera A, Halgamuge SK, Watson HC (2004) Self-organizing hierarchical particle swarm optimizer with time varying acceleration coefficients. IEEE Trans Evol Comput 8(3):240–255

    Google Scholar 

  135. Lippmann R (1987) An introduction to computing with neural networks. IEEE ASSP Mag 4:4–22

  136. Preshelt L (1994) Proben1-a set of neural network benchmark problems and benchmarking rules. Technical Report 21/94, Universitat Karlsruhe, Germany

  137. Ghosh A, Dehuri S, Ghosh S (2008) Multi-objective evolutionary algorithms for knowledge discovery from databases. Springer

  138. Kriegel H-P et al (2007) Future trends in data mining. Data Mining Knowl Discov 15(1):87–97

    MathSciNet  Google Scholar 

  139. Vellido A, Lisboa PJG, Vaughan J (1999) Neural networks in business: a survey of applications (1992–1998). Expert Syst Appl 17:51–70

    Google Scholar 

  140. Liatsis P, Hussain AJ (1999) Non-linear one dimensional DPCM image prediction using polynomial neural network. In: Proceedings of SPIE applications of artificial neural networks in image processing IV, San Jose, CA 3647:58–68

Download references

Acknowledgments

Authors would like to thank BK21 research program on Next Generation Mobile Software at Yonsei University, South Korea for their financial support. The authors greatly appreciate all the reviewers’ constructive comments that motivated them to think more and improve the presentation of this paper.

Author information

Authors and Affiliations

  1. Department of Information and Communication Technology, Fakir Mohan University, Vyasa Vihar, Balasore, 756019, Orissa, India

    Satchidananda Dehuri

  2. Soft Computing Laboratory, Department of Computer Science, Yonsei University, 262 Seongsanno, Seodaemun-gu, Seoul, 120-749, Korea

    Sung-Bae Cho

Authors
  1. Satchidananda Dehuri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sung-Bae Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Satchidananda Dehuri.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Dehuri, S., Cho, SB. A comprehensive survey on functional link neural networks and an adaptive PSO–BP learning for CFLNN. Neural Comput & Applic 19, 187–205 (2010). https://doi.org/10.1007/s00521-009-0288-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-009-0288-5

Keywords

Search

Navigation