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Neural Networks Approach

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Part of the Advances in Industrial Control book series (AIC)

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Neural Network Hide Layer Radial Basis Function Network Probabilistic Neural Network Neural Network Approach 
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References

  1. [1]
    Aizerman MA, Braverman EM, and Rozenoer LI (1964) Theoretical foundation of potential function method in pattern recognition. Automation and Remote Control 25: 917–936.Google Scholar
  2. [2]
    Akaike H (1970) Statistical predictor identification, Annals of the Institute of Statistical Maths., 22: 202–217.Google Scholar
  3. [3]
    Almeida LB (1987) A learning rule for asynchronous perceptrons with feedback in a combinatorial Environment. IEEE 1st International Conf. on Neural Networks, San Diego, CA II:609–618.Google Scholar
  4. [4]
    Amari S and Maginu K (1988) Statistical neurodynamics of associative memory, Neural Networks 1: 63–73.CrossRefGoogle Scholar
  5. [5]
    Anders U and Korn O (1999) Model selection in neural networks. Neural Networks 12: 309–323.CrossRefGoogle Scholar
  6. [6]
    Bashkirov OA, Braverman EM, and Muchnik IB (1964) Potential function algorithms for pattern recognition learning machines. Automation and Remote Control 25:692–695.Google Scholar
  7. [7]
    Bates JM and Granger CWJ (1969) The combination of forecasts, Operation Research Quart. 20: 451–461.Google Scholar
  8. [8]
    Baum EB and Haussler D (1989) What Size Net Gives Valid Generalisation? Neural Computation 1:151–160.Google Scholar
  9. [9]
    Bethea RM and Rhinehard RR (1991) Applied Engineering Statistics. Marcel Dekker, New York.zbMATHGoogle Scholar
  10. [10]
    Block HD (1962) The Perceptron: a model of brain functioning. Review of Modern Physics, 34:123–135.MathSciNetzbMATHGoogle Scholar
  11. [11]
    Broomhead DS and Lowe D (1988) Multivariable functional interpolation and adaptive networks. Complex Systems 2: 321–355.MathSciNetzbMATHGoogle Scholar
  12. [12]
    Butine WL and Weigend AS (1994) Computing Second Derivatives in Feedforward Networks: A Review. IEEE Trans. on Neural Networks 3: 480–488.Google Scholar
  13. [13]
    Chakraborty K, Mehrotra K, Mohan ChK, Rankas (1992) Forecasting the behavior of Multivariate Time Series Using Neural Networks. Neural Networks 5: 961–970.CrossRefGoogle Scholar
  14. [14]
    Cichocki A and Unbehauen R (1993) Neural Networks for Optimization and Signal Processing. Wiley, Chichester, West Sussex, UK.zbMATHGoogle Scholar
  15. [15]
    Cohen MA and Grossberg S (1983) Absolute Stability of global pattern formation and parallel memory storage by competitive neural networks. IEEE Trans. on Systems, Man, and Cybernetics 13: 815–826.MathSciNetzbMATHGoogle Scholar
  16. [16]
    Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Mathematical Control Signals Systems 2:303–314.MathSciNetzbMATHGoogle Scholar
  17. [17]
    Denton JW (1995) How good are neural networks for causal forecasting? J. of Business Forecasting 14(2):17–20.MathSciNetGoogle Scholar
  18. [18]
    Elman JL (1990) Finding structure in time. Cognitive Science 14: 179–211.CrossRefGoogle Scholar
  19. [19]
    Fogel DB (1991) An Information Criterion for Optimal Neural Network Selection. IEEE Trans. On Neural Networks 2:490–499.CrossRefGoogle Scholar
  20. [20]
    Forster WR, Collopy F, Ungar LH (1992) Neural network forecasting of short, noisy time series. Computers and Chemical Engineering16(2): 293–297.Google Scholar
  21. [21]
    German SE, Bienenstock, and Doursat R (1992) Neural networks and the bias/variance dilemma. Neural Computation 1: 1–58.Google Scholar
  22. [22]
    Girosi F and Poggio T (1989) Representation Properties of Networks: Kolmogorov’s Theorem is Irrelevant. Neural Computation 1: 465–469.Google Scholar
  23. [23]
    Girosi F and Poggio T (1990) Networks and the best approximation properties. Biological Cybernetics:169–176.Google Scholar
  24. [24]
    Gorr WL, Nagin D, Szczypula J (1994) Comparative study of artificial neural network and statistical models predicting student grade point averages. Intl. J. of Forecasting 10: 17–34.Google Scholar
  25. [25]
    Grossberg S (1988) Competitive Learning: From interactive activation to adaptive resonance, Neural Networks and Neural Intelligence, Grossberg S. (Eds.), MIT Press, Cambridge, MA.Google Scholar
  26. [26]
    Hagan MT and Menhaj MB (1994) Training feedforward networks with the Marquardt algorithm, IEEE Trans. on Neural Networks, vol. 5(6): 989–993.CrossRefGoogle Scholar
  27. [27]
    Hann TH and Steurer E. (1996) Much ado about nothing? Exchange rate forecasting: Neural networks vs. linear using monthly and weekly data. Neurocomputing 10: 323–339.CrossRefzbMATHGoogle Scholar
  28. [28]
    Hansen IK and Rasmussen CE (1994) Pruning from adaptive regularization. Neural Computation 6: 1223–1232.zbMATHGoogle Scholar
  29. [29]
    Harald PG and Kamastra M (1997) Evolving artificial neural networks to combine the financial forecasts, IEEE Trans. on Evolutionary Computation, vol. 1(1): 40–51.Google Scholar
  30. [30]
    Hassibi B, Stork DG, and Wolff GJ (1992) Optimal brain surgeon and general network pruning. IEEE Intl Conf on Neural Networks, San Francisco 1:293–299.Google Scholar
  31. [31]
    Haykin S (1994) Neural Networks: a comprehensive foundation. McMillan, USAGoogle Scholar
  32. [32]
    Hebb DO (1949) The organisation of behaviour. Wiley, New York.Google Scholar
  33. [33]
    Hecht-Nielsen R (1987a) Counterpropagation Networks. Applied Optics 26(23): 4979–4984.Google Scholar
  34. [34]
    Hecht-Nielsen R (1987b) Kolmogorov’s Mapping Neural Network Existence Theorem, IEEE Conf. On Neural Networks; San Diego, CA. III: 11–14.Google Scholar
  35. [35]
    Hecht-Nielsen R (1988) Application of counterpropagation networks, Neural Networks 1: 131–139.CrossRefGoogle Scholar
  36. [36]
    Hertz J, Krogh A, and Palmer RG (1991) Introduction to theory of neural computation, Addison-Wesley, Reading, MA.Google Scholar
  37. [37]
    Hill T, O’Connor M, Remus W. (1996) Neural network models for time series Models forecasts. Management Sciences 42(7): 1082–1092.zbMATHCrossRefGoogle Scholar
  38. [38]
    Hinton GE (1989) Connectionist learning procedures, Artificial Intelligence, 40: 185–243.CrossRefGoogle Scholar
  39. [39]
    Hopfield JJ (1982) Neural Networks and physical systems with emergent collective computational abilities. Proc. of the Nat. Acad. of Sciences, USA, 79: 2554–2558. Neural Networks Approach 139MathSciNetGoogle Scholar
  40. [40]
    Hopfield JJ (1984) Neurons with graded response have collective computational properties like those of two-state neurons. Proc. of the Nat. Acad. of Sciences, USA 81: 3088–3092.Google Scholar
  41. [41]
    Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are Universal approximators. Neural Networks 2(5): 359–366.CrossRefGoogle Scholar
  42. [42]
    Hu MJC (1964) Application of the ADALINE system to weather forecasting. Master Thesis, Technical Report 6775-1, Stanford El. Lab., Stanford, CA.Google Scholar
  43. [43]
    Ishikawa M. and Moriyama T (1996) Prediction of time series by a structural learning of neural networks.Fuzzy Sets and Systems 82: 167–176.CrossRefGoogle Scholar
  44. [44]
    Iyer MS and Rhinehart RR (2000) A novel methodGoogle Scholar
  45. [45]
    Iyer MS and Rhinehart RR (2000) A Novel Method To Stop Neural Network Training. 2000 American Control Conference, paper WM17-3Google Scholar
  46. [46]
    Johanson EM, Dowla EU, and Goodman DM (1990) Backpropagation learning for multi-layer feedforward neural networks using the conjugate gradient method, Report UCRL-JC-104850, Lawrence Livermore National Laboratory, CA.Google Scholar
  47. [47]
    Jollife IT (1986) Principal Components Analysis. Springer-Verlag.Google Scholar
  48. [48]
    Jordan M (1986) Attractor dynamics and parallelism in a connectionist sequential machine. Proc. of the Eight Annual Conference on Cognitive Science Society:532–546.Google Scholar
  49. [49]
    Karnin ED (1990) A simple procedure for Pruning back-propagation trained neural networks. IEEE Trans on Neural Networks 2: 188–197.Google Scholar
  50. [50]
    Khorasani K and Weng W (1994) Structure Adaptation in Feedforward Neural Networks. Proc. IEEE Internat. Conf. on Neural Networks, III: 1403–1408.Google Scholar
  51. [51]
    Klimasauskas CC (1991) Applying Neural Networks. Part 3: Training a Neural Network. PC-AI, May/June: 20–24. BGoogle Scholar
  52. [52]
    Kohonen T (1989) Self-Organisation and Associative Memory. 3rd Edition, Springer, Berlin, NY.Google Scholar
  53. [53]
    Kröse B and Smagt P (1996) An introduction to neural networks, The University of Amsterdam, Eighth edition, November, http://www.fwi.uva.nl/research/neuro.Google Scholar
  54. [54]
    Kubat M (1998) Decision trees can initialise radial-basis-function networks. IEEE Trans. on Neural Networks. 9: 813–821.CrossRefGoogle Scholar
  55. [55]
    Kurita T (1990) A method to determine the number of hidden units of three-layered neural networks by information criteria, Trans. of Inst. of Electronics, Information and Commun. Engineers, J73-D-II-11: 1872–1878 (in Japanese)Google Scholar
  56. [56]
    Lapedes A and Farber R (1988) Nonlinear signal processing using neural networks: Prediction and system modelling. Technical Report LA-UR-87-2662, Los Alamos National Laboratory, Los Alamos, NM.Google Scholar
  57. [57]
    Le Cun Y, Denker JS, and Solla SA (1990) Optimal Brain Damage. In: Touretzky S (Ed.). Advances in Neural Information Processing Systems 2, San Mateo, CA, Morgan Kaufman.Google Scholar
  58. [58]
    Levin AU, Leen TK, and Moody JE (1994) Fast pruning using principle components, In: Advances in Neural Information Processing Systems 6, Covan JD, Tesauro G and Alspector J, Editors: 35–42, Morgan Kaufman Publi. Inc., San Mateo, CA.Google Scholar
  59. [59]
    Lippmann RP (1987) An introduction to computing with neural nets. IEEE ASSP Magazine (April): 4–22Google Scholar
  60. [60]
    Mahmoud E (1984) Accuracy in forecasting: A survey. J. of Forecasting 3:139–159.Google Scholar
  61. [61]
    McClelland JL and Rumelhart DE (1988) Exploration in Parallel Distributed Processing. Cambridge, MA, MIT Press.Google Scholar
  62. [62]
    McCulloch WS, Pitts W, (1943) A logical Calculus of the ideas Immanent in nervous activity. Bulletin of Mathematical Biophysics 5:115–133.MathSciNetzbMATHGoogle Scholar
  63. [63]
    McNees SK (1985) Which forecast should you use. New England Economic Review, July/August: 36–42.Google Scholar
  64. [64]
    Minsky ML and Papert S, (1969) Perceptrons. MIT Press, Cambridge MA.zbMATHGoogle Scholar
  65. [65]
    Moody JE (1991) Note on Generalization, Regularization and Architecture Selection in Nonlinear Systems, Proc. of the IEEE-SP Workshop: 1–10.Google Scholar
  66. [66]
    Moody JE and Darken CJ (1989) Fast learning in networks of locally-tuned processing units. Neural Computation 1: 281–294.Google Scholar
  67. [67]
    Morozov VA (1984) Methods for Solving Incorrectly Posed Problems. Springer-Verlag, Berlin.Google Scholar
  68. [68]
    Mozer MC and Smolensky P (1990) Skeletonization: A technique for trimming the fat from a network via relevance assessment. In: Advances in Neural Information Processing 1, Touretzky DS (Ed.): 107–115.Google Scholar
  69. [69]
    Murata N, Yoshizawa S, and Amari S (1994) Network Information criterion-Determining the number of Hidden Units for an Artificial Neural model. IEEE Trans. On Neural Networks 6: 865–871.Google Scholar
  70. [70]
    Natarajan S and Rhinehart RR (1997) Automated Stopping Criteria For Neural Network Training. Proc. of the 1997 American Control Conf., paper #TP09-4.Google Scholar
  71. [71]
    Nelson M, Hill T, O’Connor M (1994) Can a neural network be applied to time series forecasting and learn seasonal patterns: An empirical investigation. Proc. of the 20th Annual Hawaii Intl. Conf on System Sciences: 649–655.Google Scholar
  72. [72]
    Oja E (1982) A simplified neuron model as a principal component analyzer. Journal of Mathematical Biology 15: 267–273.CrossRefMathSciNetzbMATHGoogle Scholar
  73. [73]
    Palit AK and Popovic D (2000) Nonlinear combination of forecasts using artificial neural network, fuzzy logic and neuro-fuzzy approaches, FUZZ-IEEE, 2: 566–571.Google Scholar
  74. [74]
    Pineda FJ (1987) Generalisation of back-propagation to recurrent neural networks. Physical Review Letters 59: 2229–2232.CrossRefMathSciNetGoogle Scholar
  75. [75]
    Poggio T and Girosi F (1990) Networks for Approximation and Learning. Proc. IEEE 78:1481–1497.CrossRefGoogle Scholar
  76. [76]
    Powel MID (1988) Radial basis function approximation to polynomials, Numerical Analysis Proceedings, Dundee, U.K.: 223–241.Google Scholar
  77. [77]
    Prechelt L (1998) Early Stopping-but when? In: Orr GB and Moeller K-R (Eds.), Neural Networks: Tricks of the Trade. Springer, Berlin: 55–69.Google Scholar
  78. [78]
    Reed R (1993) Pruning Algorithms-A Survey. IEEE Trans. on Neural Networks 4: 740–747.CrossRefGoogle Scholar
  79. [79]
    Rosenblatt F, (1958) The Perceptron: A probabilistic model for information storage and organisation of the brain. Psych. Review 65: 386–408.MathSciNetGoogle Scholar
  80. [80]
    Rumelhart DE and McClelland (1986) Parallel Distributed Processing: Explorations in the Microstructure of Cognition MIT Press, Cambridge, MA.Google Scholar
  81. [81]
    Rumelhart DE, Hinton GE, and Williams RJ (1986) Learning internal representation by back-propagation errors. In: Rumelhart DE, McClelland JL, the PDP Research Group(Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition. MIT Press, MA.Google Scholar
  82. [82]
    Sastry PS, Santharam G, and Unikrishnan KP (1994) Memory neuron networks for identification and control of dynamic systems. IEEE Trans. on NeuralGoogle Scholar
  83. [83]
    Schmidhuber J (1989) Accelerated learning in backpropagation net, In: Connectionism in Perspective, Elsevier, North Holland, Amsterdam, pp. 439–445.Google Scholar
  84. [84]
    Sharda R and Patil RB (1990) Neural Networks as Forecasting Experts: An Empirical Test, Proc. of the IJCNN Meeting, Washington: 491–494.Google Scholar
  85. [85]
    Shi S and Liu B (1993) Nonlinear combination of forecasts with neural networks. Proc. of Intl. Joint Conf. on Neural Networks’ 93 (IJCNN’ 93), Nagoya, Japan, 952–962.Google Scholar
  86. [86]
    Silva FM and Almeida LB (1990) Speeding-up backpropagation, In: Advances of Neural Computers, Eds. Eckmiller R, Elsevier Science Publish. BV., North Holland, pp. 151–158.Google Scholar
  87. [87]
    Specht DF (1988) Probabilistic neural networks for classification, or associative memory, Proc. of IEEE Intern. Conf. on Neural Networks, San Diego, 1: 525–532.Google Scholar
  88. [88]
    Specht DF (1990) Probabilistic neural networks and the polynomial ADALINE as complementary techniques for classifications. IEEE Trans. on Neural Networks, 1: 111–121.CrossRefGoogle Scholar
  89. [89]
    Sprecher DA (1965) On the Structure of Continuous Functions of Several Variables. Trans. Amer. Math. Soc. 115:340–355.MathSciNetzbMATHGoogle Scholar
  90. [90]
    Srinivasan D, Liew AC, Chang CS (1994) A neural network short-term load forecaster. Electric Power Systems Research 28: 227–234.CrossRefGoogle Scholar
  91. [91]
    Stahlberger A and Riedmuller M (1996) Fast network pruning and feature extraction using the Unit-OBS algorithm. Advances in Neural Information Processing systems (NIPS’96), Denver.Google Scholar
  92. [92]
    Stone M (1977) An asymptotic equivalence of choice of model by cross-validation and Akaike’s criterion cross validation. J. of the Royal Statistical Soc. B36:44–47.Google Scholar
  93. [93]
    Sue CT, Tong LI, and Leou CM (1997) Combination of time series and neural network for reliability forecasting modelling. J. Chin. Inst. Ind. Eng. 14(4): 419–429.Google Scholar
  94. [94]
    Tang Z, Almeida de Ch, and Fishwick, PA (1991) Time series forecasting using neural networks vs. Box-Jenkins methodology. Simulation 57(5): 303–310.Google Scholar
  95. [95]
    Tiao GC and Tsay RS (1989) Model specification in multivariate time series. J. of the Royal Statistical Society B 51: 157–213.MathSciNetzbMATHGoogle Scholar
  96. [96]
    Tikhonov AN (1963) On solving incorrectly posed problems and methods of regularisation. Docklady Akademii Nauk USSR 151: 501–504.zbMATHGoogle Scholar
  97. [97]
    Tseng F-M, Yu H-Ch, and Tzeng G-H (2002) Combining neural network model with seasonal time series ARIMA model. Technological Forecasting.Google Scholar
  98. [98]
    Vapnik V (1995) The Nature of Statistical Learning Theory, Springer-Verlag, NY.zbMATHGoogle Scholar
  99. [99]
    Villiers de J and Bernard E (1992) Backpropagation Neural Nets with one and Two Hidden Layers. IEEE Trans. On Neural Networks: 136–141.Google Scholar
  100. [100]
    Vogl TP, Mangis JK, Rigler AK, Zink WT and Allcon DL (1988) Accelerating the convergence of backpropagation method, Biological Cybernetics, vol. 59: 257–263.CrossRefGoogle Scholar
  101. [101]
    Voort VD, Dougherty M, and Watson M. (1996) Combining Kohonen Maps with ARIMA time series models to forecast traffic flow. Transp. Res. Circ. (Emerg. Technol.) 4C(5): 307–318.Google Scholar
  102. [102]
    Wedding II DK and Cios KJ (1996) Time series forecasting by combining RBF networks certainty factors, and the Box-Jenkins model. Neurocomputing 10: 149–168.CrossRefzbMATHGoogle Scholar
  103. [103]
    Weigend AS, Rumelhart DE, and Huberman BA (1991) Generalisation by weightelimination with application to forecasting. Adv. In Neural Information Processing Systems, Morgan Kaufmann, San Mateo, CA 3: 875–882.Google Scholar
  104. [104]
    Werbos P (1990) Backpropagation through time what it does and how to do it, Proc. of IEEE, 78(10):1550–1560.Google Scholar
  105. [105]
    Werbos PJ (1974) Beyond Regression: New Tool for Prediction and analysis in the Behavioural sciences. Ph.D. Thesis, Harvard University, Cambridge, MA.Google Scholar
  106. [106]
    Werbos PJ (1989) Backpropagation and neural control: A review and prospectus. Internat. Joint Conf. of Neural Networks, Washington, 1: 209–216.Google Scholar
  107. [107]
    Widrow B and Hoff ME (1960) Adaptive Switching Circuits. In: Anderson J and Rosenfeld E. (eds.) Neurocomputing. MIT Press, Cambridge, MA, 126–134.Google Scholar
  108. [108]
    Williams RJ and Zipser D (1989) A learning algorithm for continually running fully recurrent neural networks. Neural Computation 1: 270–280.Google Scholar
  109. [109]
    Winkler R and Makridakis S (1983) The combination of forecasts, Journal of the Royal Statistical Society, Series A: 150–157.Google Scholar
  110. [110]
    Yang Y (2000) Combining different procedures for adaptive regression, J. of Multivar. Analysis, 74: 135–161.zbMATHGoogle Scholar
  111. [111]
    Yu X-H, Chen G-A, and Cheng S-X (1995) Dynamic Learning Rate Optimization of the Backpropagation Algorithm. IEEE Trans. on Neural Networks 3: 669–677.Google Scholar
  112. [112]
    Zhang PG (2003) Time series forecasting using a hybrid ARIMA and neural network models. Neurocomputing 50:159–175.zbMATHGoogle Scholar
  113. [113]
    Zhou S, Popovic D, and Schulz-Ekloff G (1991) An Improved Learning Law for Backpropagation Networks. IEEE Int. Conf. on Neural Networks, San Francisco: 573–579.Google Scholar

Selected Reading

  1. [114]
    Anderson JA (1972) A Simple Neural Network Generating an Interactive Memory, Mathematical Biosciences 14: 197–220.CrossRefzbMATHGoogle Scholar
  2. [115]
    Cybenko G (1988) Continuous valued neural networks with two hidden layers are sufficient. Technical Report, Taft University.Google Scholar
  3. [116]
    Kohonen T (1972) Correlation Matrix Memories. IEEE Transactions on Computers 21: 353–359.zbMATHCrossRefGoogle Scholar
  4. [117]
    Kolmogorov AI (1957) On Representation of Continuous Function of Many Variables by Superposition of Continuous Functions of One Variable and Addition. Dokl. Akad. Nauk USSR 114:953–956.MathSciNetzbMATHGoogle Scholar
  5. [118]
    Kurkova V (1991) Kolmogorov’s Theorem is Relevant, Neural Computation, 3: 617–622.CrossRefGoogle Scholar
  6. [119]
    Kurkova V (1992) Kolmogorov’s Theorem and Multilayer Neural Networks, Neural Networks 5: 501–506.CrossRefGoogle Scholar
  7. [120]
    Moody JE (1992) The Effective Number of Parameters: An Analysis of Generalization and Regularisation in Nonlinear learning Systems. In: Advances in Neural Information Processing 4 (Moody JE, Hanson SJ, and Lippmann RP (Eds.), Morgan Kaufman Publ., San Mateo, CA.Google Scholar
  8. [121]
    Schwenkler F, Kestler H, Palm G (2001) Three learning phases for radial-basisfunction networks. Neural Networks 14: 439–458.Google Scholar
  9. [122]
    Schwenkler F, Kestler H, Palm G, and Höher M (1994) Similarities of LVQ and RBF learning, Proc. IEEE International Conference SMC: 646–651.Google Scholar
  10. [123]
    Xiaosong D, Popovic D, and Schulz-Ekloff G (1995) Oscillation-Resisting in the Learning of Backpropagation Neural Networks. 3rd IFAC/IFIP Workshop on Algorithms and Architectures for Real-Time Control, 31 May-2 June, Ostend, Belgium.Google Scholar

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