Alternative Neural Network Training Methods

IEEE Expert-Intelligent Systems and their Applications

Volumn 10, Issue 3, 1995, Pages 16-22

  Porto, Vincent W ^a   Fogel, David B ^b   Fogel, Lawrence J ^b  

^a LOCKHEED MARTIN   (United States)

^b NATURAL SELECTION INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BACKPROPAGATION; LEARNING ALGORITHMS; LEARNING SYSTEMS; NATURAL SCIENCES COMPUTING; OPTIMIZATION; PERFORMANCE; PROBABILITY; SIMULATED ANNEALING; TOPOLOGY;

EVOLUTIONARY PROGRAMMING; NONLINEAR PROCESSING NODES; STOCHASTIC TRAINING METHODS; SUPERVISED LEARNING NETWORKS; UNSUPERVISED LEARNING NETWORKS;

NEURAL NETWORKS;

EID: 0029324927     PISSN: 08859000     EISSN: None     Source Type: Journal    
DOI: 10.1109/64.393138     Document Type: Article

References (20)

1. F. Rosenblatt, “The Perceptron, a Perceiving and Recognizing Automaton,” Project PARA, Cornell Aeronautical Laboratory, Report 85-640-1, Buffalo, NY, 1958.
2. L.K. Jones, “Constructive Approximations for Neural Networks by Sigmoidal Functions,” Proc. IEEE, Vol. 78,1990, pp. 1586–1589.
3. P. Werbos, Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences, doctoral dissertation, Harvard, Cambridge, Mass., 1974.
4. D. Ackley, G. Hinton, and T. Sejnowski, “A Learning Algorithm for Boltzmann Machines,” Cognitive Science, Vol. 9,1985, pp. 147–169.
5. D.B. Fogel, L.J. Fogel, and V.W. Porto, “Evolving Neural Networks,” Biological Cybernetics, Vol. 63, 1990, pp. 487–493.
6. D.E. Rumelhart, G.E. Hinton, and R.J. Williams, “Learning Internal Representations,” Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1, D.E. Rumelhart and J.L. McClelland, eds., MIT Press, Cambridge, Mass., 1986, pp. 318–362.
7. S.K. Rogers and M. Kabrisky, An Introduction to Biological and Artificial Neural Networks for Pattern Recognition, SPIE Optical Engineering Press, Bellingham, Wash., 1991.
8. S. Kirkpatrick, C.D. Gellatt, and M.P. Vecchi, “Optimization by Simulated Annealing,” Science, Vol. 22,1983, pp. 671–680.
9. S. Geman and D. Geman, “Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 6, pp. 721–741.
10. I.O. Bohachevsky, M.E. Johnson, and M.L. Stein, “Generalized Simulated Annealing for Function Optimization,” Technometrics, Vol. 28, 1986, pp. 209–217.
11. L.J. Fogel, A.J. Owens, and M.J. Walsh, Artificial Intelligence Through Simulated Evolution, John Wiley, New York, 1966.
12. 12. D.B. Fogel, Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, IEEE Press, Piscataway, N.J., 1995.
13. K.A. De Jong, “Genetic Algorithms: A 25-Year Perspective,” Computational Intelligence: Imitating Life, J.M. Zurada, R.J. Marks, and C.J. Robinson, eds., IEEE Press, 1994, pp. 125–134.
14. H.P. Schwefel, Evolution and Optimum Seeking, Springer-Verlag, Berlin, 1995.
15. D.B. Fogel and L.C. Stayton, “On the Effectiveness of Crossover in Simulated Evolutionary Optimization,” BioSystems, Vol. 32, No. 3,1994, pp. 171–182.
16. P.J. Angeline, G.M. Saunders, and J.B. Pollack, “An Evolutionary Algorithm That Constructs Recurrent Neural Networks,” IEEE Trans. Neural Networks, Vol. 5, 1994, pp. 54—65.
17. J.D. Schaffer, “Combinations of Genetic Algorithms with Neural Networks or Fuzzy Systems,” Computational Intelligence: Imitating Life, J.M. Zurada, R.J. Marks, and C.J. Robinson, eds., IEEE Press, 1994, pp. 371–382.
18. V.W. Porto, “Detection of Undersea Objects Using Neural Networks,” Proc. 23rd Asilomar Conf on Signals, Systems and Computers, Vol. 1, R.R. Chen, ed., Maple Press, San Jose, Calif., 1989, pp. 376–380.
19. J.R. McDonnell and D. Waagen, “Evolving Recurrent Perceptrons for Time-Series Modeling,” IEEE Trans. Neural Networks, Vol. 5, 1994, pp. 24–38.
20. L. Ingber and B. Rosen, “Genetic Algorithms and Very Fast Simulated Annealing—A Comparison,” Math, and Comp. Mod., Vol. 16, No. 11,1992, pp. 87–100.