Abstract
A technical description of the backpropagation network is presented along with the feedforward backpropagation artificial neural network.
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References
Bridle, J. S. (1989). Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition. In F. Fogelman-Soulié & J. Hérault (Eds.), Neuro-computing: Algorithms, architectures. New York: Springer.
Buscema, M. (1994). Squashing Theory. Modello a Reti Neurali per la Previsione dei Sistemi Complessi [Squashing theory: A neural network model for prediction of complex systems] Rome: Armando, Semeion Collection.
Buscema, M. (1996). SQUASH. Shell for program feed forward neural networks, Semeion Software n. 5, Rome, 1992–1996.
Buscema, M., & Sacco, P. L. (2000). Feedforward networks in financial prediction: The future that modifies the present. Expert System, 17(3), 149–170.
Chauvin, Y., & Rumelhart, D. E. (Eds.). (1995). Backpropagation: Theory, architectures, and applications. Hillsdale: Lawrence Erlbaum.
Fahlman, S. E. (1988). An empirical study of learning speed in back-propagation networks (CMU Technical Report, CMU-CS-88-162).
Jacobs, R. A. (1988). Increased rates of convergence through learning rate adaptation. Neural Network, 1, 295–307.
Lapedes, A., & Farber, R. (1987). Nonlinear signal processing using neural networks: Prediction and system modeling (Los Alamos National Laboratory Report LA-UR-87-2662).
McClelland, J. L., & Rumelhart, D. E. (1988). Explorations in parallel distributed processing. Cambridge, MA: MIT Press.
Minai, A. A., & Williams, R. D. (1990). Acceleration of backpropagation through learning rate and momentum adaptation. International Joint Conference on Neural Networks, 1, 676–679.
Neuralware. (1995). Neural computing. Pittsburgh: NeuralWare Inc.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representations by error propagation. Nature, 323, 533–536.
Samad, T. (1988). Back-propagation is significantly…. International Neural Network Society Conference Abstracts.
Samad, T. (1989). Back-propagation extension (Honeywell SSDC Technical Report).
Tawel, R. (1989). Does neuron learn like the synapse? In D. S. Touretzky (Ed.), Advances in neural information processing systems (Vol. 1). San Mateo: Morgan Kaufman.
Weigend, A. S., Rumelhart, D. E., & Huberman, B. A. (1991). Generalization by weight-elimination with applications to forecasting. In R. P. Lippmann et al. (Ed.), Advances in neural information processing systems 3 (pp. 875–882). Morgan Kaufmann, San Mateo, CA.
Werbos, P. (1974). Beyond regression: New tools for prediction and analysis in behavioral sciences, Ph.D. thesis, Harvard, Cambridge, MA.
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Buscema, M. (2013). Supervised Artificial Neural Networks: Backpropagation Neural Networks. In: Buscema, M., Tastle, W. (eds) Intelligent Data Mining in Law Enforcement Analytics. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4914-6_7
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