Perceptron network

Authors & references

Edited by:

Professor Emeritus Kauko Leiviskä, University of Oulu

Based on: Leiviskä, K., Methods (Chapter 3). In: Leiviskä, K. (ed), Process and Maintenance Management, (Book 14), Papermaking Science and Technology. 2nd edition. Jyväskylä, 2009, Paper Engineer’s Association/Paperi ja Puu Oy. pp. 28–71.

References:

1. Rosenblatt, F. 1958, The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain, Cornell Aeronautical Laboratory, Psychological Review 65(6):386–408.
2. Dayhoff, J. E. 1990. Neural network architectures. Van Nostrand Reinhold, New York. 259 p .
3. Gallant, S. I. 1990. Perceptron-based learning algorithms. IEEE Transactions on Neural Networks 1(2):179–191.
4. Wendemuth, A. 1995. Learning the Unlearnable. Journal of Physics A: Math. Gen. 28: 5423–5436.
5. García Nieto, P.J., Martínez Torres, J., de Cos Juez, F.J. and Sánchez Lasheras, S. 2012. Using multivariate adaptive regression splines and multilayer perceptron networks to evaluate paper manufactured using Eucalyptus globulus. Applied Mathematics and Computation, 219(2): 755–763.
6. Dufour, P., Bhartiya, S., Dhurjati, P.S. and Doyle III, F. 2005. Neural network-based software sensor: Data set design and application to a continuous pulp digester. Control Engineering Practice 13(2):135–143.
7. Iglesias, C., Santos, A., Martínez, J., Pereira, H. and Anjos, O. 2016. Influence of heartwood on pulp properties explained by machine learning techniques. WOOD QC 2016 Conference, Quebec City, Canada, 12–17 June 2016.