A multi-interacting perceptron model with continuous outputs

R.M.C. de Almeida and E. Botelho

Physica A: Statistical Mechanics and its Applications, 1997, vol. 242, issue 1, 27-37

Abstract: We consider learning and generalization of real functions by a multi-interacting feed-forward network model with continuous outputs with invertible transfer functions. The expansion in different multi-interacting orders provides a classification for the functions to be learnt and suggests the learning rules, that reduce to the Hebb-learning rule only for the second order, linear perceptron. The over-sophistication problem is straightforwardly overcome by a natural cutoff in the multi-interacting synapses: the student is able to learn the architecture of the target rule, that is, the simpler a rule is the faster the multi-interacting perception may learn. Simulation results are in excellent agreement with analytical calculations.

Date: 1997
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S037843719700188X
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:242:y:1997:i:1:p:27-37

DOI: 10.1016/S0378-4371(97)00188-X

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().