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Article
Distributed Fault Tolerance in Optimal Interpolative Nets
IEEE Transactions on Neural Networks
  • Daniel J. Simon, Cleveland State University
Document Type
Article
Publication Date
11-1-2001
Abstract
The recursive training algorithm for the optimal interpolative (OI) classification network is extended to include distributed fault tolerance. The conventional OI Net learning algorithm leads to network weights that are nonoptimally distributed (in the sense of fault tolerance). Fault tolerance is becoming an increasingly important factor in hardware implementations of neural networks. But fault tolerance is often taken for granted in neural networks rather than being explicitly accounted for in the architecture or learning algorithm. In addition, when fault tolerance is considered, it is often accounted for using an unrealistic fault model (e.g., neurons that are stuck on or off rather than small weight perturbations). Realistic fault tolerance can be achieved through a smooth distribution of weights, resulting in low weight salience and distributed computation. Results of trained OI Nets on the Iris classification problem show that fault tolerance can be increased with the algorithm presented in this paper.
DOI
10.1109/72.963771
Version
Postprint
Citation Information
Simon, D.; , "Distributed fault tolerance in optimal interpolative nets," Neural Networks, IEEE Transactions on , vol.12, no.6, pp.1348-1357, Nov 2001 doi: 10.1109/72.963771