Adaptive Transfer Function Networks
Conference
·
OSTI ID:10160192
- Los Alamos National Lab., NM (United States)
Real-time pattern classification and time-series forecasting applications continue to drive artificial neural network (ANN) technology. As ANNs increase in complexity, the throughput of digital computer simulations decreases. A novel ANN, the Adaptive Transfer Function Network (ATF-Net), directly addresses the issue of throughput. ATF-Nets are global mapping equations generated by the superposition of ensembles of neurodes having arbitrary continuous functions receiving encoded input data. ATF-Nets may be implemented on parallel digital computers. An example is presented which illustrates a four-fold increase in computational throughput.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- W-7405-ENG-36
- OSTI ID:
- 10160192
- Report Number(s):
- LA-UR-93-1983; CONF-930741-3; ON: DE93014344
- Resource Relation:
- Conference: World congress on neural networks,Portland, OR (United States),11-15 Jul 1993; Other Information: PBD: [1993]
- Country of Publication:
- United States
- Language:
- English
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