MATHEMATICAL MODEL OF ARCHITECTURE AND LEARNING PROCESSES OF ARTIFICIAL NEURAL NETWORKS
Abstract
A mathematical model of architecture and learning processes of multilayer artificial neural netwoks is discussed in the paper. Dynamical systems theory is used to describe the learning precess of networks consisting of linear, weakly nonlinear and nonlinear neurons. Conjugacy between a gradient dynamical system with a constant time step and a cascade generated by its Euler method theorem is applied as well.
Keywords:
artificial neural network, neuron, learning process, topological conjugacy, gradient dynamical system, Euler methodDetails
- Issue
- Vol. 7 No. 1 (2003)
- Section
- Research article
- Published
- 2003-03-31
- Licencja:
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This work is licensed under a Creative Commons Attribution 4.0 International License.
