Related Experiment Videos
On the approximation of stochastic processes by approximate identity neural networks
C Turchetti1, M Conti, P Crippa
1Department of Electronics, University of Ancona, 60131 Ancona, Italy.
Abstract:
The ability of a neural network to learn from experience can be viewed as closely related to its approximating properties. By assuming that environment is essentially stochastic it follows that neural networks should be able to approximate stochastic processes. The aim of this paper is to show that some classes of artificial neural networks exist such that they are capable of providing the approximation, in the mean square sense, of prescribed stochastic processes with arbitrary accuracy. The networks so defined constitute a new model for neural processing and extend previous results concerning approximating capabilities of artificial neural networks.
Related Concept Videos
Approximate Integration
Application of Linearization and Approximation
Linearization and Approximation
Linear Approximation in Time Domain
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
Linear Approximations
Linear Approximation in Frequency Domain
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.