Neural network-based analysis of MR time series
✍ Scribed by Harald Fischer; Jürgen Hennig
- Publisher
- John Wiley and Sons
- Year
- 1999
- Tongue
- English
- Weight
- 190 KB
- Volume
- 41
- Category
- Article
- ISSN
- 0740-3194
No coin nor oath required. For personal study only.
📜 SIMILAR VOLUMES
This paper is concerned with modelling time series by single hidden layer feedforward neural network models. A coherent modelling strategy based on statistical inference is presented. Variable selection is carried out using simple existing techniques. The problem of selecting the number of hidden un
An efficient computational approach to time domain microwave design and optimization is presented. In particular, artificial neural networks are coupled with a fullwave time domain simulator in order to model and optimize microwave structures. Furthermore, neural networks are used to predict the lat
It is known that superpositions of ridge functions (single hidden-layer feedforward neural networks) may give good approximations to certain kinds of multivariate functions. It remains unclear, however, how to effectively obtain such approximations. In this paper, we use ideas from harmonic analysis
## Abstract In this paper a new time domain based neural network model of a 0.18 μm RF MOSFET will be demonstrated. The model consists of only three intrinsic non‐linear current sources, each being modelled by a neural network derived from large signal time domain voltage/current waveform measureme