Non-linear model automatic generating method
US5819246A · kind A · utility
Assignee
Inventors
Key dates
| Filing date | Oct 17, 1995 |
| Grant date | Oct 6, 1998 |
| Priority date | — |
| Expiry date | Oct 17, 2015 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F18/211
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present invention is intended to automatically select least required input items for a non-linear model and improve an efficiency of building up the non-linear model. For building up, for example, a neural network as the non-linear model, a group-by rule 105 and dividing point information 106 of the data are automatically generated by a group-by rule induction device 104 by selecting a dividing method option 101 and a group selection information 103 if data for learning 101 is given. An initial neural network model generating device 107 automatically generates an initial neural network model 108 from the group-by rule 105. The initial neural network model is learned in a neural network model learning device 111 and outputted as a post-learning neural network model 112. Data for learning with group information 110 which is the data for learning is generated in a data classification device 109 by using data for learning 102, group-by rule 105 and dividing point information 106. Input-output variables can be automatically selected from the data for learning according to selection of the group and a neural network model of respective groups can be built up.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.