Non-linear model automatic generating method

Details Non-linear model automatic generating method Non-linear model automatic generating method

1995-10-17

1998-10-06

MacDonald, Allen R.

Data processing: artificial intelligence

Neural network

Learning method

G06F 1518

Patent

active

058192468

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.

REFERENCES:
patent: 5339818 (1994-08-01), Baker et al.
Holmstorm et al., Using additive noise in back-propagation training, IEEE transactions on neural networks, vol. 3, pp. 24-38, Jan. 1992.
Abdulbary et al., Application of connectionist model to controlling a MSF desalination plant, Proceedings of the third IEEE conference on control applications, pp. 1821-1822, Aug. 26, 1994.
Sudharsanan et al., Self-tuining adaptive control of multi-input, multi-output networks with application to synchronous power generators, 1993 IEEE International Conference on Neural Networks, pp. 1307-1312, Apr. 1, 1993.

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