Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
A penalty-function approach for pruning feedforward neural networks
Neural Computation
Symbolic knowledge extraction from trained neural networks: a sound approach
Artificial Intelligence
Designing a decompositional rule extraction algorithm for neural networks
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
| Hi-index | 0.00 |
The principal object of this paper is to develop a neural network model, which can simulate the plasma enhanced chemical vapor deposition (PECVD) process in TFT-Array procedure. Then the Boolean logic rules are extracted from the trained neural network in order to establish a knowledge base of expert system. The input data of neural network was collected form the process parameters of PECVD machines in the TFT-Array department, included the flow rate of all gases, pressure and temperature of the chamber, etc. After checking, explaining and integrating the extraction rules into knowledge base, the rules can be the basics of membrane thickness prediction and alarm diagnosis in PECVD system.