Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Knowledge-based artificial neural networks
Artificial Intelligence
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Data mining methods for knowledge discovery
Data mining methods for knowledge discovery
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Machine Learning
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Abstract-Driven Pattern Discovery in Databases
IEEE Transactions on Knowledge and Data Engineering
A survey of evolutionary algorithms for data mining and knowledge discovery
Advances in evolutionary computing
Intelligent prognostics tools and e-maintenance
Computers in Industry - Special issue: E-maintenance
A hybrid learning-based model for on-line detection and analysis of control chart patterns
Computers and Industrial Engineering
Flexible learning of problem solving heuristics through adaptive search
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
Rule learning by searching on adapted nets
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
Support vector machines for quality monitoring in a plastic injection molding process
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Implementing a data mining solution for enhancing carpet manufacturing productivity
Knowledge-Based Systems
Journal of Intelligent Manufacturing
| Hi-index | 0.00 |
In many manufacturing processes, some key process parameters (i.e., system inputs) have very strong relationship with the categories (e.g., normal or various faulty products) of finished products (i.e., system outputs). The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a hybrid learning-based model is developed for on-line intelligent monitoring and diagnosis of the manufacturing processes. In the proposed model, a knowledge-based artificial neural network (KBANN) is developed for monitoring the manufacturing process and recognizing faulty quality categories of the products being produced. In addition, a genetic algorithm (GA)-based rule extraction approach named GARule is developed to discover the causal relationship between manufacturing parameters and product quality. These extracted rules are applied for diagnosis of the manufacturing process, provide guidelines on improving the product quality, and are used to construct KBANN. Therefore, the seamless integration of GARule and KBANN provides abnormal warnings, reveals assignable cause(s), and helps operators optimally set the process parameters. The proposed model is successfully applied to a japing-line, which improves the product quality and saves manufacturing cost.