The Strength of Weak Learnability
Machine Learning
Neural networks and the bias/variance dilemma
Neural Computation
Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Machine Learning
Measurement of machine performance degradation using a neural network model
Computers in Industry - Special issue: computer integrated manufacturing (ICCIM '95)
Extracting rules from neural networks by pruning and hidden-unit splitting
Neural Computation
Data mining methods for knowledge discovery
Data mining methods for knowledge discovery
Data Mining for Design and Manufacturing: Methods and Applications
Data Mining for Design and Manufacturing: Methods and Applications
Machine Learning
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Ensembling neural networks: many could be better than all
Artificial Intelligence
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
An ensemble of neural networks for weather forecasting
Neural Computing and Applications
Evolutionary ensembles with negative correlation learning
IEEE Transactions on Evolutionary Computation
Modeling and optimizing maintenance schedule for energy systems subject to degradation
Computers and Industrial Engineering
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In this paper, a hybrid learning-based model is developed for online intelligent monitoring and diagnosis of manufacturing processes. In this model, a Genetic Algorithm (GA)-based selective Neural Network (NN) ensemble (GASENN) is developed for monitoring the manufacturing process and recognising faulty quality categories of products being produced. In addition, a GA-based Rule (GARule) extraction algorithm is developed to discover the causal relationship between manufacturing parameters and product quality. These extracted rules are applied for fault diagnosis of the manufacturing process to reveal why this has occurred and how to recover from the abnormal condition with the specific guidelines on process parameter settings. Therefore, the seamless integration of GASENN and GARule provides abnormal warning, reveals assignable cause(s) and helps operators optimally set the process parameters. This model is conducted in an Ethernet network environment with various sensors, PLCs, computers, etc. The whole system is successfully applied into a japanning-line, which improves the product quality and saves manufacturing cost.