Database design for predictive preventive maintenance system of automated manufacturing system
Proceedings of the 14th annual conference on Computers and industrial engineering
Integration of self-organizing feature map and K-means algorithm for market segmentation
Computers and Operations Research
Computers and Industrial Engineering
A hierarchical genetic algorithm for segmentation of multi-spectral human-brain MRI
Expert Systems with Applications: An International Journal
Optimizing back-propagation networks via a calibrated heuristic algorithm with an orthogonal array
Expert Systems with Applications: An International Journal
Rule effectiveness in rule-based systems: A credit scoring case study
Expert Systems with Applications: An International Journal
Application of a hybrid case-based reasoning approach in electroplating industry
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Dynamic adaptive ensemble case-based reasoning: application to stock market prediction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Failure prediction with self organizing maps
Expert Systems with Applications: An International Journal
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Review: Hybrid expert systems: A survey of current approaches and applications
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Important issues for notebook computer companies include how to ascertain the problems of machines sent by customers, and then assigning those machines to the appropriate department for servicing; and how to maintain breakdown data to save both handling time and costs. However, in practical application, unreliable data decreases the model's accuracy, and thus, new methods are brought forward in rapid succession to increase accuracy when inferring causes of notebook computer breakdown. This study integrated several different methods, consisting of a neural network, with case-based reasoning (CBR) and a rule-based system (RBS) to propose a gradual model for inferring causes of notebook computer breakdown. It stressed that the model should have accuracy, elasticity, and transparent interpretability. The model contains three phases: data extracting, group indexing and knowledge creation. Initially, the data extraction phase uses a self-organizing map (SOM) and a revised learning vector quantization network method to reduce isomorphic data to similarity characteristic-based clustering, thus, improving data quality. Then, the group indexing phase establishes a clustering index prediction model based on a back-propagation network (BPN) and genetic algorithm (GA) to increase the efficiency of case selections. Then, the knowledge creation phase uses CBR and RBS to create a notebook computer breakdown case selection model to determine the breakdown cause. Finally, the experimental results show that data purification can actually improve the model's accuracy. The CBR with clustering index and rule-based reasoning has a better classification accuracy rate than either the CBR, without the clustering index and rule-based reasoning, or the traditional CBR, in addition, it provides a reference for inferring causes of notebook computer breakdown.