Name
Abstract | ||
|---|---|---|
Pattern classification was an important part of the RBF neural network application. When the electronic nose is concerned, in many cases it is difficult to obtain the entire representative sample; it requires frequent updating the sample libraries and re-training the electronic nose. In addition, the gas detected from the online environment is not always the known gas in the training samples. This paper proposes a RBF neural network model in order to identify gas. This model uses K-means clustering algorithm and has incremental learning ability, the network output node can be adjustable online to ensure the network with high generalization ability and some incremental learning ability. Finally, the classification system based on this algorithm is used to identify the vinegar online. The results show that this algorithm has faster convergence speed, good performance of the network's online classifieds. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.4304/jcp.7.9.2276-2282 | JOURNAL OF COMPUTERS |
Keywords | Field | DocType |
radial basis function, K-means clustering, incremental learning, electronic nose | Convergence (routing),Data mining,Radial basis function,Computer science,Classifieds,Artificial intelligence,Artificial neural network,Cluster analysis,Electronic nose,k-means clustering,Pattern recognition,Sampling (statistics),Machine learning | Journal |
Volume | Issue | ISSN |
7 | 9 | 1796-203X |
Citations | PageRank | References |
1 | 0.38 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hong Men | 1 | 5 | 3.86 |
| Lei Wang | 2 | 4 | 0.89 |
| Haiping Zhang | 3 | 1 | 0.38 |