An improved wavelet neural network for classification and function approximation
| dc.contributor.author | Pauline, Ong | |
| dc.date.accessioned | 2015-09-14T03:56:20Z | |
| dc.date.available | 2015-09-14T03:56:20Z | |
| dc.date.issued | 2011-01 | |
| dc.description.abstract | Properly designing a wavelet neural network (WNN) is crucial for achieving the optimal generalization performance. In this thesis, two different approaches were proposed for improving the predictive capability of WNNs. First, the types of activation functions used in the hidden layer of the WNN were varied. Second, the proposed enhanced fuzzy c-means clustering algorithm-specifically, the modified point symmetry-based fuzzy c-means (MPSDFCM) algorithm-was employed in selecting the locations of the translation vectors of the WNN. The modified WNN was then applied in the areas of classification and function approximation. In the context of classification, the modified WNN was implemented to heterogeneous cancer and diabetes classification using five different microarray datasets. The comparative experimental results showed that the proposed methodology achieved an almost 100% classification accuracy in multiclass prediction, leading to superior performance with respect to other clustering algorithms. Performance comparisons with other classifiers were made. An assessment analysis showed that this proposed approach outperformed most of the other classifiers. In the context of function approximation, the modified WNN was applied in approximating five different functions. Performance comparisons indicated significant improvement in the approximation accuracy was accomplished by the proposed WNN. Subsequently, performance comparisons with other methods in approximating the same benchmark piecewise function were made. Evaluation demonstrated the superiority of the proposed approach when compared with other methods. A study of the proposed WNN in a real-world application, i.e. prediction of blood glucose level for diabetics was also investigated. A novel hybrid algorithm for edge detection was presented in this thesis. The resulting algorithm, namely, wavClust, was then applied in the microarray image spot segmentation. Comparisons with the classical spot segmentation methods were made. Assessment analysis showed that the proposed wavClust algorithm was able to segment all the donut-shaped spot, irregular spot and spots with intensity variations and different noise types accurately, compared to the classical methods. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/1183 | |
| dc.language.iso | en | en_US |
| dc.subject | Wavelet neural | en_US |
| dc.subject | Network for classification | en_US |
| dc.subject | Function approximation | en_US |
| dc.title | An improved wavelet neural network for classification and function approximation | en_US |
| dc.type | Thesis | en_US |
Files
License bundle
1 - 1 of 1
Loading...
- Name:
- license.txt
- Size:
- 1.71 KB
- Format:
- Item-specific license agreed upon to submission
- Description: