JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Design of Digit Recognition System Realized with the Aid of Fuzzy RBFNNs and Incremental-PCA
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Design of Digit Recognition System Realized with the Aid of Fuzzy RBFNNs and Incremental-PCA
Kim, Bong-Youn; Oh, Sung-Kwun; Kim, Jin-Yul;
  PDF(new window)
 Abstract
In this study, we introduce a design of Fuzzy RBFNNs-based digit recognition system using the incremental-PCA in order to recognize the handwritten digits. The Principal Component Analysis (PCA) is a widely-adopted dimensional reduction algorithm, but it needs high computing overhead for feature extraction in case of using high dimensional images or a large amount of training data. To alleviate such problem, the incremental-PCA is proposed for the computationally efficient processing as well as the incremental learning of high dimensional data in the feature extraction stage. The architecture of Fuzzy Radial Basis Function Neural Networks (RBFNN) consists of three functional modules such as condition, conclusion, and inference part. In the condition part, the input space is partitioned with the use of fuzzy clustering realized by means of the Fuzzy C-Means (FCM) algorithm. Also, it is used instead of gaussian function to consider the characteristic of input data. In the conclusion part, connection weights are used as the extended diverse types in polynomial expression such as constant, linear, quadratic and modified quadratic. Experimental results conducted on the benchmarking MNIST handwritten digit database demonstrate the effectiveness and efficiency of the proposed digit recognition system when compared with other studies.
 Keywords
Digit Recognition;Fuzzy RBFNNs Pattern Classifier;Incremental-PCA(IPCA);Fuzzy C-Means(FCM);Least Square Estimation(LSE);
 Language
Korean
 Cited by
 References
1.
S. K. Oh, W. Pedrycz, B. J. Park, "Polynomial-based Radial Basis Function Neural Networks realized with the Aid of Particle Swarm Optimization," Fuzzy Sets and Systems, vol. 163, pp. 54-77, 2011. crossref(new window)

2.
S. K. Oh, W. D. Kim, and W. Pedrycz, "Fuzzy Radial Basis Function Neural Networks with information granulation and its parallel genetic optimization," Fuzzy Sets and Systems, vol. 237, pp. 96-117, February 2014. crossref(new window)

3.
H. H lee, X. Cui, H. R. Kim, S. W. Ma, J. H. Lee and H. I. Kim, "Robust Object Tracking in Mobile Robots using Object Features and On-line Learning based Particle Filter," Journal of Institute of Control, vol. 18, pp. 562-570, 2012.

4.
Byoung-Joo Kim, "Feature Extraction on High Dimensional Data Using Incremental PCA," KIICS Integrated Conference, vol. 8, no. 7, pp. 1475-1479, 2004.

5.
D. Zhang and Z. H. Zhou, "$(2D)^2PCA$ : Two-directional two-dimensional PCA for efficient face representation and recognition," Neurocomputing, vol. 69, pp. 224-231, 2005. crossref(new window)

6.
Y. LeCun, L. Bottou, Y. Bengio and P. Haffner, "Gradient-Based Learning Applied to Document Recognition," IEEE, vol. 86, pp. 2278-2324, 1998. crossref(new window)

7.
S. C. Lee, S, K. Oh and H. K. Kim, "Design of PCA-based pRBFNNs Pattern Classifier for Digit Recognition," Korea Institute of intelligent systems, vol. 25, no. 4, pp. 355-360, 2015. crossref(new window)

8.
S. H. Lee, S. Y. Choi, S. Y. Lee and Y. S. Kim, "Licence Plate Recognition Using Improved IAFC Fuzzy Neural Network," Korea Institute of intelligent systems, vol. 19, no. 1, pp. 6-12, 2009. crossref(new window)

9.
S. S. Yoon, H. S. Chung, K. O. Yi, Y. B. Lee and S. H. Lee, "A Study on Human Recognition Experiments with Handwritten Digit for Machine Recognition of Handwritten Digit," Korea Institute of intelligent systems, vol. 18, no. 3, pp. 373-380, 2008.

10.
In-Cheol Kim, "Word Separation in Handwritten Legal Amounts on Bank Check by Measuring Gap Distance Between Connected Components," Korea Institute of intelligent systems, vol. 14, no. 1, pp. 57-62, 2004.

11.
W. Y. Choi, S. K. Oh and H. K. Kim, "Design of RBFNN-Based Pattern Classifier for the Classification of Precipitation/Non-Precipitation," Korea Institute of intelligent systems, vol. 24, no. 6, pp. 586-591, 2014. crossref(new window)

12.
Yann LeCun, Corinna Cortes and Christopher J.C. Burges, "The MNIST DATABASE of handwritten digits," Available:http://yann.lecun.com/exdb/mnist