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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xiaojin Xue Yun Pan Ruijuan Jiang Yilan Liu |
| Copyright Year | 2015 |
| Description | Author affiliation: Dept. of Comput., Commun. Univ. of China, Beijing, China (Xiaojin Xue; Yun Pan; Ruijuan Jiang; Yilan Liu) |
| Abstract | Classification has become a very important field in the current era of big data. As one of the main stream algorithms for classification, the well-known Error Back Propagation algorithm, with the characteristic of nonlinear mapping, good self-study ability and fault tolerance ability, has been pervasively applied in finance, agriculture, industry and other fields. However, the Error Back Propagation algorithm would face the problems of low accuracy, poor stability and slow convergence if the weights and thresholds are set improperly. In this paper, the Cuckoo algorithm is employed to train the weights and thresholds of the Error Back Propagation algorithm. From the aspects of accuracy, stability and time cost, experiments and performance comparisons towards the basic Error Back Propagation algorithm model (BP), the improved neural network model based on Cuckoo algorithm (BPCS) and the improved neural network model based on Genetic algorithm (GABP) are organized by using two classification datasets, respectively. The results show that the neural network optimizing by Cuckoo algorithm has faster convergence speed, higher accuracy and better stability than others. In addition, the ranges for selecting parameters are suggested based on an appropriate model. |
| Starting Page | 24 |
| Ending Page | 30 |
| File Size | 634267 |
| Page Count | 7 |
| File Format | |
| ISSN | 21579563 |
| e-ISBN | 9781467376792 |
| DOI | 10.1109/ICNC.2015.7377960 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-08-15 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training Algorithm design and analysis genetic algorithm the Error Back Propagation algorithm Prediction algorithms Classification algorithms the Cuckoo algorithm classification Biological neural networks Convergence |
| Content Type | Text |
| Resource Type | Article |
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