Affiliation:
1. School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, Hunan 410014, China
Abstract
The traditional Back Propagation (BP) has some significant disadvantages, such as training too slowly, easiness to fall into local minima, and sensitivity of the initial weights and bias. In order to overcome these shortcomings, an improved BP network that is optimized by Cuckoo Search (CS), called CSBP, is proposed in this paper. In CSBP, CS is used to simultaneously optimize the initial weights and bias of BP network. Wine data is adopted to study the prediction performance of CSBP, and the proposed method is compared with the basic BP and the General Regression Neural Network (GRNN). Moreover, the parameter study of CSBP is conducted in order to make the CSBP implement in the best way.
Funder
Ministry of Education of the People’s Republic of China
Subject
General Environmental Science,General Biochemistry, Genetics and Molecular Biology,General Medicine
Cited by
33 articles.
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