title: Novel back propagation optimization by Cuckoo Search algorithm.
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.
authors.affiliation: School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, Hunan 410014, China.
authors.forename: Jiao-hong
authors.initials: JH
authors.lastname: Yi
authors.affiliation: School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, Hunan 410014, China.
authors.forename: Wei-hong
authors.initials: WH
authors.lastname: Xu
authors.affiliation: School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, Hunan 410014, China.
authors.forename: Yuan-tao
authors.initials: YT
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D008962:Models, Theoretical; D016571:Neural Networks, Computer