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Abstract

In order to make the RBF hidden layer centres being established more adaptively and avoid the blindness, this paper proposes a fusion algorithm in order to optimize the parameters of the RBF neural network used in recognizing the state of coal flotation. Firstly, in the optimization algorithm, the improved immune algorithm was used to determine the center position and the number of hidden layer of RBF neural network. Before this, the immune algorithm has been improved in several aspects, such as the initial population selection algorithm and the method for segment selection of affinity thresholds. In addition, the antibody removal mechanism, antibody immune mechanism and antibody concentration regulation principle had also been added in immune algorithm. Secondly, in virtue of combining a fuzzy C-means clustering algorithm, the centers of the hidden layer were optimized accurately. Through the sample verification, the RBF neural network obtained by the fusion algorithm was proved to have been improved significantly in the accuracy of identifying the coal flotation state and has better generalization ability.

Keywords

RBF neural network centers of hidden layer froth image of coal flotation immune algorithm fuzzy c-means clustering algorithm state recognition

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References

Guan

, Gao

J.-W.

, Zhang

, et al., Traffic flow prediction based on K-means clustering algorithm and RBF neural network, Journal Of Qingdao University (E&T)29(2) (2014), 20–23.

Liu

, Wang

and Yan

, Transformer fault diagnosis based RBF neural network and adaptive genetic algorithm, Engineering Journal of Wuhan University49(1) (2016), 89–92.

Jie

, Bai-Chuan

, Quan-Fu

, et al., Forecasting model of trip generation based on RBF neural network, Journal of WUT (Information & Management Engineering)36(1) (2014), 43–48.

Babu

G.S.

and Suresh

, Meta-cognitive RBF network and its projection based learning algorithm for classification problems, Applied Soft Computing13(1) (2013), 654.

Huang

W.-M.

, Xu

S.-S.

, Deng

Z.-R.

, et al., Short-term traffic flow prediction of optimized RBF neural networks based on the modified ABC algorithm, Computer Engineering & Science38(4) (2016), 714–719.

Haiqi

, Zhen

and Quanhui

, A color prediction model of printer based on GA-RBF neural network, Laser & Optoelectronics Progress52(113301) (2015), 1133011–113301-3.

Zheng

and Wang

, Transfer learning based radius-basis-function neural-network modeling, Computer, Engineering and Applications52(5) (2016), 6–10.

Cao

J.-Y.

, Lu

G.-P.

and Yang

, Research on the generalization of radial basic function neural network and its application, Aerospace Electronics Information Engineering and Control28(1) (2006), 72–74.

Mao

, Zhao

H.-D.

and Yao

J.-J.

, Application and prospect of artificial neural network, Electronic Design Engineering45(11) (2011), 1581–1586.

10.

Cohn

, Atlas

and Richard

, Improving generalization with active learning, Maching Learning15 (1994), 201–221.

11.

, Ge

L.-D.

and Wu

Y.-X.

, An application of improved RBF neural network in modulation recognition, Acta Automatica Sinica33(6) (2007), 652.

12.

Zhu

, Zhang

Y.-H.

and Bao

Q.-L.

, Identification of temperature drift for FOG using RBF neural networks, Journal of Shanghai Jiaotong University34(2) (2000), 222.

13.

Weikuan

, Dean

and Xiaoyang

, K-means and GA-RBF-LMS neural network recognition of robot picking fruit, Transactions of the Chinese Society of Agricultural Engineering31(18) (2015), 175.

14.

Saifang

, Fang

and Wanbin

, K mean algorithm based on optimization of initial cluster centers, Computer Engineering and Science32(10) (2010), 105.

15.

Jingxin

, Chuntian

, et al., Application of the improved immune algorithm in function optimization, Journal of Harbin Institute of Technology41(7) (2009), 135.

16.

Shao

, Weimin

, Ning

and Liang

, Short-term forecasting for PV power generation using RBF neural network based on improved K-means algorithm, Journal of Shanghai Dianji University20(1) (2017), 28–31.

17.

Platt

, A resource-allocating network for function interpolation, Neural Computation3(2) (1991), 213.

18.

Tian

, Xie

, Sun

and He

, Optimization of variable blank holding force based on artificial immune algorithm and RBF neural network, Machine Tool & Hydraulics43(7) (2015), 6–9.

19.

Yang

L.-C.

, Lin

and Wang

D.-W.

, Dynamic route guidance algorithm based on artificial immune system, Journal of Control Theory and Applications5(4) (2007), 325.

20.

Liu

M.S.

, Hybrid clustering algorithm based on the Kmeans clustering, Jorrnal of Anqing Teachers College (Natural Science Edition)22 (2016), 40–42.

21.

Xiuling

, Dong

and Haibin

, Digital recognition based on hybrid optimization of RBF neural network structure, Journal of shenyang university (Natural Science Edition)27(3) (2015), 214.

22.

Nikhil

P.R.

and Bezdek

J.C.

, On cluster validity for the fuzzy C-means model, IEEE Transactions on Fuzzy Systems3(3) (1995), 370.

Optimization of RBFneural network used in state recognition of coal flotation

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