Sage Journals: Discover world-class research

Abstract

In practical application, the parameters of RBF neural network are difficult to determine. In general, we need to test several times according to experience and prior knowledge, which is lack of a strict design procedure on theoretical basis. And we also don’t know whether the RBF neural network is convergent. This paper proposes a genetic algorithm to optimize the centers and the widths of hidden nodes and the connection weights between hidden layer and output layer of RBF neural network globally. In contrast to optimizing RBF neural network by genetic algorithm partially, each generation group contains the whole parameters of RBF neural network. The fitness value of each individual is calculated by the adaptive function. The optimal individual is obtained by selecting, crossover and mutation by genetic algorithm. The optimal parameters are chosen as initial value of RBF neural network. According to the characteristics of wood dyeing, a predictive model of pigment formula for wood dyeing based on RBF neural network is proposed. The average relative error of the original RBF neural network is 1.55% in 158 epochs. However, the average relative error of the RBF neural network which is optimized globally by genetic algorithm is only 0.87% in 20 generations. Therefore, the convergence rate and approximation precision of the RBF neural network are improved significantly.

Keywords

Genetic algorithm RBF neural network global optimization pigment formula

Get full access to this article

View all access options for this article.

References

Dmytro

, Jiri

and Michal

, Mods: Fast and robust method for two-view matching, Computer Vision and Image Understanding141 (2015), 81–93. doi: 10.1016/j.cviu.2015.08.005

Kang

H.R.

, Kubelka-munk modeling of ink jet ink mixing pigment, Imaging Technology17 (2010), 76–83. doi: 10.1016/j.jebo.2014.06.008

Demyanov

, Backhouse

and Christie

, Geological feature selection in reservoir modelling and history matching with multiple kernel learning, Computers and Geosciences85 (2015), 16–25. doi: 10.1016/j.cageo.2015.07.014

Jasine

, Sunil

C.L.

and Krishna

, Rainbow matchings in strongly edge-colored graphs, Discrete Mathematics338 (2015), 1191–1196. doi: 10. 1016/j.disc.2014.12.019

Yunmei

, Juntao

and Kaiqi

, Model reference adaptive sliding mode control using rbf neural network for active power filter, International Journal of Electrical Power and Energy Systems73 (2015), 249–258. doi: 10.1016/j.ijepes.2015.05.009

Xiaoxuan

, Zhonghu

and Xiaowei

, Diagnosis of misalignment faults by 145 tacholess order tracking analysis and rbf networks, Neurocomputing169 (2015), 439–448. doi: 10.1016/j.neucom.2014.09.008

Jiangyang

and Zhenming

, Multi-source image fusion algorithm based on cellular neural networks with genetic algorithm, Optik-International Journal for Light and Electron Optics126 (2015), 5230–5236. doi: 10.1016/j.ijleo.2015.09.187

Stratis

and Andreas

, Intelligent road adaptive suspension system design using an experts based hybrid genetic algorithm, Expert Systems with Applications42 (2015), 8232–8242. doi: 10.1016/j.conbuildmat.2015.06.035

Ali

, A novel dynamic genetic algorithm-based method for vehicle scheduling in cross docking systems with frequent unloading operation, Computers and Industrial Engineering90 (2015), 221–240. doi: 10.1016/j.cie.2015.09.008

10.

Jeremy

, Kash

, Jose

E.R.-M.

and Nazanin

, Quantifying the risk of project delays with a genetic algorithm, International Journal of Production Economics170 (2015), 34–44. doi: 10.1016/j.ijpe.2015.09.007

11.

Priya

, Sudhakar

B.T.

, Karthik

, Sathish

K.K.

and Rajasekar

, A novel approach for fuel cell parameter estimation using simple genetic algorithm, Sustainable Energy Technologies and Assessments12 (2015), 46–52. doi: 10.1016/j.seta.2015.09.001

12.

Kaushik

and Neha

, Voltage stability enhancement using vsc-opf including wind farms based on genetic algorithm, International Journal of Electrical Power and Energy Systems73 (2015), 560–567. doi: 10.1016/j.ijepes.2015.05.007

13.

Tomas

, Marta

H.-C.

and Pedro

M.-R.

, An iterative method for non-autonomous nonlocal reaction-diffusion equations, Applied Mathematics and Nonlinear Science2 (2017), 73–82. doi: 10.21042/AMNS.2017.1.00006

14.

Sizhong

, Lan

and Yang

, A sufficient condition for the existence of a k-factor excluding a given r-factor, Applied Mathematics and Nonlinear Science2 (2017), 13–20. doi: 10.21042/AMNS.2017.1.00002

Application of RBF neural network optimized globally by genetic algorithm in intelligent color matching of wood dyeing

Abstract

Keywords

Get full access to this article

References