@article{(Open Science Index):https://publications.waset.org/pdf/3930,
	  title     = {A New Approach to Polynomial Neural Networks based on Genetic Algorithm},
	  author    = {S. Farzi},
	  country	= {},
	  institution	= {},
	  abstract     = {Recently, a lot of attention has been devoted to
advanced techniques of system modeling. PNN(polynomial neural
network) is a GMDH-type algorithm (Group Method of Data
Handling) which is one of the useful method for modeling nonlinear
systems but PNN performance depends strongly on the number of
input variables and the order of polynomial which are determined by
trial and error. In this paper, we introduce GPNN (genetic
polynomial neural network) to improve the performance of PNN.
GPNN determines the number of input variables and the order of all
neurons with GA (genetic algorithm). We use GA to search between
all possible values for the number of input variables and the order of
polynomial. GPNN performance is obtained by two nonlinear
systems. the quadratic equation and the time series Dow Jones stock
index are two case studies for obtaining the GPNN performance.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {2},
	  number    = {8},
	  year      = {2008},
	  pages     = {2700 - 2707},
	  ee        = {https://publications.waset.org/pdf/3930},
	  url   	= {https://publications.waset.org/vol/20},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 20, 2008},
	}