A Data Simulatilon System Using CSINC Polynomial Higher Order Neural Networks
نویسنده
چکیده
In this paper a data simulation system, based on the cos(x) and sin(x)/x functions, called CSINC Polynomial Higher Order Neural Network (CSINCPHONN) has been developed. The CSINCPHONN model provides one more opportunity to find the optimal neural network model for simulation and prediction. This paper also proves that CSINCPHONN models can converge when simulating XOR data. This study also tests rainfall data. Based on the test results, CSINCPHONN model is 0.2774% better than Polynomial Higher Order Neural Network (PHONN) model and 0.2448% batter than trigonometric Polynomial Higher Order Neural Network (THONN) model in simulating rainfall. Moreover, foreign exchange rate simulation has been tested by using CSINCPHONN models. Test results show that CSINCPHONN models are about 0.0868% to 8.0725% better than PHONN, THONN, Sigmoid Polynomial Higher Order Neural Network (SPHONN), and SINC Polynomial Higher Order Neural Network (SINCHONN) models. Key Word Neural Networks, Higher Order, COS/SINC, CSINCSPHONN
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