| Title |
RBF Network Structure for Prediction of Non-linear, Non-stationary Time Series |
| Authors |
김상환(Kim, Sang-Hwan) ; 이종호(Lee, Jong-Ho) |
| Keywords |
RBF(Radial Basis Function) ; Neural Network ; Prediction ; Time-Series |
| Abstract |
In this paper, a modified RBF(Radial Basis Function) network structure is suggested for the prediction of a time-series with non-linear, non-stationary characteristics. Coventional RBF network predicting time series by using past outputs sense the trajectory of the time series and react when there exists strong relation between input and hidden activation function's RBF center. But this response is highly sensitive to level and trend of time serieses. In order to overcome such dependencies, hidden activation functions are modified to react to the increments of input variable and multiplied by increment(or dectement) for prediction. When the suggested structure is applied to prediction of Macyey-Glass chaotic time series, Lorenz equation, and Rossler equation, improved performances are obtained. |