CAJ | 학술논문

为提高频谱利用率，该文利用非线性动力学理论对频谱状态持续时长序列进行建模并预测。以实际采集的频谱数据作为研究对象，采用指向导数法对该时长序列进行非一致延长时间相空间重构，利用基于尺度的Lyapunov指数判定其混沌特性。以基于Davidon-Fletcher-Powell方法的二阶Volterra预测模型(DFPSOVF)为基础，提出一种基于限域拟牛顿方法的Volterra自适应滤波器系数调整模型，并将该模型应用于具有混沌特性的短时频谱状态持续时长预测，通过自适应剔除对预测贡献小的滤波器系数，降低预测模型的复杂度。实验结果表明该算法在保证预测精度的同时降低运算复杂度。
위제고빈보이용솔，해문이용비선성동역학이론대빈보상태지속시장서렬진행건모병예측。이실제채집적빈보수거작위연구대상，채용지향도수법대해시장서렬진행비일치연장시간상공간중구，이용기우척도적Lyapunov지수판정기혼돈특성。이기우Davidon-Fletcher-Powell방법적이계Volterra예측모형(DFPSOVF)위기출，제출일충기우한역의우돈방법적Volterra자괄응려파기계수조정모형，병장해모형응용우구유혼돈특성적단시빈보상태지속시장예측，통과자괄응척제대예측공헌소적려파기계수，강저예측모형적복잡도。실험결과표명해산법재보증예측정도적동시강저운산복잡도。
In order to enhance the spectrum utilization, this paper uses the nonlinear dynamics theory for modeling and prediction of spectrum state duration. Firstly, the real spectrum state duration is investigated. Then, this study uses the directional derivative to accomplish the state-space reconstruction of the spectrum time series with the non-uniform time delays. Finally, the Scale-Dependent Lyapunov Exponent (SDLE) is used to determine the characteristics of chaos. Based on the Davidon-Fletcher-Powell-based Second Order of Volterra Filter (DFPSOVF) method, a novel Volterra model with adaptive coefficient adjusting using Limited storage Broyden-Fletcher- Goldfarb-Shanno quasi-Newton (L-BFGS) method is proposed. Furthermore, the proposed model is applied to predict the short-term spectrum with chaotic characteristics. To reduce the complexity of this new model, the useless filter coefficients are eliminated adaptively. The numerical simulations show that the new algorithm can reduce the complexity and guarantee prediction accuracy.