CAJ | 학술논문

针对现有混沌理论中嵌入维数和延迟时间的选取难以达到最优，局域预测时邻近预测点的选取不够准确，提出了改进的混沌理论：采用改进的C-C法找到嵌入维数和延迟时间；邻近预测点的选取根据参考相点的演化趋势进行判断。针对最小二乘支持向量回归机LSSVR参数难以确定，提出ACPSO-LSSVR：自适应混沌粒子群ACPSO一方面能根据群体早熟收敛程度和个体自适应值来调整惯性权重，另一方面能根据混沌变量的随机性和遍历性进行粒子的初始化，加快优化过程，防止局部极小。采用ACP-SO来优化LSSVR的待选参数，提高负荷预测的精度。实例分析验证了该方法的可行性和实用性。
침대현유혼돈이론중감입유수화연지시간적선취난이체도최우，국역예측시린근예측점적선취불구준학，제출료개진적혼돈이론：채용개진적C-C법조도감입유수화연지시간；린근예측점적선취근거삼고상점적연화추세진행판단。침대최소이승지지향량회귀궤LSSVR삼수난이학정，제출ACPSO-LSSVR：자괄응혼돈입자군ACPSO일방면능근거군체조숙수렴정도화개체자괄응치래조정관성권중，령일방면능근거혼돈변량적수궤성화편력성진행입자적초시화，가쾌우화과정，방지국부겁소。채용ACP-SO래우화LSSVR적대선삼수，제고부하예측적정도。실례분석험증료해방법적가행성화실용성。
In view of the existing chaos theory, the selection of the embedding dimension and delay time is difficult to determine, the choose of adjacent estimate point is not accurate enough. So an improved chaos theory is presen-ted. On one hand, embedding dimension and delay time are determined via the improved C-C theory, the accu-rate embedding dimension is determined according to the forecasting result. On the other hand, the adjacent esti-mate points are determined according to the evolution trend of reference points. In view of the parameters of LSSVR is difficult to determine, ACPSO-LSSVR is presented. ACPSO can adjust inertia weight according to the premature convergence degree and the individual fitness. The initialization of the particles is made according to the random-ness and ergodicity of chaotic variables. The parameters of LSSVR are optimized according to ACPSO, so the pre-cision of load forecasting is improved. The example analysis proves the feasibility and practicability of the method.