中文摘要：

在一些非线性的动态系统，州的变量功能能通常与控制变量功能被分开，它把许多麻烦带到如此的系统的鉴定。很好解决这个问题，一个改进最少的广场支持向量回归(LSSVR ) 模型与多重核被建议，模型被用于非线性的可分离的系统鉴定。这个方法利用 Morlet 小浪内核功能的优秀非线性的印射能力并且把状态和控制变量信息合为一个内核矩阵。用合成小浪内核， LSSVR 包括二个非线性的函数，其变量是州的变量和控制这样，分别地，回归函数能获得更好非线性的印射的能力，和它能几乎在二次的连续不可分的空格模仿任何曲线。然后，他们被用来在可分离的非线性的动态系统识别二功能。模拟结果证明多重核的 LSSVR 方法罐头极大地比单个核方法，和 Morlet 小浪核改进鉴定精确性比另外的核更有效。

英文摘要：

In some nonlinear dynamic systems, the state variables function usually can be separated from the control variables function, which brings much trouble to the identification of such systems. To well solve this problem, an improved least squares support vector regression （LSSVR） model with multiple-kernel is proposed and the model is applied to the nonlinear separable system identification. This method utilizes the excellent nonlinear mapping ability of Morlet wavelet kernel function and combines the state and control variables information into a kernel matrix. Using the composite wavelet kernel, the LSSVR includes two nonlinear functions, whose variables are the state variables and the control ones respectively, in this way, the regression function can gain better nonlinear mapping ability, and it can simulate almost any curve in quadratic continuous integral space. Then, they are used to identify the two functions in the separable nonlinear dynamic system. Simulation results show that the multiple-kernel LSSVR method can greatly improve the identification accuracy than the single kernel method, and the Morlet wavelet kernel is more efficient than the other kernels.