The support vector machine has been widely used in binary classification applications because of the simplicity of its implementation. This article proposes an online identification method based on the support vector machine in the field of parameter identification. By substituting the constraint item into the original criterion function to form a new criterion function about the weight vector and the bias item, and then on the basis of the recursive identification methods, the newly established criterion function is minimized by using the Newton search to derive the Newton recursive support vector machine algorithm. Additionally, in order to improve the performance of the Newton recursive support vector machine algorithm, the multi-innovation identification theory is applied to optimize the algorithm, and a multi-innovation Newton recursive support vector machine algorithm is derived. In addition, the convergence and the calculation amount of the proposed algorithms are carefully analyzed in this article. Finally, a numerical simulation example is given to compare the Newton recursive algorithm and the corresponding multi-innovation recursive algorithm. The results show that the algorithms and optimization strategy studied in this article are effective.

中文翻译：

---

求解支持向量机回归问题的多创新牛顿递归方法

支持向量机因其实现的简单性而被广泛应用于二元分类应用中。本文在参数识别领域提出了一种基于支持向量机的在线识别方法。通过将约束项代入原始准则函数，形成一个关于权向量和偏置项的新准则函数，然后在递归识别方法的基础上，利用牛顿搜索将新建立的准则函数最小化，推导出牛顿递归支持向量机算法。此外，为了提高牛顿递归支持向量机算法的性能，应用多元创新识别理论对算法进行优化，并推导出多创新牛顿递归支持向量机算法。此外，本文仔细分析了所提出算法的收敛性和计算量。最后通过数值仿真实例对牛顿递推算法和相应的多创新递推算法进行了比较。结果表明，本文研究的算法和优化策略是有效的。