SBIR Phase I: Nonlinear Modeling in the Presence of Disturbances Using Support Vector Machines

这个小企业创新研究第一阶段项目将研究基于受干扰影响的时间序列数据开发非线性模型的新算法。在过去的几十年里，Box-Jenkins算法一直是开发受干扰影响的时间序列系统模型的标准方法。近年来，支持向量机（svm）被用于基于经验数据建立精确的非线性模型。提出的SBIR研究将研究将支持向量机建模方法与Box-Jenkins型干扰抑制技术相结合。这样的方法将大大提高计算效率，从而允许算法的商业化。由于受干扰影响的非线性时间序列系统的建模在包括经济学、过程工业、工程、心理学和国防在内的各个领域都经常遇到，因此提出的研究具有广泛的适用性。

0339249This Small Business Innovation Research Phase I project will investigate novel algorithms for developing nonlinear models based upon time series data that is affected by disturbances. The Box-Jenkins algorithm has been the standard approach over the past few decades for developing models for time-series systems that are affected by disturbances. In recent years, Support Vector Machines (SVMs) have been used to create accurate nonlinear models based upon empirical data. The proposed SBIR research will investigate combining SVM modeling approaches with Box-Jenkins type disturbance rejection techniques. Such an approach would be significantly more computationally efficient, thus, allowing commercialization of the algorithms. Because modeling of nonlinear time-series based systems that are affected by disturbances is commonly encountered across a wide variety of fields including economics, the process industries, engineering, psychology and defense, the proposed research has wide applicability.