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

SBIR 第一阶段：使用支持向量机在存在干扰的情况下进行非线性建模

• 批准号: 0339249
• 负责人: Stephen Piche
• 金额: --
• 依托单位: Pegasus Technologies Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-01-01 至 2004-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0339249&HistoricalAwards=false
• 关键词: SBIR; Phase; Nonlinear; Modeling; Presence

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.

0339249这个小企业创新研究第一阶段的项目将研究开发非线性模型的基础上，受干扰的时间序列数据的新算法。Box-Jenkins算法在过去几十年中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）已被用来创建基于经验数据的精确的非线性模型。拟议SBIR研究将调查结合SVM建模方法与Box-Jenkins型干扰抑制技术。这样的方法在计算上将显著更高效，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经济、过程工业、工程、心理学和国防等领域都很常见，因此本文的研究具有广泛的适用性。