Applications of Fuzzy Systems to Signal Processing

模糊系统在信号处理中的应用

• 批准号: 9419386
• 负责人: Jerry Mendel
• 金额: $ 20.73万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-04-01 至 1999-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9419386&HistoricalAwards=false
• 关键词: Applications; Fuzzy; Systems; Signal; Processing

For many practical problems, the information concerning the system under study is often represented in two forms: one is a set of input-output data pairs, and, the other is a set of linguistic descriptions about the system, often in the form of IF-THEN fuzzy rules. Traditional system identification methods and classification methods, even including the (model-free) neural network approaches, can only utilize the input-output data pairs. This research will continue to develop general methods to combine both input-output data pairs and linguistic IF-THEN rules into signal - processing systems designs. The new direction that this research is taking is the handling of uncertainty by using fuzzification in a fuzzy logic signal processor. The nonsingleton FLSs will be applied to a variety of important signal processing problems including fuzzy classification. The objectives are to: (1) establish a measure of uncertainty for the output of a nonsingleton FLS, in accordance with estimation theory practice; (2) develop a nonsingleton FLS state estimator for nonlinear dynamical systems; (3) extend fuzzy logic classifiers to include imprecise training samples and noisy measurements, using nonsingleton FLSs; (4) extend backpropagation and orthogonal least squares parameter training procedures to nonsingleton FLSs and classifiers; (5) apply nonsingleton FLSs to the forecasting of multiple time series and to adaptive filtering problems of noise cancellation and blind equalization; and, (6) apply nonsingleton fuzzy logic classifiers to modulation classification.

对于许多实际问题，所研究系统的信息通常以两种形式表示：一种是一组输入-输出数据对，另一种是一组关于系统的语言描述，通常以IF-THEN模糊规则的形式表示。传统的系统识别方法和分类方法，甚至包括（无模型）神经网络方法，都只能利用输入输出数据对。本研究将继续发展将输入-输出数据对和语言IF-THEN规则结合到信号处理系统设计中的一般方法。本文研究的新方向是在模糊逻辑信号处理器中应用模糊化来处理不确定性。非单态fls将应用于各种重要的信号处理问题，包括模糊分类。目标是：(1)根据估计理论实践，建立非单态FLS输出的不确定性度量；(2)建立了非线性动力系统的非单态FLS状态估计器；(3)使用非单例FLSs扩展模糊逻辑分类器，以包括不精确的训练样本和噪声测量；(4)将反向传播和正交最小二乘参数训练方法推广到非单态fls和分类器；(5)将非单态fls应用于多时间序列的预测以及噪声消除和盲均衡的自适应滤波问题；(6)将非单态模糊逻辑分类器应用于调制分类。