基于模糊推理和非线性模糊识别原理，讨论了从实际信号中检测识别主要影响因子，进而滤除干扰的方法，进行了相应的去噪试验。试验结果表明：由于模糊系统具有非线性、容错性和自适应学习等特性，因此，能够较为有效地辨识和检测出复杂非线性信号中的主要影响因子及其贡献大小。作为应用，研究了从观测资料中辩识El Niño/La Niña主要影响因子的诊断检测过程，并对 20世纪70年代以来出现的典型El Niño/La Niña事件中信风因子的影响作用进行了诊断检测和模糊识别，分析发现，70年代的几次ElNiño事件主要是由赤道西太平洋西风异常所触发，而80年代的几次El Niño事件（尤其是1982/1983年暖水事件）则主要是由赤道西太平洋与赤道东太平洋的信风活动异常共同所致，前者触发激励在先，后者巩固加强在后。

 Based on the principles of non-linear fuzzy inference and recognition，a method and approach identifying systemic chief influence factors from an actual signals and accordingly removing its interference,was studied and discussed, a relevant denoise experiments was also carried out.From our experimental results, it is shown that due to the affiliated advantages of fuzzy inference system such as non-linear, tolerance-error and self-adaptable, by fuzzy reasoning, we can easily identify and recognise the chief influence factors from a non-linear complicated system and effectively find out its contribution to the system.
As an application, based on appointed observational data, the research process of identifying chief influence factors forcing on El Niño/La Niña was explored , and the chief inducing/exciting effect of the pacific trade wind influencing on El Niño/La Niña events since 1970's were diagnosed and identified. It is shown that the notable El Niño events during 1970's were mainly induced by the west pacific west wind near the equatorial being abnormally stronger, on the other hand, the remarkable El Niño events during 1980's (especially in the 1982-1983's SST warming event) were chiefly leaded by the corporate effects of the equatorial trade wind abnormality both over the west and the east Pacific, generally, first exciting by the former, then strengthening by the latter.