【摘要】：直覺(jué)模糊集作為模糊集概念的推廣,由于它借助隸屬度和非隸屬度來(lái)刻畫(huà)事物的模糊性,可同時(shí)表示模糊現(xiàn)象中的“支持”、“中立”和“反對(duì)”三種狀態(tài),因此在模式識(shí)別、機(jī)器學(xué)習(xí)及圖像處理等領(lǐng)域得到廣泛的應(yīng)用.作為模糊推理的推廣,直覺(jué)模糊推理的核心問(wèn)題是求解直覺(jué)Fuzzy Modus Ponens(IFMP)和直覺(jué)Fuzzy Modus Tollens(IFMT)問(wèn)題,而某種推理算法能否在實(shí)際中得到應(yīng)用取決于該推理算法性質(zhì)的優(yōu)劣,本文主要研究了直覺(jué)模糊推理三Ⅰ算法的相關(guān)性質(zhì),主要內(nèi)容如下:(1)在有限論域的情形下,利用模糊集間的Hamming距離定義了直覺(jué)模糊集之間的Hamming距離,從而建立了有限論域上的直覺(jué)模糊度量空間.針對(duì)無(wú)窮論域,利用模糊集間的自然距離定義了兩種直覺(jué)模糊集之間的自然距離,從而建立了無(wú)窮論域上的直覺(jué)模糊度量空間.(2)在直覺(jué)模糊度量空間中研究了Lukasiewicz型直覺(jué)模糊推理三Ⅰ算法的連續(xù)性和逼近性.(3)在直覺(jué)模糊度量空間中研究了Lukasiewicz型直覺(jué)模糊推理三Ⅰ算法的魯棒性.
[Abstract]:As an extension of the concept of fuzzy sets, intuitionistic fuzzy sets can represent the "support", "neutrality" and "opposition" of fuzzy phenomena by virtue of membership degree and non-membership degree to describe the fuzziness of things, so they are used in pattern recognition. Machine learning and image processing are widely used. As a generalization of fuzzy reasoning, the core problem of intuitionistic fuzzy reasoning is to solve intuitionistic Fuzzy Modus Ponens (IFMP) and intuitionistic Fuzzy Modus Tollens (IFMT) problems. Whether a certain reasoning algorithm can be applied in practice depends on the properties of the reasoning algorithm. In this paper, we mainly study the related properties of the three 鈪，

本文編號(hào)：2378081