發(fā)布時間：2018-05-21 19:47

  本文選題：TSK模糊邏輯系統(tǒng) + 神經(jīng)網(wǎng)絡��； 參考：《遼寧工業(yè)大學》2017年碩士論文

【摘要】：模糊邏輯系統(tǒng)由于其應用廣泛,已成為學術和應用領域的研究熱點。模糊系統(tǒng)辨識包括結構辨識和參數(shù)辨識兩個方面,針對參數(shù)辨識,目前常采用最小二乘法、BP算法等,雖然方法很多,但都不是智能算法。本文將一型、A1-C1、A2-C0、A2-C1(A—規(guī)則前件,C—規(guī)則后件)區(qū)間二型TSK模糊邏輯系統(tǒng)融入到神經(jīng)網(wǎng)絡中,設計出四種模糊神經(jīng)網(wǎng)絡系統(tǒng),采用量子粒子群(QPSO)智能算法調整模糊邏輯系統(tǒng)的參數(shù),將所設計的智能系統(tǒng)應用于國際黃金價格和納斯達克綜合指數(shù)的預測中,并給出仿真研究。具體工作如下:(1)介紹了TSK模糊邏輯系統(tǒng)、神經(jīng)網(wǎng)絡以及QPSO算法的相關知識。(2)研究基于QPSO算法的一型TSK模糊邏輯系統(tǒng)的設計。將模糊邏輯系統(tǒng)融入到神經(jīng)網(wǎng)絡中形成五層模糊神經(jīng)網(wǎng)絡系統(tǒng),先用QPSO算法篩選規(guī)則,再用QPSO算法優(yōu)化系統(tǒng)參數(shù),最后將所設計的智能系統(tǒng)模型應用到國際黃金價格和納斯達克綜合指數(shù)的預測問題中,并與BP算法進行比較。仿真結果表明,所設計的一型智能系統(tǒng)是可行的、有效的。(3)在一型TSK模糊邏輯系統(tǒng)的基礎上,前件不變,后件變?yōu)橐恍湍：?得到A1-C1型區(qū)間二型TSK模糊邏輯系統(tǒng)。將模糊邏輯系統(tǒng)融入到神經(jīng)網(wǎng)絡中形成六層模糊神經(jīng)網(wǎng)絡系統(tǒng),先用QPSO算法篩選規(guī)則,再用QPSO算法優(yōu)化系統(tǒng)參數(shù),最后將所設計的智能系統(tǒng)模型應用到納斯達克綜合指數(shù)的預測問題中,并與BP算法進行比較。仿真結果表明,所設計的A1-C1型智能系統(tǒng)是可行的、有效的。(4)在一型TSK模糊邏輯系統(tǒng)的基礎上,后件不變,前件變?yōu)閰^(qū)間二型模糊集,得到A2-C0型區(qū)間二型TSK模糊邏輯系統(tǒng)。將模糊邏輯系統(tǒng)融入到神經(jīng)網(wǎng)絡中形成五層模糊神經(jīng)網(wǎng)絡系統(tǒng),先用QPSO算法篩選規(guī)則,再用QPSO算法優(yōu)化系統(tǒng)參數(shù),最后將所設計的智能系統(tǒng)模型應用到納斯達克綜合指數(shù)的預測問題中,并與BP算法進行比較。仿真結果表明,所設計的A2-C0型智能系統(tǒng)是可行的、有效的。(5)結合(3)和(4),前件為二型模糊集,后件為一型模糊集,得到A2-C1型區(qū)間二型TSK模糊邏輯系統(tǒng)。將模糊邏輯系統(tǒng)融入到神經(jīng)網(wǎng)絡中形成六層模糊神經(jīng)網(wǎng)絡系統(tǒng),先用QPSO算法篩選規(guī)則,再用QPSO算法優(yōu)化系統(tǒng)參數(shù),最后將所設計的智能系統(tǒng)模型應用到納斯達克綜合指數(shù)的預測問題中,并與BP算法進行比較。仿真結果表明,所設計的A2-C1型智能系統(tǒng)是可行的、有效的。對比四種TSK模糊邏輯系統(tǒng),由跟蹤效果圖及均方根誤差可知:區(qū)間二型模糊邏輯系統(tǒng)比一型模糊邏輯系統(tǒng)的誤差相對較小,具有更好的精度。
[Abstract]:Because of its wide application, fuzzy logic system has become a research hotspot in academic and application fields. Fuzzy system identification includes structure identification and parameter identification. The least square BP algorithm is often used in parameter identification. Although there are many methods, they are not intelligent algorithms. In this paper, the interval 2 TSK fuzzy logic system is integrated into the neural network. Four kinds of fuzzy neural network systems are designed, and the parameters of the fuzzy logic system are adjusted by using the Quantum Particle Swarm Optimization (QPSO) intelligent algorithm. The designed intelligent system is applied to the prediction of international gold price and NASDAQ composite index, and the simulation research is given. This paper introduces the TSK fuzzy logic system, neural network and the related knowledge of QPSO algorithm. The design of a type of TSK fuzzy logic system based on QPSO algorithm is studied. The fuzzy logic system is integrated into the neural network to form a five-layer fuzzy neural network system. QPSO algorithm is used to filter the rules, and then the QPSO algorithm is used to optimize the system parameters. Finally, the designed intelligent system model is applied to the prediction of international gold price and NASDAQ composite index, and compared with BP algorithm. The simulation results show that the first type of intelligent system is feasible and the effective. On the basis of the first type of TSK fuzzy logic system, the former part does not change, the latter part becomes a type of fuzzy set, and the A1-C1 type interval second type TSK fuzzy logic system is obtained. The fuzzy logic system is integrated into the neural network to form a six-layer fuzzy neural network system. QPSO algorithm is used to filter the rules, and then the QPSO algorithm is used to optimize the system parameters. Finally, the designed intelligent system model is applied to the prediction of NASDAQ composite index, and compared with BP algorithm. The simulation results show that the designed A1-C1 type intelligent system is feasible and effective. On the basis of the first type of TSK fuzzy logic system, the latter part remains the same, the former part becomes the interval 2 type fuzzy set, and the A2-C0 type interval 2 type TSK fuzzy logic system is obtained. The fuzzy logic system is integrated into the neural network to form a five-layer fuzzy neural network system. QPSO algorithm is used to filter the rules, and then the QPSO algorithm is used to optimize the system parameters. Finally, the designed intelligent system model is applied to the prediction of NASDAQ composite index, and compared with BP algorithm. The simulation results show that the designed A2-C0 intelligent system is feasible, and the effective TSK fuzzy logic system is composed of two types of fuzzy sets and one type of fuzzy sets. The A2-C1 type interval two type TSK fuzzy logic system is obtained. The fuzzy logic system is integrated into the neural network to form a six-layer fuzzy neural network system. QPSO algorithm is used to filter the rules, and then the QPSO algorithm is used to optimize the system parameters. Finally, the designed intelligent system model is applied to the prediction of NASDAQ composite index, and compared with BP algorithm. Simulation results show that the designed A2-C1 intelligent system is feasible and effective. Compared with four TSK fuzzy logic systems, the tracking effect diagram and root mean square error show that the error of interval type 2 fuzzy logic system is smaller than that of type 1 fuzzy logic system, and it has better precision.
【學位授予單位】：遼寧工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：O159;TP18

【相似文獻】

相關期刊論文 前10條

1 張小紅;何華燦;徐揚;;基于Schweizer-Sklar T-范數(shù)的模糊邏輯系統(tǒng)[J];中國科學E輯:信息科學;2005年12期

2 孫昌安;模糊邏輯系統(tǒng)建模函數(shù)的數(shù)學描述[J];沈陽航空工業(yè)學院學報;1998年03期

3 鄒麗;上下文相關模糊邏輯系統(tǒng)及其歸結方法[J];遼寧師范大學學報(自然科學版);2002年03期

4 王文麗;單榮立;劉林;;基于模糊球的模糊邏輯系統(tǒng)及其逼近性質[J];模糊系統(tǒng)與數(shù)學;2008年03期

5 卞扣成;蘭潔;王濤;;區(qū)間二型模糊邏輯系統(tǒng)在水位模糊控制中應用及仿真研究[J];遼寧工業(yè)大學學報(自然科學版);2010年06期

6 張興芳;;命題模糊邏輯系統(tǒng)中公式的理論可證度[J];河北師范大學學報(自然科學版);2007年04期

7 高芹;張興芳;王慶平;;命題模糊邏輯系統(tǒng)G銉d中公式的理論可證度[J];山東大學學報(理學版);2007年10期

8 史士英;李濤;張圣;尹義龍;;模糊邏輯系統(tǒng)的非線性組合預測方法與系統(tǒng)誤差分析研究[J];計算機工程與科學;2008年11期

9 李學鋒，黃萬偉，，周鳳岐;自適應模糊邏輯系統(tǒng)的聚類學習算法研究[J];控制與決策;1996年04期

10 孫多青,霍偉;線性多步法模糊邏輯系統(tǒng)(英文)[J];控制理論與應用;2001年06期

相關博士學位論文 前3條

1 張耿;概率模糊邏輯系統(tǒng)的理論研究及在復雜過程建模中的應用[D];中南大學;2012年

2 王文慶;基于模糊邏輯系統(tǒng)的復雜系統(tǒng)分析與控制[D];西北工業(yè)大學;2003年

3 岳菊梅;面向后件集的模糊推理機制及在Type-1與Type-2模糊邏輯系統(tǒng)中的應用[D];南開大學;2013年

相關碩士學位論文 前10條

1 王晉芳;模糊邏輯系統(tǒng)中的形式化證明[D];浙江理工大學;2016年

2 范秋楓;基于量子粒子群智能算法的模糊邏輯系統(tǒng)的設計及應用[D];遼寧工業(yè)大學;2017年

3 王文麗;模糊球及其在模糊邏輯系統(tǒng)中的應用研究[D];西安建筑科技大學;2008年

4 李玉姣;新的無規(guī)則模糊邏輯系統(tǒng)構造及控制應用[D];廣東工業(yè)大學;2014年

5 劉匯洋;若干廣義t-模及相關模糊邏輯系統(tǒng)[D];寧波大學;2010年

6 王明輝;區(qū)間2型模糊邏輯系統(tǒng)的魯棒性分析[D];東北大學;2011年

7 高子林;基于自適應模糊邏輯系統(tǒng)的非線性系統(tǒng)跟蹤及同步控制[D];廣東工業(yè)大學;2013年

8 閆鵬;二型模糊邏輯系統(tǒng)的降型與推理模型研究[D];大連海事大學;2010年

9 何義平;基于SS-三角模的模糊邏輯系統(tǒng)UL~*的若干問題研究[D];南昌大學;2007年

10 鮑成磊;區(qū)間二型TSK模糊邏輯系統(tǒng)的設計及應用研究[D];遼寧工業(yè)大學;2014年

本文編號：1920509