【摘要】：搬運(yùn)介質(zhì)、搬運(yùn)方式、沉積環(huán)境和氣候等因素控制著沉積物粒度參數(shù)的變化,因此,沉積物粒度分析對(duì)揭示氣候變化和環(huán)境的演變具有重要意義。粒度參數(shù)中平均粒徑、標(biāo)準(zhǔn)偏差、偏差和峰值是沉積物粒度的四個(gè)重要參數(shù)。不同粒度組分的形成與搬運(yùn)均與沉積環(huán)境密切相關(guān),處理、分析粒度數(shù)據(jù)有利于進(jìn)一步確定沉積環(huán)境,這對(duì)于現(xiàn)代沉積學(xué)研究,乃至古代沉積物的沉積環(huán)境分析無(wú)疑都具有重要的理論和現(xiàn)實(shí)意義。模糊神經(jīng)系統(tǒng)是這樣一個(gè)模糊系統(tǒng),它在處理數(shù)據(jù)樣本過(guò)程中,使用某種學(xué)習(xí)算法去確定系統(tǒng)中的各項(xiàng)參數(shù)如模糊集和模糊規(guī)則,而該學(xué)習(xí)算法則由神經(jīng)網(wǎng)絡(luò)原理學(xué)習(xí)或激發(fā)出來(lái)的。利用模糊技術(shù)與神經(jīng)網(wǎng)絡(luò)的結(jié)合,我們可以充分發(fā)揮兩者的優(yōu)勢(shì),有效彌補(bǔ)各自的不足,這使得有關(guān)模糊神經(jīng)系統(tǒng)的研究得到追捧,在包括自動(dòng)控制、聚類(lèi)分析和模式識(shí)別等領(lǐng)域中已得到成功應(yīng)用,為人工智能的發(fā)展又增添了新的方向。本文是以諾姆·喬姆斯基提出的Chomsky體系文法為基礎(chǔ)的,通過(guò)引入模糊自動(dòng)機(jī)基本理論,簡(jiǎn)要概述了當(dāng)前模糊自動(dòng)機(jī)研究中有關(guān)神經(jīng)網(wǎng)絡(luò)技術(shù)的應(yīng)用情況,這其中主要是以模糊有限狀態(tài)自動(dòng)機(jī)、模糊文法和神經(jīng)網(wǎng)絡(luò)這三者的關(guān)系為主線的,而通過(guò)對(duì)相關(guān)的神經(jīng)網(wǎng)絡(luò)進(jìn)行訓(xùn)練,我們就可以抽取出所需的自動(dòng)機(jī),然后利用抽取的自動(dòng)機(jī)進(jìn)行文法的推導(dǎo),以及對(duì)沉積環(huán)境進(jìn)行判別分析。有關(guān)模糊神經(jīng)系統(tǒng)的研究與應(yīng)用已經(jīng)具有一定的規(guī)模,但對(duì)于本論文來(lái)說(shuō),主要是研究基于神經(jīng)網(wǎng)絡(luò)的模糊正則文法推導(dǎo)算法以及模糊自動(dòng)機(jī)的判別方法相關(guān)的問(wèn)題。在模糊正則文法推導(dǎo)中,訓(xùn)練用于抽取模糊自動(dòng)機(jī)的二階反饋神經(jīng)網(wǎng)絡(luò)的算法主要有實(shí)時(shí)遞歸學(xué)習(xí)算法(RTRL)和實(shí)數(shù)編碼基因遺傳算法(RCGA),但是這兩種算法存在通�。簳r(shí)間復(fù)雜度高,訓(xùn)練速度極慢。另外針對(duì)模糊有限態(tài)自動(dòng)機(jī)(FSA)樣本,兩種算法都有不同種類(lèi)的異常情況出現(xiàn),也就是說(shuō)泛化性很弱。最后,RTRL穩(wěn)定性很弱；RCGA早熟現(xiàn)象經(jīng)常出現(xiàn)。因而本文給出針對(duì)RCGA的改進(jìn)算法：Levesbeg-Marguard Genetic Algorithm(LMGA),改進(jìn)了訓(xùn)練速度以及解決了早熟的問(wèn)題；針對(duì)RTRL算法,第七章給出了Levesbeg-Marguard Back Propagation(LMBP)算法,不僅加快了訓(xùn)練速度,增加了吞吐量,而且還能處理特殊情況下的字符串,比如超長(zhǎng)串的情況。本文還給出了實(shí)驗(yàn)仿真與驗(yàn)證,并歸納了本文的結(jié)論。
[Abstract]:Factors such as transport medium, transport mode, sedimentary environment and climate control the change of sediment granularity parameters. Therefore, grain size analysis of sediment is of great significance in revealing climate change and environmental evolution. The average particle size, standard deviation, deviation and peak value of grain size are four important parameters of sediment granularity. The formation and transport of different granularity components are closely related to the sedimentary environment. Processing and analyzing granularity data are helpful to further determine the sedimentary environment, which is useful for modern sedimentology research. There is no doubt that the analysis of sedimentary environment of ancient sediments has important theoretical and practical significance. The fuzzy nervous system is such a fuzzy system, in the process of processing data samples, it uses some learning algorithm to determine the parameters of the system, such as fuzzy sets and fuzzy rules. The learning algorithm is studied or inspired by the neural network principle. With the combination of fuzzy technology and neural network, we can give full play to the advantages of both, effectively make up for their shortcomings, which makes the research on fuzzy nervous system become popular, including automatic control. Clustering analysis and pattern recognition have been successfully applied, which has added a new direction for the development of artificial intelligence. This paper is based on the Chomsky system grammar proposed by Norm Chomsky. By introducing the basic theory of fuzzy automata, the application of neural network technology in the research of fuzzy automata is briefly summarized. This is mainly based on the relationship among fuzzy finite state automata, fuzzy grammar and neural network. By training the related neural networks, we can extract the necessary automata. Then the extracted automata are used to derive the grammar and discriminate the sedimentary environment. The research and application of fuzzy neural system has a certain scale, but for this paper, it is mainly concerned with the derivation algorithm of fuzzy regular grammar based on neural network and the discriminant method of fuzzy automata. In the derivation of fuzzy regular grammar, the algorithms for training second-order feedback neural networks to extract fuzzy automata mainly include real-time recursive learning algorithm (RTRL) and real-coded gene genetic algorithm (RCGA),). The training speed is extremely slow. In addition, for the (FSA) samples of fuzzy finite state automata, the two algorithms have different kinds of abnormal cases, that is to say, the generalization is very weak. Finally, the stability of RTRL is very weak and the precocious phenomenon of RCGA often occurs. Therefore, this paper presents an improved algorithm for RCGA, called: Levesbeg-Marguard Genetic Algorithm (LMGA), which improves the training speed and solves the problem of precocity. For the RTRL algorithm, the seventh chapter gives the Levesbeg-Marguard Back Propagation (LMBP) algorithm, which not only speeds up the training speed, but also increases the throughput. It can also handle strings in special cases, such as long strings. The experimental simulation and verification are also given, and the conclusions of this paper are summarized.
【學(xué)位授予單位】：廣東工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：P512.2;TP301.1

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