fnn fuzzy neural network 中文意思是什麼
fnn fuzzy neural network
解釋
模糊神經網路-
There are many types fuzzy - neural network at present. but in this paper, a new type of fnn structure is presented which is in form of series
模糊神經網路的種類很多，本文在總結模糊神經網路的發展與分類的基礎上提出了一種新型的模糊神經網路串聯式結合方式。 -
The theory of fuzzy neural network ( fnn ) modeling for nonlinear systems is presented
摘要論述了模糊系統和神經網路相結合的非線性系統辨識理論。 -
The new fnn controller not only has the fuzzy controller ' s characters of simple structure and easily - used, but also has the self - study ability of the elman network. thus it is valuable for research for non - linear system. in this paper, it summarizes the development of dc - dc converter firstly, and presents a new idea which is using fuzzy - neural network in dc - dc converter
這種串聯型模糊神經網路具有模糊控制的結構簡單、設計簡便及使用方便的特點，同時又利用了人工神經網路的聚類功能，使整個控制器又具備人工神經網路的自學習能力，因此這種串聯型模糊神經網路具有了良好的智能控制功能，特別適合於在非線性系統中的應用。 -
( 5 ) based on fuzzy control theory, fuzzy neural network ( fnn ) controller is introduced ; the changeable area and hierarchical genetic algorithm is used to training fuzzy neural network. simulation indicated that this designed fuzzy neural controller is effective
（ 5 ）在模糊控制的基礎上，提出了模糊神經網路控制器，並用本文提出的變區域多層遺傳演算法與bp相結合進行網路學習，在理論上證明了遺傳學習演算法的收斂性。 -
The structure of fuzzy neural network ( fnn ) is decided by the fuzzy rules. furthermore, fnn can be used to fit the smooth curves perfectly
而模糊神經網路（ fnn ）一旦給定模糊規則，其網路結構就固定，而且模糊神經網路能極為準確的擬合光滑數據曲線。 -
This paper presents the studies of fuzzy - neural network ( fnn ) control methods in the application of the full - bridge series type resonant dc - dc converter
本論文研究的是模糊神經網路在全橋串聯共振型dc - dc變換器控制中的應用。 -
This structure is kind of outer - coalescent. it adopts neural network ' s clustering function to classify the system output, then link a fuzzy controller in form of series which rules are made through the classified result. the design process and the experiment shows the well control effort to the full - bridge series type resonant dc - dc converter, at the same time the whole process avoids not only the modulating parameter process of the traditional method, but also the complexity of the traditional fnn design
然後本文又對全橋式串聯共振型dc - dc變換器的主電路的狀態方程進行了理論上的推導，從而確定了這種全橋式dc - dc變換器的各種工作模式；然後通過分別對模糊控制和神經網路進行介紹，提出了一種新型的模糊神經網路控制器模型，該控制器模型是一種外接式結合? ?串聯式結合方式，它利用了神經網路的聚類功能，採用了一種再生式神經網路，即elman網路來對系統輸出進行分類，然後根據分類結果設計模糊控制器，這樣就簡化了整個模糊神經網路控制器的設計過程。 -
After briefly introduce the basic genetic algorithm ( ga ) theory, aimming at the " prematurity " of basic genetic algorithm, we put forward a new improved genetic algorithm, the basic genetic algorithm combine simulate anneal ing ( gasa ), to meliorate the local search ability of basic genetic algorithm. because many design problems, such as the preliminary fuzzy rule and input and output membership fuction are hard to gain and the learni ng process of fuzzy neural network ( fnn ) is slow and local optimization, we design the fuzzy neural network excitation controllers of turbine generators with genetic algorithm combine simulate anneal ing ( gasa )
本文首先介紹了水輪發電機勵磁控制方式和軟計算理論的發展，然後介紹了遺傳演算法的基本理論，針對基本遺傳演算法存在的「早熟」現象，介紹了一種遺傳演算法結合模擬退火的改進型遺傳演算法，改善了基本遺傳演算法的局部搜索能力。鑒于常規模糊神經神經網路勵磁控制器設計方法中存在著初始模糊規則和輸入輸出隸屬度函數難以確定以及模糊神經網路訓練緩慢和難以達到全局最優等問題，利用遺傳演算法結合模擬退火的改進型遺傳演算法來設計模糊神經網路勵磁控制器。 -
Firstly, we aquire the initial fuzzy rules filtrate the initial fuzzy rules through genetic algorithm combine simulate annealing ( gasa ) ; then confirm input and output membership fuction through genetic algorithm combine simulate anneal ing ( gasa ) ; finally, the fuzzy neural network ( fnn ) is trained by genetic algorithm combine simulate anneal ing ( gasa )
首先利用改進型遺傳演算法得到控制器的初始模糊規則，並對得到的初始模糊控制規則進行過濾；然後利用改進型遺傳演算法對輸入輸出的隸屬度函數進行優化；最後利用改進型遺傳演算法對得到的模糊神經網路進行訓練。 -
A hybrid fuzzy neural network ( fnn ) controller based on genetic algorithm ( ga ) is proposed in this paper. the learning process of fnn is divided into off - line and on - line learning
在誤差較大時採用bang - bang控制，提高系統的響應速度；在誤差很小時，採用廣義的積分控制，以減少系統的穩態誤差。 -
It is difficult to design a traditional controller for some nonlinear systems, as some parts of the system are unknown. in this dissertation, the fnn and the traditional controller are combined to design two kinds of fnn adaptive controllers for a class of nonlinear plant. they are stable indirect adaptive controller based on fuzzy basic function network and stable direct adaptive controller based on t - s fuzzy neural network
由於傳統控制器本身的局限，它們在非線性控制系統的設計和應用中存在許多問題，本文將模糊神經網路和傳統控制策略相結合，設計了兩種模糊神經網路自適應控制器：基於模糊基函數網路的間接型穩定自適應控制器和基於t - s模糊神經網路直接型穩定自適應控制器。 -
Fuzzy neural network ( fnn ) is an active branch in the intelligent control. it is composed of the neural network and fuzzy logic system organically. and it is good at self - learning and self - tuning
模糊神經網路是將人工神經網路與模糊邏輯系統相結合的一種能處理抽象信息的網路，具有強大的自學習和自整定功能，是智能控制理論研究領域中一個十分活躍的分支，因此模糊神經網路控制的研究具有重要的意義。
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