recurrent system 中文意思是什麼
recurrent system
解釋
重復系統-
Activation of the complement may lead to killing of the microorganisms by direct lysis effect of complement system or the phagocytosis by the enhancement of the attachment of microbes to the phagocyte ( opsonization ). low serun concentrations of mbl in man is the basis for a common opsonic defect associated with recurrent infection
它可選擇性識別多種病原體表面的糖結構，以不依賴抗體和c1q的方式激活補體，發揮溶破和間接調理功能，還能與吞噬細胞膠凝素受體結合而起直接調理作用。 -
Neural network control is an important mode of intelligent control, and it is widely used in branches of control science, first, the architecture and the learning rule ( error back propagation algorithm ) of multiplayered neural network which is widely used in control system are presentedo especially, the paper refers to the architecture of diagonal recurrent neural network and its learning algorithm - - - - - recurrent prediction error algorithm because of its faster convergence with low computing costo next, before introducing the neural network control to the double close loop dc driver system, the controllers of current and velocity loop are designed using engineering design approach after analysis of the system, simulation models of the system are created
神經網路控制是智能控制的重要方式之一，它廣泛應用於自動控制學科各個領域。本文首先敘述了控制系統中常用的多層前饋網路結構及演算法（ bp演算法） ，特別提及了能夠較好描述系統動態性能的對角遞歸神經網路和在用遞推預報誤差演算法訓練drnn時取得了較快的收斂速度。其次，應用工程方法分析設計了tf - 1350糖分離機的電流、轉速雙閉環直流調速系統的控制器，作為引入神經網路控制的設計基礎，並建立了系統的模擬模型。 -
The common clinical manifestations of the primary agiitis of the central nervous system include burst of headache, dementia, change of aptitude, paralysis of cranial nerves, and recurrent focal depletion of the neural function
摘要原發性中樞神經系統性血管炎典型臨床表現為發作性頭痛、癡呆、精神智能改變、顱神經麻痹或反復局灶神經功能缺失。 -
Simple recurrent neural network control for non - minimum phase nonlinear system
非最小相位非線性系統的簡單遞歸神經網路控制 -
Estimate the non - recurrent and recurrent costs of the proposed system
成本估計擬議系統的非經常及經常成本。 -
Estimate with sufficient details the non - recurrent and recurrent costs of the proposed system
估計擬議系統的非經常及經常成本，並提供足夠的資料。 -
The hybrid control of a permanent magnet linear synchronous motor ( pmlsm ) servo - drive system using an adaptive recurrent neural network was put forward to solve the problem of poor stable precision in the servo system of packaging binding machine
摘要根據裹包機的驅動系統控制精度較差的問題，提出採用遞歸神經網路自適應混合控制線性同步電動器驅動機系統。 -
Excellence breeding technology and system have been built using dwarfing - sterile wheat and recurrent selection technology for longterm study and trying, and important progress have made in variety breeding, germplasm creation, transfer of characteristic inherit of dwarf male - sterile wheat and physiology bio - chemical etc
矮敗小麥育成后，經過中國農科院等科研單位多年的研究與摸索，逐步建立起一套科學、方便、實用的矮敗小麥輪回選擇技術體系，並在品種選育、種質創新、矮敗小麥遺傳特性轉移、生理生化等方面取得了很大的進展。 -
Then, as the substitute of the traditional pi controller, neuron adaptive psd controller is used to alternate the velocity loop of dc driver system, simulation results show that the super - shoot in new system is small and the performance in the way of anti - disturbance and anti - time - varying parameters are higher than the traditional pi controller and robustness of the dc driver system improved greatly, in addition, through analysis of the character of the neuron adaptive psd controller, the paper presents a neural network self - tuning control method for dc driver system in which the diagonal recurrent neural network is used to identify the plant to calculate the plant ' s sensitivity for psd controllers simulation results indicate that excellent static dynamic target was got with this control method and the performance of the system is improved greatly,
然後，應用單神經元自適應psd控制器改造調速系統的轉速環，代替傳統pi調節方式的轉速控制器。模擬表明，新系統在超調、抗負載擾動和參數時變方面性能優于的傳統的pi控制方式，系統的魯棒性增強。在分析單神經元自適應psd控制器特性后，本文提出用對角遞歸神經網路辯識控制對象，為psd控制器提供靈敏度參數，從而構成一神經網路自校正控制方案。 -
A speed - sensorless control method for bldcm, which applies recurrent fuzzy neural network ( rfnn ), is presented in this paper based on the dynamic model of bldcm. the rfnn controller is used as a speed controller to mimic the optimized output of the system
本文基於bldcm的動態模型提出了一種性能較好的遞歸模糊神經網路（ rfnn ）無速度傳感器bldcm控制方法，採用rfnn控制器作為轉速控制器來近似最優控制器輸出。 -
The simulation results show the good performance for the system by using network to adjust the parameters and the recurrent weight of neural network on - line dynamically on the condition of variety of system parameter and the impact of outside uncertainty factors
模擬結果表明，當系統參數動態變化或受到外部不確定因素影響時，利用神經網路來在線調整網路的隸屬函數參數以及神經網路遞歸權值，使系統具有良好的動靜態性能。 -
The software of diagnosis and prediction for boiler fault is developed by using fuzzy modular networks and recurrent composed networks, and the method of mixed knowledge representation and expert system technology etc are used in this paper
本文應用模糊模塊化神經網路和遞推合成bp網路，並結合混合型知識表示和知識獲取方法、基於知識的專家系統等技術對鍋爐故障診斷與預測問題進行了研究，開發了鍋爐故障診斷與預測軟體。 -
So the paper combined the fuzzy logic control and recurrent neural network, and the recurrent fuzzy neural network ( rfnn ) controller is introduced into the speedsensorless vector control system. moreover, an online parameter training methodology, which is derived from the lyapunov stability theorem and gradient descent method is proposed to increase the learning capability of the rfnn. the rfnn controller has a better performance than the pi controller system ; the effectiveness of the proposed control scheme is verified by simulation results
因此論文將模糊控制和具有優越動態性能的遞歸神經網路結合起來，取長補短，提出了一種遞歸模糊神經網路控制方法，利用神經網路來實現模糊推理，可動態的調整隸屬函數的形狀、位置以及神經網路遞歸權值，並對其與pi控制器的交流調速控制系統進行了模擬比較，模擬結果表明與普通的pi控制器相比較，遞歸模糊神經網路控制器有較好的動態性能，控制器的收斂速度快、靜差小，系統在遇到參數發生變化和外部不確定性問題時魯棒性、抗擾動性有明顯的提高。 -
The motor - drive loading system based on diagonal recurrent neural network
基於神經網路的三電平永磁同步電動機控制系統 -
Multistep predictive modeling of nonlinear system based on multilayer local recurrent neural networks
多層局部回歸網路的非線性系統預測模型 -
At first, theories about system identification are systematically reviewed, including its contents and process. at the same time, the classical system identification methods are summarized, the advantage and disadvantage of these methods are analyzed. secondly, the history of nn is briefly reviewed, and the structure, function and algorithms of feedforward nn and the recurrent nn are particularly introduced
本文首先介紹了系統辨識的內容，步驟和分類，對傳統的系統辨識方法作了總結，比較了幾種方法的優缺點；並回顧了神經網路的發展歷程，詳細介紹了前饋型網路與遞歸網路的結構、功能和演算法以及在實際應用中設計神經網路需考慮的問題。 -
Neural networks can be viewed as a universal approximator for nonlinear functions, but the multi - layer feed - forward neural network which be used usually is a static state network in nature, it is disagree with the real - time identification for dynamic system. moreover, recurrent neural networks can simulate the state memory mechanism of dynamic system, so it can be utilized as the model of dynamic time delay system
神經網路具有逼近任意連續非線性函數的能力，但常用的多層前饋式反傳網路本質上是一種靜態網路，不適合動態系統的實時辨識，而遞歸神經網路能夠實現對動態系統狀態記憶機制的模擬，因此更適合於作為動態時延系統的模型。 -
The predictive control of a nonlinear system with time - delay based on recurrent neural networks
基於遞歸神經網路的大滯后非線性系統預測控制 -
A dynamic nonlinear system is identified through recurrent nn using back propagation ( bp ) algorithm and dynamic bp algorithm respectively
然後利用遞歸網路對動態非線性系統進行辨識，並與傳統的預報誤差方法作了比較。 -
Recurrent neural networks - based multivariable system predictive control
基於遞歸神經網路的多變量系統預測控制
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