iterative network 中文意思是什麼
iterative network
解釋
迭接網路-
3. developed simply and practical fluid network algorithm for large - scale of pipe networks, such as air - gas system and powder manufacture system. iterative computation used in this algorithm is not only astringing quickly and numerical calculating steadily but also cater the required ratio of precision and guarantee the real - time and any performance simulation of fluid network
3 、本文對風煙系統和制粉系統等大型管網，建立了簡單實用的流體網路模型演算法，此法在迭代計算中，不僅可以快速收斂和數值計算穩定，而且滿足一定的模擬精度，較好的保證了流體網路的全工況實時模擬。 -
An iterative method to estimate hurst index of self - similar network traffic
指數的迭代估計演算法 -
The two - stage modeling method takes into account the characteristics of software project risk management and software metrics data, integrates qualitative knowledge and quantitative data. to study the software project iterative process risk ’ s bayesian network model, the definition of cyclic bayesian network is presented, probability convergence property of directed cycle in cyclic bayesian network is proved and probability inference method is put forward
論文在軟體項目迭代過程風險的貝葉斯網路模型研究中，定義了有環貝葉斯網路，證明了有環貝葉斯網路中有向環的概率收斂性質，給出了有環貝葉斯網路的概率推理方法。 -
Cyclic bayesian network provides modeling method and inference algorithms for the management of software project iterative process risk
有環貝葉斯網路的研究，為管理軟體項目迭代過程風險提供了建模方法和模型求解演算法。 -
Temporal bayesian network and scheduling bayesian network provide modeling method and inference algorithms for the management of software project iterative process risk
時間貝葉斯網路和進度貝葉斯網路的研究，為管理軟體項目時間相關風險提供了建模方法和模型求解演算法。 -
The parameters ' fit of iterative learning control based on neural network
基於神經網路的迭代學習控制參數擬合 -
Simulation results indicate that the method is very effective to robotic system with unknown external disturbances, and it can also acquire satisfying tracking performance by fewer numbers of network training an d iterative learning processes
模擬結果表明，該方法對有未知外部干擾的機器人系統是十分有效的，且能以極少的網路訓練次數和迭代學習次數達到滿意的跟蹤性能。 -
Three duplication based task scheduling algorithms are presented. these algorithms have good performance, and its application on a real multi - core and multi - thread processor ( network processor ixp ) are demonstrated. it also show that how a uniform network programming environment could be built through address translation and iterative compilation techniques
然後結合一種實際的多核多線程處理器（網路處理器ixp ）展開了對任務調度實例化研究，並且運用地址轉換和迭代編譯等技術構建了新型統一網路編程環境，又結合實際網路應用提出了吞吐量與延遲相結合的網路任務調度演算法。 -
A dynamic approach for the minimization subproblem in alm method is discussed, and then a neural network iterative algorithm is proposed for general constrained nonlinear optimization. 3
使用增廣lagrange乘子法求解時，雖然可以避免罰參數無限增大的弊病，但同時也提出了一個難以求解的子命題。 -
As neural network has the ability of self - learning, that utilizes prior output data of uncertain system to estimate iteratively the static state property of system in order to achieve ideal approaching precision for identification of the positive model, a robust iterative learning control scheme on the basis of better positive model is designed. the neural network is used to identify the positive model of nonlinear system on iterative axis, which can give feed - forward action of iterative learning controller to reduce the effects of nonlinear properties and model uncertainties. meanwhile, feedback action of iterative learning controller make joint movement follow the desired trajectory on time axis by using controlled parameters derived by the neural network
由於神經網路具有自學習能力，它可利用不確定性系統的歷史輸出數據對系統的穩態特性進行估計，使得對系統正向模型的辨識達到理想的逼近精度，然後在此正向模型的基礎上進行學習控制律的設計：即採用神經網路辨識非線性系統的正向模型，並消除系統不確定性和外部干擾的影響，使關節運動沿迭代軸方向逼近期望軌跡；迭代學習控制器在線學習控制參量，使關節運動沿時間軸方向跟蹤期望軌跡。 -
In order to calculate asynchronous motor ' s mater temperature accurately, a mesh on the calculation area in the stator was made at first, builded the area ' s thermal network mathematics model secondly, used the gauss - seidel iterative method to calculate the mathematics model finally
摘要為準確計算定子溫升，對計算區域進行了合理的網格剖分，建立了異步電機定子的熱網路數學模型，採用高斯賽德爾迭代法對模型進行了求解。 -
The characteristic of method is, in every process of iterative learning, after obtaining better approaching precision of network training for model identification iteratively, the feed - forward action of iterative learning control law for the next trail is constructed by output signals of the neural network, and then integrated with feedback control to track the desired trajectory of robot in real time
該方法的特點是，在每一次迭代學習過程中，使神經網路訓練到對模型的辨識達到比較好的逼近精度后，利用神經元網路的輸出構造下一次迭代學習過程中控制律的前饋部分，再將它與實時反饋控制結合，形成本文提出的魯棒迭代學習控制演算法，並對機器人系統進行控制。 -
To overcome the common problems, difficulty of determining the optimal structure and slow training process, present in bp neural network, a novel non - iterative training algorithm for multilayer feedforward neural network has been proposed
為彌補與克服推斷測量的常用技術之一的神經網路中存在的隱節點難以11摘要確定和訓練速度慢問題，提出了一種可用於多層前向神經網路模型的非迭代快速訓練演算法。 -
The iterative learning control, the variable structure sliding mode control and neural network control are combined in complementary manner. the asymptotic convergence of the tracking error to zero is established
將迭代學習控制，變結構滑模控制，神經網路自適應控制以互補的方式相結合，使得跟蹤誤差漸近收斂于零。 -
But, pso convergence ' s speed become slow in latter iterative phase, and pso is easy to fall into local optimization. at present, some scholars improve base pso mostly using 3 methods : disperse algorithm, increase convergence speed, enhance particle ' kinds. in the paper, i put forward 2 methods aiming at local best resutl but not whole best result. i modify base pso using the last method. some scholars put forward times initializations, so i select best result after circulating some times to be a parameter of formula. first, put particle into some small region, and ensure every region having one paticle at least. second, every region ' s particle has probability transfer other regions. although increase running time, enhance particle ' kinds, decrese the probability of convergence far from whole best result. nerms ( network educational resource management system ) is one of the research projects in the science and technology development planning of jilin province. the aim of nerms is to organize and manage various twelve kinds of network educational resources effectively so that people can share and gain them easily and efficiently, so as to quicken the development of network education
但粒子群演算法仍存在如下不足:首先在多峰的情況下，粒子群有可能錯過全局最優解，遠離最優解的空間，最終得到局部最優解;其次在演算法收斂的情況下，由於所有的粒子都向最優解的方向群游，所有的粒子趨向同一，失去了粒子間解的多樣性，使得後期的收斂速度明顯變慢，同時演算法收斂到一定精度時，演算法無法繼續優化，本文對原始粒子群演算法提出了二點改進方案: 1 .演算法迭代到一定代數后，把此時找到的全局最優解當作速度更新公式的另一參數（本文稱之為階段最優解）再進行迭代; 2 .每次迭代過程中除最優解以外的每個粒子都有一定概率「變異」到一個步長以外的區域，其中「變異」的粒子在每一維上都隨機生成一個步長。 -
At present, the well - known theories and methods of intelligent control are : expert control, fuzzy control, neural network control, learning control ( including iterative learning control ), hybrid control and general theory of intelligent control system
目前智能控制基本形成了專家控制、模糊控制、神經控制、學習控制（包括迭代學習控制）及混合智能控制和智能控制的整體理論體系等幾個較成熟的理論和方法。 -
The learning algorithm is only based on linear least squares, and no iterative learning processes are needed. according to the demand of model precision, the algorithm determines the best weights of network and the minimal number of hidden nodes automatically
該學習演算法能夠根據擬合精度要求，運用線性最小二乘法確定相應的最佳網路權值和線性部分的參數，並自動確定最佳的隱層節點數。
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