模化能力 的英文怎麼說
中文拼音 [móhuànénglì]
模化能力
英文
modelling power-
The result of using eight kinds of different yeast strain as biocatalyst reveals that baker ' s yeast is the best biocatalyst in this transformation
比較了八種酵母的生物轉化能力，發現在有機合成生物轉化中應用最廣泛的生物催化劑baker ' s酵母最適合催化該模型反應； 3In accordance with the problem that the fcm algorithm is quite time - consuming for search out cluster cancroids and may not be suitable for on - line modeling and control. this dissertation proposed an improved fuzzy identification method based multistage random sampling fuzzy c - means clustering algorithm ( mrfcm ). it has higher approximate precision and the cpu time has slowed down sharply compared with the common fuzzy
Johnyen和liangwang介紹了幾種應用於模糊模型的信息優化準則，本論文在此基礎上對統計信息準則進行一些改進，並與快速模糊聚類和正交最小二乘方法結合，提高了模型的辨識精度和泛化能力。Though the method has generalization capability, it doesn ’ t satisfy the application. when the examples include yawp, wrong recognition occurs. generalization of the traditional is not ideality and instability
神經網路模式分類方法雖然具有比其他分類方法更好的泛化能力，但在實際應用中，仍然不能滿足人們的期望。One of the main problems in pdes is how to partition the network simulation workload to decrease the time needed to complete the simulation and improve performance of simulation. here a new optimized partition algorithm was put forward, which first analyses the performance factors of parallel simulation and then constructs a performance estimation model for partition ; based on this model, it mends the graph partition algorithm to consider all factors, including simulation applications and simulation environments. optimized factors are workload balance 、 communication cost and time window of lookhead
鑒于基於傳統圖劃分演算法的任務劃分工具存在諸多不足，本文提出了并行網路模擬任務的優化劃分方法，其思想是:首先從pdes機制出發，分析影響并行網路模擬性能的各種因素並建立一個能夠綜合考慮各種因素的并行網路模擬性能估計模型;其次，改進多級圖劃分演算法，使得演算法具有綜合考慮模擬應用與模擬運行環境，同時在優化過程中使用性能估計模型指導劃分，實現對影響并行網路模擬性能的三個因素（包括負載均衡、通信開銷、安全時間窗口長度）的優化能力;最後，結合併行網路模擬性能估計模型與改進的多極圖劃分演算法，實現了并行網路模擬任務的優化劃分。Company has established a production specialization, standardization work, the scientific management of the modern enterprise management system, and formed a production capacity of 12 million pieces of clothing size and capacity
公司目前已建立了生產專業化、工作規范化、管理科學化的現代企業管理體系，形成了年產1200萬件服裝的生產規模和能力。Knowledge value chain describes new ways of envisioning the strategic process, which states that business strategies cannot be effectively shaped without a deep understanding of the knowledge capability interdependencies between core activities along the value chain as a core capability of innovation, new product development ( npd ) always makes great contribution to organizational performance. this dissertation proposes a framework of the link between km process capabilities and npd performance aiming at identifying the key facilitators of npd and enabling companies to launch their products into market more successfully than their competitors. attempts are made to measure km capabilities in terms of knowledge acquisition and dissemination and to use the results of empirical study for leveraging an organization against its competitors in the context of npd
本論文以文獻整理的研究方法，對既有的知識管理理論進行梳理；通過對十個較具代表性的知識管理理論框架的比較分析，以知識管理過程能力、知識管理基礎資源能力、知識管理應用能力及知識場為基石，構建了知識管理能力的框架模型；通過對產業結構為本觀點和組織資源為本觀點的比較分析，探討了競爭優勢產生的根源，創造性地構建了知識價值鏈模型，以解釋企業的核心能力；論述了以知識管理能力為主的組織特徵和相應的組織化能力匹配權變情況，即知識分佈與權力分佈的關系，分析了技術在知識管理能力領域的作用；以個案研究的方法探討了知識管理組織化能力和知識管理技術化能力；以實證研究的方法，對知識的獲取和擴散與新產品研發的績效互交關系進行了研究，以理解知識管理過程能力對業務創新產生主導影響的機制。Finally, take example for a non - linear function, method mentioned in this paper is used to design wavelet neural network to approximate this function. the computer simulations confirm the method that is brought out in this paper is useful, and prove that wavelet neural network has not only fast convergence and better precision of approximation, but also good capability of forecasting and escaping error
最後，對於一個實際的非線性函數，用本文介紹的方法來設計小波神經網路來逼近函數，模擬結果表明該方法的有效性，並且表明小波神經網路在函數逼近上，網路的收斂速度快，逼近精度高的特點，並且網路具有很好的泛化能力和容錯性。Based - w on uml, we use uml extend mechanism to process specific property develop about workflow : customize stereotype such as contract, port, collaborate, etc. and set up contract hierarchical system structure. for strict mathematical analysis and simulation, we also provide a convertible mechanism from uml model to object petri nets and a object petri nets ? formal analysis method. analyze the complicated dynamic relation among wofld7low ( work item ) in distributed workflow system by object petri nets ? strong analysis and simulation capability
我們以uml為基礎，利用uml的擴展機制進行了有關工作流特性的擴展：定製了合同、埠、通訊協議塊等版類，建立了合同化的層次體系結構；為了對建立的模型進行嚴密的數學分析和模擬，我們又提供了從uml模型到對象petri網的轉換機制和對象petri網的形式化分析方法，利用對象petri網強勁的分析模擬能力來分析分散式工作流系統中工作流（工作任務）之間的復雜的動態關系；根據分析結果可以對模型進行有效地改進，最後確定的模型容易實現到程序代碼的轉換。While putting rough set theory into practice, this thesis pays attention to setting - up the proper data structure. in order to improve the data utilization ratio and promote rule quality, this thesis puts forward the method of " divide equally and examine each other this thesis bring forward the method of dynamic reduce to overcome data noise and confirm the best reduction finally with the help of rosetta tool software we apply the above concept and method to reality, and succeeded in obtaining the optimum rule for the expert system of production scheduling in daye iron ore mine, wuhan iron and steel company
由於標準粗糙集模型對數據噪音高度敏感以及工程應用中數據噪音引入的不可避免性，標準粗糙集模型在實際應用中存在一系列問題，為克服數據噪音以及規則泛化的需要，本文採用變精度模型，由此模型引入近似約簡方法。本文在將粗糙集理論及變精度粗糙集模型應用於實際的同時，注重研究了適當數據結構的建立。為提高數據利用率，提升規則生成的質量，提出了平分互測規則集泛化能力考核方法。3. the modeling method based on mechanism analysis and identification method always exits unmodeled high - order part and the modeling method based on neural networks usually has not good enough generalization capability. we fuse above two kinds of modeling method and put forward a hybrid modeling method based on mechanism analysis, identification and rbf neural networks. this paper proposed a hybr id modeling method based on mechanism analysis, identification and rbf neural networks. first, get a object ' s low - order model by the mechanism analysis and identification method. second, adopt rbf neural networks modeling method to compensate unmodeled high - order model. the sum of the low - order model and high - order model is the hybrid model. this kind of hybrid model has more accuracy than a model based on mechanism analysis and identification and has more generalization capability than a model based on neural networks
針對基於機理分析和辨識的建模方法總是存在未建模的高階部分，精度不夠高和神經網路建模方法泛化能力差的缺點，將這兩種建模方法進行融合，提出基於機理分析、系統辨識和rbf神經網路的混合建模方法，首先採用機理分析和辨識的方法得到工業對象的低階模型，再用rbf神經網路建模方法補償未建模高階模型，這樣得到的混合模型，比單純基於機理分析和辨識的建模方法具有更高的精度，比單純的神經網路的建模方法具有更好的泛化能力。Their learning and training rules have been analyzed profoundly and their abilities to approximate arbitrary nonlinear function have been testified and compared by the simulation. a new rbf neural network has been presented which uses a raised - cosine function as activation transfer function. it provides a wider generalization in comparison with gaussian rbf neural networks by simulation as well as strong approximation ability, fast convergence, a rule to select the parameters of the networks
本文詳細研究了兩種典型的前向神經網路（ bp網路和rbf網路）的學習和訓練演算法，提出了一種新穎的基於緊支集餘弦函數的徑向基神經網路，其克服了常用的高斯型rbf神經網路雖具有緊支集但各基函數非正交的不足，其收斂速度快、網路參數選取有理論依據且相比于高斯型rbf神經網路具有更強的泛化能力，模擬驗證了其有效性。In this paper, a bogused vortex with tilted vertical structure is firstly taken into account. check results of experiment b, experiment bt and experiment nb, we find that the simulated tc track, center pressure and pattern of precipitation during landing in experiment b are better than others. so, a bogused vortex with asymmetrical horizontal structure and tilted vertical structure is good for a tc moving in mid - latitude, which may be a new way to initialize this kind of tc in numerical simulation
本文首次採用傾斜垂直結構的人造熱帶氣旋作為初始渦旋，試驗b和試驗bt以及試驗nb的模擬結果對比發現，前者對9806號熱帶氣旋登陸前後的運動路徑、中心氣壓、降水分佈和初始結構的模擬能力優於後兩者，因此，具有傾斜垂直結構的人造熱帶氣旋比正壓結構的人造熱帶氣旋更適合於作為模擬中緯度運動的熱帶氣旋的初始渦旋，從而提出解決該類熱帶氣旋初始化問題的一種新思路。Then the author has constructed the submodel of the time of blowing oxygen and refrigerant, making use of the rbf neural networks. meantime, applying the genetic algorithm, completed the optimizationof parameter of the number of neuron and spread, and get the model with a better generalization ability
然後分別應用rbf神經網路建立吹氧時間子模型、冷卻劑子模型，並結合遺傳演算法完成rbf神經網路本身其神經元個數、 spread等參數的優化，使模型具有較強的泛化能力。Everyone got better at imitation, intensifying the pressure to enlarge the brain still further in a kind of cerebral arms race
等到每個人都有很強的模仿能力后，使腦子變得更大的演化壓力就會加強，啟動了提升腦量的軍備競賽。Ownership - advantages, integration - ability, location advantages and speed advantages are the four dynamic reasons of transnational m & a, they lead to the emergence and quick development of transnational m & a from different and interrelated angles, just these different and interrelated dynamic relations form the dynamic model which comprehensively explains the dynamic reasons of transnational m & a
摘要所有權優勢、內部化能力、區位優勢和速度優勢是跨國並購的四大動因，它們對跨國並購的產生和迅速發展起著各自不同又相互關聯的作用，正是這種各自不同又相互關系的動態關系形成了對跨國並購進行解釋的動因模型。This paper researches the numeric approximation characteristic of series - parallel fuzzy system and points out that the number of fuzzy rules should not exceed the number of the samples. in addition, the influence of approximation error and system initial error on the performance of the series - parallel fuzzy system is also investigated
本文研究了串並聯方式模糊系統的數字逼近特性，得出結論：當模糊規則數等於樣本數時，已經可以實現精確插值，因此模糊規則條數不能超過樣本數目，否則將冗餘，並可能引起振蕩，削弱模糊系統的泛化能力。It is shown that using backward selection algorithm select centers can minimize the net work output error in great extent, make network structure more simple and more generalization and approximation. furthermoure, the offline control to pangang hot metal desulfuration process is realized using rbfnn model
理論研究和模擬實驗證明： rols法比起批處理正交最小二乘法（ ols ）所需的計算空間小，訓練速度快；用後向選擇法選擇中心能最大程度地減小網路的輸出誤差，使網路結構簡化，並且具有更強的泛化能力和逼近能力。Results showed csa had strong ability of function optimization and maintaining diversity. it was one of effective algorithms for multi - modal functions and non - stationary functions optimization
研究結果表明，該演算法優化能力和保持模式多樣性的能力較強，能夠獲得較好的多模態函數和時變函數優化效果。To overcome the difficulty in determining the rbf center numbers and spread, a kind of generalized genetic algorithm is introduced, which follows the analysis of the basic rules of genetic algorithm. the new hybrid algorithm determines the center numbers and spread adaptively to reach the optimal tradeoff between the training accuracy and the generalization, so it increases the prediction accuracy of the model
針對建模過程中出現的rbf中心和寬度難以確定的難點，在分析遺傳演算法機理和基本演算法的基礎上，提出了使用廣義遺傳演算法對rbf神經網路模型的中心和寬度進行自適應選擇，從而達到模型訓練精度和范化能力的一個最優的平衡，從而提高模型的預報精度。With applying bpann, the model of tactic selection can be put in practice. the key problem of applying bpann is efficiency of arithmetic, which is solved by comparing efficiency of arithmetic in different arithmetic
採用前向人工神經網路（ bpann ）實現策略選擇模型，主要分析bpann原始數據處理問題? ?指標體系可靠性，在matlab6 . 0環境下分析bpann的演算法和泛化能力。分享友人