非全局優化 的英文怎麼說
中文拼音 [fēiquánjúyōuhuà]
非全局優化
英文
no global optimization-
A novel dynamic evolutionary clustering algorithm ( deca ) is proposed in this paper to overcome the shortcomings of fuzzy modeling method based on general clustering algorithms that fuzzy rule number should be determined beforehand. deca searches for the optimal cluster number by using the improved genetic techniques to optimize string lengths of chromosomes ; at the same time, the convergence of clustering center parameters is expedited with the help of fuzzy c - means ( fcm ) algorithm. moreover, by introducing memory function and vaccine inoculation mechanism of immune system, at the same time, deca can converge to the optimal solution rapidly and stably. the proper fuzzy rule number and exact premise parameters are obtained simultaneously when using this efficient deca to identify fuzzy models. the effectiveness of the proposed fuzzy modeling method based on deca is demonstrated by simulation examples, and the accurate non - linear fuzzy models can be obtained when the method is applied to the thermal processes
針對模糊聚類演算法不適應復雜環境的問題，提出了一種新的動態進化聚類演算法，克服了傳統模糊聚類建模演算法須事先確定規則數的缺陷.通過改進的遺傳策略來優化染色體長度，實現對聚類個數進行全局尋優;利用fcm演算法加快聚類中心參數的收斂;並引入免疫系統的記憶功能和疫苗接種機理，使演算法能快速穩定地收斂到最優解.利用這種高效的動態聚類演算法辨識模糊模型，可同時得到合適的模糊規則數和準確的前提參數，將其應用於控制過程可獲得高精度的非線性模糊模型Aimed at multiple - limit, multiple - object, non - linear, discrete of voltage / var optimization and control, on account of whole evolution of evolutionary programming, no demand for differentiability of optimal function, and random search, it can obtain global optimum with mayor probability, this paper solve optimal function with evolutionary programming
在對優化的具體實現過程中，由於進化規劃著眼于整個整體的進化，對于所求解的優化問題無可微性要求，採用隨機搜索技術，能以較大的概率求解全局最優解的特點，針對電壓無功控制模型是一個多限制、多目標、非線性、離散的優化控制問題，因此應用進化規劃演算法進行模型的求解。An interval algorithm for problems of nonlinear equality constrained global optimization
非線性等式約束全局優化問題的區間演算法A global optimization algorithm for solving nonlinear programming problem involving many equality constraints
含有等式約束非線性規劃的全局優化演算法A main difficult in topology optimization is the singular optimum exists, which performs dimensions mutated and un - connectively in spatial domain ( the singular optimum point was hard to get ). so topology optimization is as regarded as a global optimization problem in concave feasible fields
拓撲優化的主要困難在於其可行域的奇異性，該奇異性表現為可行域在某些點維數突變及可行域非連通（而奇異點往往難以被搜索到） ，因此拓撲優化也可看作是在非凸可行域的全局最優問題。Based on polymerization reaction of the nylon - 6 rubberized cord fabric production of distributed control system in yangzhou organic chemical plant computer integrated manufacturing system ( yh - cims / dcs ), the multiple stepwise regression method was used to build the statistic mathematical models of the molecule weight and the monomer quantum of casting slice belt. then the optimization model of polymerization reaction was presented, which was solved by using simulation annealing algorithm to obtain the best techniques parameters. the improved hybrid genetic algorithm and back propagation algorithm are combined to train neural network, brought out the neural network prediction model of casting slice belt ' s average molecule weight to guide the technologist on - line
提出了流程工業生產過程操作優化策略和應用實施方法，包括生產過程離線優化策略、非線性問題求解策略、在線優化模型及學習策略；結合揚州有機化工廠計算機集成製造系統集散控制系統（ yh - cims dcs ）的實施，針對錦綸? 6浸膠南京理工大學博士學位論文摘要簾于布生產中己內酚胺聚合反應過程優化控制這一工程實際問題，採用統計建模方法，建立了聚合反應過程的優化模型；為求解所得的優化模型，提出了種改進的有約束條件下的模擬退火演算法，該演算法能避免陷於局部最優解，有效地提高了所求解的全局性和可靠性：提出了基於改進的ga演算法和sp演算法相結合的混合學習演算法，建立了基於神經網路的聚合反應過程生產目標在線預測模型，該演算法和模型滿足了生產中的實時性和實用性要求。Global convergence of the non - quasi - newton method for unconstrained optimization problems problems
基於非擬牛頓方法無約束最優化問題的全局收斂性The novel optimization algorithm, genetic algorithm popular these years, is introduced. genetic algorithm has the characteristics of multi - point searching, parallel computing and self - adaptive global optimization. so it is very suitable for the solution of complex engineering problems, which often have numerous variables, high dimension, highly nonlinear optimization objects, and the great solution spaces
引入了近幾年興起的新型優化演算法? ?遺傳演算法，其具有多點搜索、并行計算和自適應全局尋優的特點，特別適用於求解離散型設計變量多、維數高、優化目標高度非線性、解空間十分龐大的復雜工程問題。It improves on preventing the switching control process from oscillation and refusing compensation. the other is optimal switching control of the multi - tap capacitors formulated by nonlinear hybrid integer programming model
為了實現全局優化控制，本文將一個非線性混合整數規劃優化控制模型逐次線性化成增量形式后求解，即逐次線性規劃法。In consideration of the defects of conventional ga, an improved ga has been investigated in this paper. the evolution speed and quality of the population are directly influenced by the change of the number of chromosomes in ga and by whether the paternal excellent information is passed to the offspring as much as it can. in regard to the questions existing in ga, an algorithm with dynamic population scale is provided in this paper
在對模擬電荷配置的程序化實現方面，本文採用智能優化方法? ?遺傳演算法（ ga ） ，不過本文針對傳統ga - csm中遺傳演算法的定種群規模遺傳使得父本染色體的多樣性受到限制及同代非同父本進行繁殖、交叉、突變的遺傳方法可能收斂于局部極小值而得不到全局最優解的問題，對其加以改進，提出了一種新方法? ?變種群規模的遺傳演算法。It indicates that the genetic algorithm has much more superiority than traditional algorithm in global optimization, nonlinear optimization and multiparameter optimization
計算表明遺傳演算法在全局優化、非線性優化、多參數優化等方面表現出了傳統演算法無法比擬的優勢。A hybrid chaos optimazation algorithm for global optimization of nonlinear functions
非線性函數全局最優化的一種混沌優化混合演算法Nonlinear predictive control is realized by the global linear model based roll optimizing, and ontime adjusting using neural network based nonlinear model of the nonlinear system
全局線性模型用於滾動優化，非線性模型用於預測系統輸出和校正線性模型，實現非線性預測控制。Global optimal algorithm for linear programming problems subjected to nonlinear constraints
一種具有非線性約束線性規劃全局優化演算法In addition, in consideration of the non - convex and non - concave nature of the combinational optimization sub - problem, the branch and bound technique is adopted to obtain or approximate the global optimal solution
針對組合優化子問題的非凸非凹性，採用分支定界法，以求得或接近全局最優解。In this paper we reformulate gcp a sasystem of nonlinear equations, and the gcp is reformulated as unconstrained optimization problem, as for the optimization problem, the damped gauss - newton method algorithm of two kinds of steps is employed for obtaining its solution, and the global convergence analysis are given in this thesis
摘要本文將廣義互補問題轉化為一個非線性方程組問題，然後建立了gcp問題的無約束優化問題的轉化形式，對該優化問題，用兩種步長下的阻尼高斯牛頓演算法來求解，並給出了兩種情況下演算法的全局收斂性。Control systems in modern automatic engineering are nonlinear, time - changed and indefinite. lt is difficult to model by traditional method, even sometime impossible. under these circumstances we should apply model identification to gain the approximate model of object for effective control, there are many models to be chosen, fuzzy model is one of them, it is put forward with the development of fuzzy control. fuzzy model has characteristics of general approximation and strong nonlinear, it is fit for describing complex, nonlinear systems. to avoid rules expansion when the number of input values are very big. in this paper we apply hierarchical fuzzy model to resolve this problem, we also illustrate it has general approximation to any nonlinear systems. genetic algorithm is a algorithm to help find the best parameters of process. lt has abilities of global optimizing and implicit parallel, it can be generally used for all applications. in our paper we use fuzzy model as predictive model and apply ga to identify fuzzy model ( including hierarchical fuzzy model ), we made experiments to nonlinear predictive systems and got very good results. the paper contains chapters as below : chapter 1 preface
現代控制工程中的系統多表現為非線性、時變和不確定性，採用傳統的建模方法比較困難，或者根本無法實現，在這種情況下，要實現有效的控制，必須採用模型辨識的方法來獲取對象的近似模型，並加以控制，目前用於系統辨識的模型種類很多，模糊模型是其中的一種，它隨著模糊控制的發展而被人提出，模糊模型具有萬能逼近和強非線性的特點，比較適合於描述復雜非線性系統，為了解決模糊模型在輸入變量較多時規則數膨脹的問題，文中引入遞階型模糊模型，並引證這種結構的通用逼近特性。遺傳演算法是模擬自然界生物進化「優勝劣汰」原理的一種參數尋優演算法，它具有隱含并行性和全局最優化的能力，並且對尋優對象的要求比較低，在工程應用和科學研究中，得到了廣泛的應用，本文將遺傳演算法引入模糊模型的辨識，取得了很好的效果。Genetic algorithm is a highly collateral, random, self - adaptive, general and globe search algorithm, which simulates biologic evolution process. in this paper, genetic algorithm is applied to optimizing the model optimum in what is evaluated by projection pursuit algorithm
採用遺傳演算法對于投影尋蹤方法在評價過程中涉及到的模型優化問題進行優化，遺傳演算法是模擬生物「優勝劣汰」進化過程而形成的一種高度并行、隨機和自適應的通用性全局搜索演算法，能夠處理非線性較強的優化問題。In recent years, some non - linear stochastic optimistic algorithms are developed by biology, physics, artificial intelligence and nonlinear science, such as genetic algorithm, simulated annealing, tabu searching and chaos searching et al
近年來，基於生物學、物理學、人工智慧和一些非線性科學而發展了一些具有全局優化性能且通用性強的隨機搜索演算法，如：遺傳演算法、模擬退火、禁忌搜索和混沌搜索等。Abstract : according to the characteristic of hybridize and sudden change of binary coding chromosome in classical genetic algorithm, this paper defines the rule of hybridize and sudden change of continuous variables. the method is noted for easy to operate and quite good to approach the best true solution in overall situation. by means of three computed examples of nonlinear optimization and quadratic programme problem, it confirms that the method has strong adaptability and can precisely determine the best solution, in overall situation
文摘:根據傳統遺傳演算法中對於二進制編碼染色體的雜交和突變的特點，定義了連續變量的雜交、突變規則.此方法有操作簡便，可較好地逼近全局最優的真實解的特點.通過非線性優化和二次規劃問題的3個算例，證實本演算法適應性強，可較精確地確定全局最優解分享友人