1、simulated annealing

simulated annealing發(fā)音

英：　　美：

simulated annealing中文意思翻譯

常用釋義：模擬退火：一種隨機改進(jìn)算法

模擬退火法

simulated annealing雙語(yǔ)使用場(chǎng)景

1、Stochastic network can employ simulated annealing method to optimize parameters of equivalent circuits.───隨機網(wǎng)絡(luò )可利用模擬退火算法優(yōu)化等效電路參數.

2、The method of simulated annealing algorithm is used to solve the model.───并給出了模型求解的方法,通過(guò)模擬退火算法優(yōu)化求解該模型.

3、Simulated Annealing Tools Software complete source code can be directly used by the test.───模擬退火工具軟件完整的源代碼可以直接使用的考驗.

4、So the combination of genetic algorithm and simulated annealing could be sufficient to initial alignment for speed and precision.───因此，將遺傳算法和模擬退火算法結合起來(lái)，能很好地解決初始對準的速度和精度的問(wèn)題。

5、The simulated annealing algorithm is used to determine material parameters of fluid - saturated porous media response data.───本文研究了模擬退火算法在流體飽和孔隙介質(zhì)參數反演中的應用.

6、To optimize sub - job assignments, two simplified algorithms of simulated annealing are proposed.───為優(yōu)化子作業(yè)指派, 提出了兩個(gè)簡(jiǎn)化的仿真退火算法.

7、Self - adaptative simulated annealing is selected as optimization method for this thesis.───選擇自適應模擬退火算法作為本文的優(yōu)化方法.

8、The dissertation designed simulated annealing algorithms and hybrid algorithms for searching parameters during inversion.───將模擬退火算法和混合算法用于反演過(guò)程中的參數尋優(yōu).

9、So, simulated annealing and simplex combine to form a global hybrid method.───因此本文將快速模擬退火和下降單純形結合起來(lái),形成全局混合法.

10、In this paper, we present a speculative simulated annealing algorithm for task mapping.───本文提出了一種冒險模擬退火算法.

11、The strike price and the interest rate is solved, using simulated annealing.───最后, 應用模擬退火算法,求解貼現價(jià)格、履約價(jià)格.

12、To optimize discrete volume, a stochastic solution procedure via an improved simulated annealing algorithm is presented.───通過(guò)分段積分的方法,解決了柔度的最優(yōu)化問(wèn)題, 從而改進(jìn)了現有的模型.

13、So the simulated annealing algorithm is introduced to solving the material balance equation.───因此，本文引入模擬退火算法求解物質(zhì)平衡方程。

14、Very fast simulated annealing method ( VFSA ) is used in cross dipole anisotropy inversion commonly.───正交偶極子各向異性反演中一般采用快速模擬退火算法 ( VFSA).

15、The subproblem is solved by simulated annealing algorithm.───該子問(wèn)題可通過(guò)模擬退火算法來(lái)解決.

16、The simulated annealing is combined with genetic algorithm. The new tournament selection strategy is proposed.───通過(guò)將模擬退火算法嵌入到遺傳算法中，建立了一種新的錦標賽選擇策略。

17、This paper studies the simulated annealing algorithm for topology optimization of truss.───研究了平面桁架結構拓撲優(yōu)化設計的模擬退火算法.

18、A mathematical model for ferry schedule problem is developed and solved with simulated annealing algorithm.───建立了客輪調度問(wèn)題的數學(xué)模型,并用模擬退火算法求其數值解.

simulated annealing相似詞語(yǔ)短語(yǔ)

1、vaulted ceiling───拱形的天花板

2、simple ordering───線(xiàn)性序

3、simulated leather───仿皮革

4、simulated leathers───人造革

5、simultaneous translation───同聲傳譯；同步翻譯

6、simulating───n.模擬；假裝

7、self-annealing───自退火

8、simultaneous translations───同聲傳譯；同步翻譯

9、simultaneity───n.同時(shí)；[計][力]同時(shí)性；同時(shí)發(fā)生

2、最優(yōu)化理論與方法的目錄

第1篇線(xiàn)性規劃與整數規劃

1最優(yōu)化基本要素

1.1優(yōu)化變量

1.2目標函數

1.3約束條件

1.4最優(yōu)化問(wèn)題的數學(xué)模型及分類(lèi)

1.5最優(yōu)化方法概述

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2線(xiàn)性規劃

2.1線(xiàn)性規劃數學(xué)模型

2.2線(xiàn)性規劃求解基本原理

2.3單純形方法

2.4初始基本可行解的獲取

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3整數規劃

3.1整數規劃數學(xué)模型及窮舉法

3.2割平面法

3.3分枝定界法

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第2篇非線(xiàn)性規劃

4非線(xiàn)性規劃數學(xué)基礎

4.1多元函數的泰勒展開(kāi)式

4.2函數的方向導數與最速下降方向

4.3函數的二次型與正定矩陣

4.4無(wú)約束優(yōu)化的極值條件

4.5凸函數與凸規劃

4.6約束優(yōu)化的極值條件

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5一維最優(yōu)化方法

5.1搜索區間的確定

5.2黃金分割法

5.3二次插值法

5.4切線(xiàn)法

5.5格點(diǎn)法

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6無(wú)約束多維非線(xiàn)性規劃方法

6.1坐標輪換法

6.2最速下降法

6.3牛頓法

6.4變尺度法

6.5共軛方向法

6.6單純形法

6.7最小二乘法

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7約束問(wèn)題的非線(xiàn)性規劃方法

7.1約束最優(yōu)化問(wèn)題的間接解法

7.2約束最優(yōu)化問(wèn)題的直接解法

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8非線(xiàn)性規劃中的一些其他方法

8.1多目標優(yōu)化

8.2數學(xué)模型的尺度變換

8.3靈敏度分析及可變容差法

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第3篇智能優(yōu)化方法

9啟發(fā)式搜索方法

9.1圖搜索算法

9.2啟發(fā)式評價(jià)函數

9.3A*搜索算法

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10Hopfield神經(jīng)網(wǎng)絡(luò )優(yōu)化方法

10.1人工神經(jīng)網(wǎng)絡(luò )模型

10.2Hopfield神經(jīng)網(wǎng)絡(luò )

10.3Hopfield網(wǎng)絡(luò )與最優(yōu)化問(wèn)題

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11模擬退火法與均場(chǎng)退火法

11.1模擬退火法基礎

11.2模擬退火算法

11.3隨機型神經(jīng)網(wǎng)絡(luò )

11.4均場(chǎng)退火

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12遺傳算法

12.1遺傳算法實(shí)現

12.2遺傳算法示例

12.3實(shí)數編碼的遺傳算法

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第4篇變分法與動(dòng)態(tài)規劃

13變分法

13.1泛函

13.2泛函極值條件——歐拉方程

13.3可動(dòng)邊界泛函的極值

13.4條件極值問(wèn)題

13.5利用變分法求解最優(yōu)控制問(wèn)題

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14最大（?。┲翟?/p>

14.1連續系統的最大（?。┲翟?/p>

14.2應用最大（?。┲翟砬蠼庾顑?yōu)控制問(wèn)題

14.3離散系統的最大（?。┲翟?/p>

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15動(dòng)態(tài)規劃

15.1動(dòng)態(tài)規劃數學(xué)模型與算法

15.2確定性多階段決策

15.3動(dòng)態(tài)系統最優(yōu)控制問(wèn)題

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附錄A中英文索引

Part 1Linear Programming and Integer Programming

1Fundamentals of Optimization

1.1Optimal Variables

1.2Objective Function

1.3Constraints

1.4Mathematical Model and Classification of Optimization

1.5Introduction of Optimal Methods

Problems

References

2Linear Programming

2.1Mathematical Models of Linear Programming

2.2Basic Principles of Linear Programming

2.3Simplex Method

2.4Acquirement of Initial Basic Feasible Solution

Problems

References

3Integer Programming

3.1Mathematical Models of Integer Programming and Enumeration

Method

3.2Cutting Plane Method

3.3Branch and Bound Method

Problems

References

Part 2Non?Linear Programming

4Mathematical Basis of Non?Linear Programming

4.1Taylor Expansion of Multi?Variable Function

4.2Directional Derivative of Function and Steepest Descent Direction

4.3Quadratic Form and Positive Matrix

4.4Extreme Conditions of Unconstrained Optimum

4.5Convex Function and Convex Programming

4.6Extreme Conditions of Constrained Optimum

Problems

References

5One?Dimensional Optimal Methods

5.1Determination of Search Interval

5.2Golden Section Method

5.3Quadratic Interpolation Method

5.4Tangent Method

5.5Grid Method

Problems

References

6Non?Constraint Non?Linear Programming

6.1Coordinate Alternation Method

6.2Steepest Descent Method

6.3Newton?s Method

6.4Variable Metric Method

6.5Conjugate Gradient Algorithm

6.6Simplex Method

6.7Least Squares Method

Problems

References

7Constraint Optimal Methods

7.1Constraint Optimal Indirect Methods

7.2Constraint Optimal Direct Methods

Problems

References

8Other Methods in Non Linear Programming

8.1Multi Objectives Optimazation

8.2Metric Variation of a Mathematic Model

8.3Sensitivity Analysis and Flexible Tolerance Method

Problems

References

Part 3Intelligent Optimization Method

9Heuristic Search Method

9.1Graph Search Method

9.2Heuristic Evaluation Function

9.3A*Search Method

Problems

References

10Optimization Method Based on Hopfield Neural Networks

10.1Artificial Neural Networks Model

10.2Hopfield Neural Networks

10.3Hopfield Neural Networks and Optimization Problems

Problems

References

11Simulated Annealing Algorithm and Mean Field Annealing Algorithm

11.1Basis of Simulated Annealing Algorithm

11.2Simulated Annealing Algorithm

11.3Stochastic Neural Networks

11.4Mean Field Annealing Algorithm

Problems

References

12Genetic Algorithm

12.1Implementation Procedure of Genetic Algorithm

12.2Genetic Algorithm Examples

12.3Real?Number Encoding Genetic Algorithm

Problems

References

Part 4Variation Method and Dynamic Programming

13Variation Method

13.1Functional

13.2Functional Extreme Value Condition—Euler?s Equation

13.3Functional Extreme Value for Moving Boundary

13.4Conditonal Extreme Value

13.5Solving Optimal Control with Variation Method

Problems

References

14Maximum (Minimum) Principle

14.1Maximum (Minimum) Principle for Continuum System

14.2Applications of Maximum (Minimum) Principle

14.3Maximum (Minimum) Principle for Discrete System

Problems

References

15Dynamic Programming

15.1Mathematic Model and Algorithm of Dynamic Programming

15.2Deterministic Multi?Stage Process Decision

15.3Optimal Control of Dynamic System

Problems

References

Appendix AChinese and English Index