moga 中文意思是什麼

moga 解釋
莫高
  1. Then, multi - objective genetic algorithm ( moga ) was used to search for the optimal parameters of the module and step width of motion

    在第三章中首先引入了移動機器人規劃環境的矢量場模型，然後利用變權重多目標遺傳演算法對規劃參數進行優化。
  2. Modeling of fermentation process based on moga and svm

    的發酵過程建模
  3. Multi - objective genetic algorithm ( moga ) is a method that can optimize multiple objectives effectively and parellelly

    而多目標遺傳演算法是一種能夠同時高效、并行地優化多個目標函數的優化演算法。
  4. The results indicates that the fuzzy control and moga has wide prospect in the aspect of control of the aero - engine

    研究結果表明，模糊控制以及多目標遺傳演算法在航空發動機控制中具有廣闊的應用前景。
  5. In this dissertation, the basic theory, and its applications in aero - engine control system design of fuzzy control, fuzzy pid control, and moga is studied

    本文對模糊控制、模糊pid控制和多目標遺傳演算法的基本理論以及在航空發動機控制系統中的應用進行了較為全面的研究。
  6. This paper presents a critical review of moga " current researches mainly in the last 15 years. the multi - objective optimization techniques have two branches, one with parameters and another with no parameters

    本文對近十五年來多目標遺傳演算法的國內外研究現狀進行了較全面地闡述，其優化方法大致分為兩大類：帶參數的方法和不帶參數的方法。
  7. Key techniques and main measures that apply genetic algorithm ( ga ) to multiobjective search are studied. based on the comparison of various multiobjective optimization ga ( moga ), a new algorithm called improved pareto ga ( ipga ) that combines the nsga - ii and local search algorithm is presented

    研究了遺傳演算法應用於多目標搜索的關鍵技術及主要解決措施，比較分析了幾種主要的多目標遺傳演算法的優缺點，提出了一種改進的多目標遺傳演算法（ ipga ） 。
  8. Chapter 4 gives a full - scale study and analyse about ga and moga, presents the main idea of ga and moga, analyses a typical moga. the fuzzy controller parameters of an aero - engine model is optimized by nsga - ii in all possible operation points of two working condition. the result shows good dynamic and stable performance

    然後，使用先進多目標遺傳演算法nsga - ii對航空發動機模糊控制系統的參數進行了兩個飛行狀態下所有可行工作點的優化選取，各個工作點的優化結果表明模型的控制系統響應速度快、超調量小、穩定效果佳。
  9. It is widely applied to the domain of combinational evolutionary problem seeking, self - adapt controlling, planning devising, machine learning and artificial life etc. however, there are multi - objective attributes in real - world optimization problems that always conflict, so the multi - objective genetic algorithm ( moga ) is put forward. moga can deal simultaneously with many objections, and find gradually pareto - optimal solutions

    由於現實世界中存在的問題往往呈現為多目標屬性，而且需要優化的多個目標之間又是相互沖突的，從而多目標遺傳演算法應運而生，它使得進化群體并行搜尋多個目標，並逐漸找到問題的最優解。
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