遺傳預測 的英文怎麼說

中文拼音 [yízhuànyùcè]
遺傳預測 英文
genetic anticipation
  • 遺 : 遺動詞[書面語] （贈與） offer as a gift; make a present of sth : 遺之千金 present sb with a gener...
  • 傳 : 傳名詞1 （解釋經文的著作） commentaries on classics 2 （傳記） biography 3 （敘述歷史故事的作品）...
  • 預 : Ⅰ副詞（預先; 事先） in advance; beforehand Ⅱ動詞（參與） take part in
  • 測 : 動詞1. （測量） survey; fathom; measure 2. （測度; 推測） conjecture; infer
  • 遺傳 : [生物學] heredity; hereditary; inheritance; inherit
  • 預測 : calculate; forecast; prognosis; divine; forecasting; foreshadowing; predetermination
  1. A definitive diagnosis, via renal biopsy, is therefore essential for prognosis and genetic counselling

    通過腎活組織檢查來確診，對于其推測其預后和遺傳咨詢是必要的。
  2. Cost forecast based on genetic programming

    基於遺傳規劃的費用預測
  3. Chapter 6 combines the genetic programming with aqmc optimization method to solve the prediction problems

    第6章結合遺傳程序設計方法和自適應擬蒙特卡羅優化方法用於預測問題。
  4. In this paper, the power transformer interior fault diagnosis technique based on the dissolved gas in oil analysis and the principles of genetic algorithm are analyzed. the forecasting models for power transformer interior fault are proposed based on the grey prediction model. the genetic algorithm is applied to estimating optimum coefficients of this forecasting model

    本文對基於變壓器油中溶解氣體分析（ dissolvedgasesanalysis ，簡稱dga ）技術的大型電力變壓器內部故障診斷技術和遺傳演算法原理進行了深入的分析，首次將灰色預測理論引入到大型電力變壓器內部故障預測工作中，運用遺傳演算法實現預測模型的優化，建立了基於遺傳演算法的變壓器內部故障改進灰色預測模型。
  5. ( 4 ) research on ann model joined with ga for area rainfall forecast the method is taken to join the genetic algorithm ( ga ) and bp algorithm together and supplementing mutually by optimizing the initial weights of ann with ga, and some application has been made in the binjiang basin for precipitation forecast

    （ 4 ）建立了基於遺傳演算法的降雨預報神經網路模型利用濱江流域的雨量站和周圍探空站的觀測資料，首次將遺傳演算法（ ga ）應用於流域面降雨量預報研究。
  6. Based on the discussions of the conventional and recent methods of short term load forecasting such as time series, multiple regression approaches and artificial intelligence technologies, this paper presents a hybrid short term forecasting model which combines the artificial neural network ( ann ) and genetic algorithm ( ga ). in order to improve the convergence speed and precision of the back - propagation ( bp ), a new improved algorithm - the adapted learning algorithm based on quasi - newton method is given

    本文首先分析比較了電力系統短期負荷預測的傳統方法時間序列法和回歸方法以及最近的專家系統和神經網路技術的優點和不足，然後針對人工神經網路bp演算法的不足對其進行了改進，採用了基於擬牛頓的自適應演算法，它提高了網路學習效率，具有較快的收斂速度和較高的精度。接著提出了改進的遺傳演算法來改善神經網路的局部收斂性。
  7. Genetic models were constructed for qtl mapping by two - dimensional searching. corresponding analysis methods were also proposed, which could estimate additive effects, dominance effects, epistatic effects of additive by additive, additive by dominance, dominance by additive, dominance by dominance, and could predict their interaction effects with environments

    構建了可以估汁加性效應、顯性效應、加加、加顯、顯加、顯顯上位性效應以及預測這些效應與環境互作效應的qtl定位兩維搜索遺傳模型，提出了相應的分析方法。
  8. Monte carlo simulations were conducted to study the new approaches of qtl mapping, the results indicated that general least squares ( gls ) method, which was widely applied in mixed linear model, could unbiasedly estimate all genetic main effects, including additive effects, dominance effects and epistatic effects of additive by additive, additive by dominance, dominance by additive, dominance by dominance. the interaction effects between genetic main effects and environments could also be predicted unbiasedly by linear unbiased prediction ( lup ). the heterosis prediction based on qtl effects was also unbiased

    對新提出的qtl分析方法進行了montecarlo模擬研究，結果表明，廣泛應用於混合線性模型的廣義最小二乘法（ gls ）能夠無偏估計加性效應，顯性效應以及加加、加顯、顯加、顯顯上位性效應等各項遺傳主效應；運用線性無偏預測法（ lup ）能夠無偏預測上述各項遺傳主效應與環境的互作效應；基於qtl效應的雜種優勢預測也是無偏的。
  9. Related topics include first principle based and genetic programming ( gp ) based modeling of refining process, predictive control, predictive inferential control, etc. main contents in the dissertation include : 1. based on the analyses of current status in the research of modeling and control of pulp refining process, some opening problems for developing its computer control system are pointed out

    研究領域包括打漿過程的機理建模、基於遺傳編程的打漿過程建模、預測控制、預測推斷控制等。本文主要研究內容： 1在系統綜述了打漿過程機理模型和打漿過程實時控制研究現狀的基礎上，指出了開發打漿過程計算機控制系統所需解決的幾個主要問題。
  10. 3. a new runoff forecasting model, based on the combination of genetic algorithm and neural network, is proposed, which integrate with the strongpoint of genetic algorithm and neural network. the accuracy and the speed of runoff forecasting are effectively improved that offered a new method for solving runoff forecasting problem

    3 .提出了基於遺傳演算法的神經網路洪水流量的預測模型，該模型綜合遺傳演算法和神經網路的優點，有效地提高了預測精度和速度，為洪水流量預報問題提供了一種新的方法。
  11. Thirdly, the weight and threshold of bp neural network model was optimized by genetic algorithm ( ga ), which has stronger macroscopic search and global optimization property, based on bp network model of the preparation of superfine quartz powder. this model is named ga - bp, and improves the generalization capability and the parameters forecast precision of bp network model, and was proved to be correct by both theoretical analysis and experiment

    再次，本文以粉石英制備的bp網路模型為基礎，利用遺傳演算法（ ga ）較強的宏觀搜索能力和良好的全局優化性能，對bp網路模型的權值和閾值進行優化，極大地提高了bp網路模型的泛化性能和參數預測精度，將經過ga優化后的bp網路模型簡稱為ga - bp網路模型。
  12. After a short - term load forecasting method based analogous and linear extrapolation is proposed, the load forecast and the priority of equipment action are led into static reactive power optimization. the aim function is constructed for the practical situation of power system. on the basis of traditional genetic algorithm the fitness function and the holding of population diversity are improved

    在提出基於相似日和線性外推的短期負荷預測新方法的基礎上，將負荷預測和設備動作優先級引入靜態無功優化中，並結合電網實際情況，構造了實用的目標函數，對遺傳演算法的適應度函數和群體多樣性的保持進行了改進，採用鄰域搜索運算元增加遺傳演算法的局部尋優能力。
  13. Technology combining ga with bp predicting financial parameter

    遺傳演算法耦合的金融參數預測研究
  14. In this method, ga is used to optimize connection weights of forward - back neural network until the learning error has tended to stability, then we use sp algorithm with optimized weights to finish short - term load forecasting process

    我們用遺傳演算法來訓練網路參數，直到誤差趨於一穩定值，然後用優化的權值進行bp演算法，實現短期負荷預測，模擬實驗結果表明該方法加快網路學習速度，並能提高負荷預測精度。
  15. Logging data as input of the network, the productivities of oil and gas in a layer of a meter as output of the network, the predict model for of genetic nerve network can be established according to the application of the theories of bp nurve network and genetic algorithms ; 4

    應用反饋神經網路理論及遺傳演算法理論，將測井數據作為輸入，每米採油指數及每米采氣指數作為輸出，建立遺傳神經網路儲層產能預測模型。
  16. 1 the forecast model of the load of the heating system was put forward, parmeter of the model was recognized in genetic algorithm. on the basis of the result, the temperature of water supply was calculated and become target aim of burning system

    針對集中供熱系統的特點，設計了整個系統的運行控制方案。 1建立了供熱負荷預測模型，採用遺傳演算法對模型的參數辨識，並以此來計算當天的供熱溫度，指導鍋爐的自動控制。
  17. Dry matter accumulation trends of the each individual organ were predicted under the different condition such as varieties, densities, fertilizer applications and sowing dates

    並以收獲指數作為遺傳參數調節不同品種器官的干物質分配比例，預測不同品種、不同密度、不同施肥、不同播期下各器官的干物質積累動態變化。
  18. In order to override the well - known limitation of back propagation algorithm, such as local grade problem, we suggest genetic algorithm, a global optimization algorithm, to optimize the weights set. the different parts of this model were modularized and combined as a prediction system

    通過對固定網路結構的權系值進行遺傳操作，優化網路的權系值組合，快速收斂到最優權系值組合，進而提高網路的分析預測效率和能力。
  19. We propose a combined slf method to extrapolate feeder load growth by using feeder ' s history peak value and the merits of gray theory and genetic programming ( gp ). at first, we adopt load transfer coupling method to correct load history and its error for load transfer. secondly, we get the real power - supply area by using layer overlap analysis, based on practical feeder path and distribution gis map layer

    將gis的空間信息分析功能應用於配網空間負荷預測的研究：綜合利用灰色理論及遺傳規劃（ geneticprogramming ， gp ）的優點，提出了一種根據饋線的歷史峰值負荷進行外推的組合slf法：首先採用負荷耦合回歸法來修正負荷歷史，消除由於負荷轉移引起的誤差；然後根據實際饋線路徑和配網gis圖形分層，運用圖層疊加分析得到饋線的實際供電范圍；接著採用灰色關聯度聚類方法對饋線負荷增長曲線進行聚類分析；最後採用gp來對灰色聚類結果進行符號回歸，分別得到每一類曲線的最佳擬合曲線形式。
  20. Bp algorithm can establish the high nonlinear mapping among the object and seismic attributes. ga algorithm can select the survival of the fittest

    神經網路可以建立屬性參數與預測目標之間的高度非線性映射，而遺傳演算法選擇適者生存。
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