genetic learning 中文意思是什麼

genetic learning 解釋
遺傳學習
  • genetic : adj. 1. 遺傳（學）上的。2. 發生的，發展的；創始的。adv. -ically
  • learning : n 學，學習；學問，學識；專門知識。 good at learning 善於學習。 a man of learning 學者。 New learn...
  1. Features learning activities about plants, space geology, veterinary science, parasites, ecology, sea otters, pollen, and using a virtual telescope to view genetic material

    -藉由動畫漫畫游戲等方式，提供關于海洋的故事介紹海洋生物海洋生物常識問答保育資訊等內容。
  2. It is very important to estimate the basic parameters in helicopter preliminary design. neural network ( nn ) has the advantages in estimating accuracy and generalization over traditional methods. however, there are some difficulties in using nn, e. g., how to select a proper network structure and the number of hidden layers. in this paper, structure and connection weight of a three - layer nn are optimized by genetic algorithm, and the optimized network is applied to helicopter sizing. the proposed method can not only give an optimal nn structure and connection weight, but also reduce the prediction error and has the capability of self - learning when the latest data are available. furthermore, this method can be easily applied to helicopter design systems

    在直升機初步設計階段估算其基本參數是很重要的.神經網路的通用性和精度比傳統的估算方法有更多的優勢，但是在應用神經網路時存在如何選擇合適的網路結構和隱層節點數目等一些困難.應用遺傳演算法優化三層神經網路結構和連接權重，並將優化得到的網路應用於直升機參數選擇中.該方法不但可以給出一個最優的神經網路結構和連接權重，而且降低了估算誤差，具有及時應用最新數據學習的能力.此外，該方法易於在直升機設計系統中得到應用
  3. In this text, we first do some research on the genetic algorithm about clustering, discuss about the way of coding and the construction of fitness function, analyze the influence that different genetic manipulation do to the effect of cluster algorithm. then analyze and research on the way that select the initial value in the k - means algorithm, we propose a mix clustering algorithm to improve the k - means algorithm by using genetic algorithm. first we use k - learning genetic algorithm to identify the number of the clusters, then use the clustering result of the genetic clustering algorithm as the initial cluster center of k - means clustering. these two steps are finished based on small database which equably sampling from the whole database, now we have known the number of the clusters and initial cluster center, finally we use k - means algorithm to finish the clustering on the whole database. because genetic algorithm search for the best solution by simulating the process of evolution, the most distinct trait of the algorithm is connotative parallelism and the ability to take advantage of the global information, so the algorithm take on strong steadiness, avoid getting into the local

    本文首先對聚類分析的遺傳演算法進行了研究，討論了聚類問題的編碼方式和適應度函數的構造方案與計算方法，分析了不同遺傳操作對聚類演算法的性能和聚類效果的影響意義。然後對k - means演算法中初值的選取方法進行了分析和研究，提出了一種基於遺傳演算法的k - means聚類改進（混合聚類演算法） ，在基於均勻采樣的小樣本集上用k值學習遺傳演算法確定聚類數k ，用遺傳聚類演算法的聚類結果作為k - means聚類的初始聚類中心，最後在已知初始聚類數和初始聚類中心的情況下用k - means演算法對完整數據集進行聚類。由於遺傳演算法是一種通過模擬自然進化過程搜索最優解的方法，其顯著特點是隱含并行性和對全局信息的有效利用的能力，所以新的改進演算法具有較強的穩健性，可避免陷入局部最優，大大提高聚類效果。
  4. In rsdm, binary patterns are replaced by real - valued patterns, accordingly avoiding the coding process ; the outer learning rule is replaced by regression rule, therefore the model has not only the ability of pattern recognition but the ability of function approximation. the prearrangement of the address array bases on the distribution of patterns. if the distribution of patterns is uniform. then the address array is prearranged randomly, otherwise predisposed with the theory of genetic algorithm and the pruneing measure so as to indicate the distribution of patterns and improve the network performance. non - linear function approximation, time - series prediction and handwritten numeral recognition show that the modified model is effective and feasible

    在rsdm中，以實值模式代替二值模式，避免了實值到二值的編碼過程：以回歸學習規則代替外積法，使該模型在具有識別能力的同時具有了對函數的逼近能力；地址矩陣的預置根據樣本的分佈採取不同方法，若樣本均勻分佈，則隨機預置，否則利用遺傳演算法的原理和消減措施來預置地址矩陣，使之反映樣本的分佈，改善網路的性能。
  5. Based on these graphic models an efficient routing algorithm, called ieacs ( intensified evolutionary ant colony system ) algorithm is suggested. ieacs merges the acs ( ant colony system ) and ga ( genetic algorithm ), thus features both the mechanism of cooperative learning of acs and the information - exchange capability of ga, is presented in this thesis. in case of asymmetric grid graph and grid - off graph

    然後，介紹了一種兼具生物仿生特性的蟻群和遺傳演算法特點的進化蟻群演算法，並對該演算法模仿蟻群的協同學習機制，以及遺傳演算法的優秀群體中的個體之間信息交換的策略進行了闡述，接著探討了該演算法在總體布線和斯坦納樹問題中的應用。
  6. In the algorithm level, currently various training algorithms of neural networks, including gradient algorithms, intelligent learning algorithms and hybrid algorithms, are comparatively studied ; the optimization principle of bp algorithm for neural networks training is analyzed in detail, and the reasons for serious disadvantages of bp algorithms are found out, moreover, the optimization principle of two kinds of improved bp algorithms is described in a uniform theoretic framework ; and the global optimization algorithms of neural networks, mainly genetic algorithm are expounded in detail, it follows that a improved genetic algorithm is proposed ; finally the training performances of various algorithms are compared based on a simulation experiment on a benchmark problem of neural network learning, furthermore, a viewpoint that genetic algorithm is subject to " curse of dimension " is proposed

    在演算法層，本文對目前用於神經網路訓練的各種演算法，包括梯度演算法、智能學習演算法和混合學習演算法進行了比較研究；對用於神經網路訓練的bp演算法的優化原理進行了詳細的理論分析，找到了bp演算法存在嚴重缺陷的原因，並對其兩類改進演算法-啟發式演算法和二次梯度演算法的優化原理，在統一的框架之下進行了詳盡的理論描述；對神經網路全局優化演算法主要是遺傳演算法進行了詳細的闡述，並在此基礎上，設計了一種性能改進的遺傳演算法；最後基於神經網路學習的benchmark問題對各種演算法在網路訓練中的應用性能進行了模擬研究，並提出了遺傳演算法受困於「維數災難」的觀點。
  7. Fourthly, leaning agent adjusts the domain model and user model adaptively by reinforcement learning and genetic algorithm

    第四，作者研究了學習agent使用強化學習、遺傳演算法自適應地調整領域模型和用戶模型。
  8. Sim - cheng lin, yung - yaoo chen. design of silf - learning fuzzy sliding mode controllers based on genetic algorithm. fuzzy sets and systems. 1997, 86 : 139 - 153

    鄭懷林，陳維南.基於遺傳演算法的模糊滑模控制器設計及其在直流伺服系統中的應用.電氣自動化， 2000 （ 1 ） 38 - 39
  9. 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。
  10. And it is obvious that pseudo - relaxation is a kind of local optimization method, so it cannot guarantee to get the global optimal solution. in this paper, a novel learning algorithm eprbam evolutionary psendo - relaxation learning algorithm for bidirectional association memory employing genetic algorithm and pseudo - relaxation method is proposed to get feasible solution of bam weight matrix

    即使在和取定后，準鬆弛演算法的訓練和學習仍是一種局部最優化過程，它只是在初始權矩陣的附近找到第一個可行解就結束訓練，這類演算法並不能保證獲得全局最優解。
  11. An axial direction line multi - sensors placed method is presented. in the process of machine learning, use genetic algorithm to fit the curve of sensor ' s output. to improve the convergence speed of genetic algorithm, hill climbing method is joined with

    對示教過程中進行曲線擬和所用的遺傳演算法進行了一定的研究，為了解決遺傳演算法在接近收斂點時收斂速度慢的缺點，使用爬山法對其進行改進。
  12. Self - learning hybrid fuzzy adaptive genetic algorithm

    基於特殊二進制編碼的自學習模糊遺傳演算法
  13. In the constructing of the diagnosis module using the technology of the combination of the fuzzy logic and neural network, which based on the fuzzy adaptive learning control network, a simple kind of capable method for consummate the structure and performance of network is introduced, which includes the rules extraction based on the maximum weights matrix and the parameters amendment based on genetic algorithm by floating - point coding. during the monitoring of the parts condition, the output of the condition monitoring system shows the good working condition of the executing agency by fuzzily deducing from the control instruction send by the auv ' s controller and motion status, and so offers the proof to complete mission and return safely

    在珍斷模塊建模中採用模糊邏輯與神經網路結合的技術，以模糊自適應學習控制網路為核心，提出了一種簡單可行的基於最大權值矩陣的規則提取及基於浮點數編碼的遺傳演算法的參數調整的，完善網路結構與性能的方法，並在狀態監測過程中，通過對由控制器輸入的水下機器人運動控制量以及運行狀態的模糊推理，得到執行部件（推進器或舵）的工作狀態優劣程度，為保證水下機器人完成任務，安全返回提供控制依據。
  14. Finally, the paper has designed the program of bp neural networks, neural networks based genetic algorithms and hybrid intelligence learning algorithms in vc + +, and applied those algorithms to the xor problem, the function approximating problem and the explaining high difference seismic data problem. the experiment results have showed that hybrid intelligence learning algorithm for training neural networks is better, faster and more accurate than bp algorithm and genetic algorithm

    最後，用vc + +語言設計了bp神經網路、基於遺傳演算法的神經網路和混合智能學習法神經網路實現和進行計算機模擬運行程序，並分別將它們應用於解決異或、函數擬合和高解析度地震資料解釋等問題，從實踐中證明混合智能學習法神經網路與bp神經網路和基於遺傳演算法的神經網路相比有更好的運算性能、更快的收斂速度和更高的精度。
  15. Secondly, by incorporating the temporal difference prediction technique with the genetic algorithm, a novel hybrid genetic neural networks for controlling nonlinear chaotic system based on the scheme of small perturbations and the use of gradient descent learning method is presented ( known as hygann strategy )

    2 .研究了一種將暫態誤差預測技術、小擾動控制技術、梯度下降法和遺傳演算法（ ga ）融合起來控制非線性混沌系統的復合遺傳神經網路方法（簡稱hygann法） 。
  16. Genetic algorithm solves problem by learning from nature and referring to evolution mechanism of creature, which has wide usage, high stability and concise, and global optimization

    遺傳演算法通過向自然學習，借鑒生物進化機制求解問題，具有廣泛的可應用性和高度穩健性、簡明性和全局優化性。
  17. In designing a multi - structuring elements filter, combination rules and structuring elements of the morphological transform are determined automatically, and one kind of neural networks is taken for the filter, in optimzing structural parameters of the filter, three computation methods are designed respectively, by adopting some priori information in application fields to guide optimal structural parameter learning procedure, which are the bp adaptive learning algorithm, the heuristic genetic learning algorithm and the inductive simulated annealing learning algorithm

    在多結構基元濾波器設計中，通過學習人-機交互選定的目標樣本，自動確定形態變換的組合規則及其結構元素，最終以神經網路形式構成濾波器。在結構參數的優化學習中，利用應用領域的先驗知識，分別設計了自適應bp學習、啟發式遺傳學習和引導式模擬退火學習等三種最優化計算方法。
  18. The dissertation mainly aims at applying several active machine learning strategies to intrusion detection and systematically studies signal analysis techniques of intrusion detection based on statistical learning theory ( slt ), symbol inductive learning theory and genetic learning method. meanwhile, performance comparison and evaluation among intrusion detection techniques based on different machine learning strategies are presented according to computational learning theory and statistical hypothesis test methodology. intrusion detection is regarded as a pattern recognition problem in term of statistical learning theory ; i

    本文的主要工作是將目前幾種有生命力的機器學習策略應用於入侵檢測技術中，論文從入侵檢測的不同視角出發，系統深入地研究了統計學習理論、基於符號的歸納學習理論和遺傳學習方法在入侵檢測信號分析中的應用技術，並在可能近似正確（ pac ）學習框架下，利用計算學習理論和統計假設檢驗方法對基於不同機器學習策略的入侵檢測方法進行了性能比較和評估。
  19. The main factors of probabilistic neural network including the hidden neuron size, hidden central vector and the smoothing parameter, to influence the pnn classification, are analyzed ; the xor problem is implemented by using pnn. a new supervised learning algorithm for the pnn is developed : the learning vector quantization is employed to group training samples and the genetic algorithms ( ga ’ s ) is used for training the network ’ s smoothing parameters and hidden central vector for determining hidden neurons. simulations results show that, the advantage of our method in the classification accuracy is over other unsupervised learning algorithms for pnn

    本文主要分析了pnn隱層神經元個數，隱中心矢量，平滑參數等要素對網路分類效果的影響，並用pnn實現了異或邏輯問題;提出了一種新的pnn有監督學習演算法:用學習矢量量化對各類訓練樣本進行聚類，對平滑參數和距離各類模式中心最近的聚類點構造區域，並採用遺傳演算法在構造的區域內訓練網路，實驗表明:該演算法在分類效果上優于其它pnn學習演算法
  20. Mathematically it corresponds to a linear transformation for a set of points in the euclidean space. for k value learning, this paper made a better selection using genetic algorithm primarily

    從數學意義上講，這種權值學習相當于歐氏空間中對一組點進行了一個線性變換。
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