In this sense , objects are similar to branch nodes of an hdbms , which likewise contain a bundle of child nodes 從這個意義上說,對象與hdbms的分支節(jié)點很象,它同樣也包含許多子節(jié)點。
This thesis transforms the composition of learning content into the logic cluster of correlative concepts in the concept layer , and proposes the idea of learning content hierarchy graph . by means of the and / or graph of artificial intelligent theory , we can transform the learning content into the problem of and / or tree further . hence , we conclude that the learning content is a tree , the root is learning goal , the branch node is correlative concept , and the leaf is media material 本文將具體的學習內容組織轉化成在概念層上相關概念的邏輯聚集,提出了學習內容層次圖思想;經過分析發(fā)現(xiàn)學習內容層次圖的組織方式和人工智能理論中的and / or圖之間存在著相似性,將學習內容的獲取轉化為and / or圖中的and / or樹問題;最終確立了學習內容的獲取就是以樹根為學習目標,以分枝節(jié)點為相關概念組織,以樹葉節(jié)點為對應的媒體素材的邏輯聚集。
A branch and bound algorithm for solving a class of nonlinear 0 - 1 knapsack problems is proposed , in which branching is common 0 - 1 variables one and a better feasible solution is found by a simply integer heuristic method as well as a lower bound of the optimal value of the subproblem in the each branching node is determined by solving linear programming relaxed approximate problem to be obtained with linear relaxed technique 摘要構造出了一類可分離非線性0 - 1背包問題的分枝定界算法,分枝的過程是普通的0 - 1變量分枝,用簡單的取整啟發(fā)式法確定更好的可行解;而在每個分枝結點處用線性松弛技術確定了它的子問題的一個線性規(guī)劃松弛逼近,由此得到最優(yōu)值的一個下界。
For the sake of using the binary classification key more flexiblly to classify and identify the diseases and pests as knowledge reasoning , the integration of the principles of backward reasoning and two - way reasoning was put forward , which charges the binary key into the knowledge database of expert system , and computers can make use of the structure to search the branch node and realize the assistant identification of diseases and pests 為了能更靈活地應用二叉分類檢索表作為知識推理進行病蟲分類鑒定,提出在常規(guī)推理的基礎上,融入反向推理和混合推理技術,將二叉分類檢索表作為知識裝入專家系統(tǒng)知識庫,計算機可以利用它搜索分支結點以實現(xiàn)病蟲輔助鑒定。