shortest path problem造句
例句與造句
- An approach to the shortest path problem with time - varying
一種求解時(shí)變條件下最短路的算法 - Chaotic neural network - based approach to the shortest path problem
基于混沌神經(jīng)網(wǎng)絡(luò)最短路問(wèn)題的優(yōu)化算法 - Heuristic algorithm for shortest path problem with nonlinear constraints
非線性約束最短路問(wèn)題的啟發(fā)式算法 - Shortest path problem with turn penalties and prohibitions and its solutions
帶轉(zhuǎn)向延誤和限制的最短路徑問(wèn)題及其求解方法 - This paper deals with tsp by transform tsp to a special shortest path problem
本文將解決tsp問(wèn)題轉(zhuǎn)化為一種特殊的最短路問(wèn)題。 - It's difficult to find shortest path problem in a sentence. 用shortest path problem造句挺難的
- Improvement of a algorithm and its application in shortest path problem in dynamic networks
算法改進(jìn)及其在動(dòng)態(tài)最短路徑問(wèn)題中的應(yīng)用 - Concept and implement of graph , basic operations of graph , minimum cost spanning tree , shortest path problem , aov and aoe network
圖的概念和實(shí)現(xiàn)；圖的基本操作；最小價(jià)值生成樹(shù)；最短路徑；活動(dòng)網(wǎng)絡(luò)。 - Finally , chapter eleven covers the special topics of the maximal flow problem , the shortest path problem , and the multicommodity minimal cost flow problem
最后，第十一章覆蓋了特殊主題：最大化流問(wèn)題、最短路徑問(wèn)題和多物最小消費(fèi)流問(wèn)題。 - Base on exist hardware and network equipment the grid computing experimental environment is constituted in this paper . the parallel dijkstra algorithm to solve the short path problem is tested in the experimental environment
本文在介紹網(wǎng)格計(jì)算基本理論基礎(chǔ)上，在現(xiàn)有硬件環(huán)境下架構(gòu)了globus試驗(yàn)平臺(tái)，并在globus網(wǎng)格環(huán)境下測(cè)試了最短路徑問(wèn)題的并行dijkstra算法。 - This subject will survey some of the applications of network flows and focus on key special cases of network flow problems including the following : the shortest path problem , the maximum flow problem , the minimum cost flow problem , and the multi - commodity flow problem
這個(gè)主題將審視一些網(wǎng)流的應(yīng)用并集中焦點(diǎn)在以下網(wǎng)流問(wèn)題的關(guān)鍵特殊情況:最短路徑問(wèn)題,最大流量問(wèn)題,最少成本流量問(wèn)題,和多元物品流量問(wèn)題。 - It first shows the building of stochastic , time - dependent network model , the description of k expected shortest paths problem , the demonstration of travel time probability distributions for the arcs in transportation area , and the calculation of expected travel time on path
本文首先給出了隨機(jī)時(shí)間依賴網(wǎng)絡(luò)模型( stdn模型) 、 k期望最短路徑問(wèn)題的形式化描述，并針對(duì)交通應(yīng)用領(lǐng)域推導(dǎo)出弧耗費(fèi)服從的概率密度函數(shù)，路徑期望值的計(jì)算方法。 - Concretely we discuss the application of the shortest path problem , traveling salesman problerm and project management method on the touring itinerary improvement of travel agency . furthermore , we explore the use of minimum spanning tree problem , overlaid problerm and maximum flow problerm on the touring itinerary improvement of scenic spots . and also enclosed detailed spreadsheet solutions
具體的，討論了最短路問(wèn)題、旅行商問(wèn)題和排程問(wèn)題在旅行社線路優(yōu)化中的應(yīng)用；討論了最小支撐樹(shù)問(wèn)題、覆蓋問(wèn)題和最大流問(wèn)題在旅游景區(qū)線路優(yōu)化中的運(yùn)用，并在附錄中給出了詳細(xì)的電子表格解法。 - Passing through the construction of a special coupled neural network , which can mimic the autowaves in the pulse - coupled neural networks ( pcnns ) , we present a new approach ( auto waves approach ) for solving tsp . the autowaves spread in the network , and the path which the first arrived at the end point from the start point has passed is the optimal answer to the shortest path problem
通過(guò)構(gòu)造耦合神經(jīng)網(wǎng)絡(luò)，使得由神經(jīng)元點(diǎn)火所產(chǎn)生的自動(dòng)波在其中傳播，最先到達(dá)目的地的波前所走過(guò)的路徑即為最短路問(wèn)題的最優(yōu)解，從而有效地獲得了tsp問(wèn)題的最優(yōu)解。 - Standard shortest path algorithms ( such as the dijkstra algorithm ) are not valid , since travel times are random , time - dependent variables . especially , the recognition version of the stochastic shortest path problems is np - complete . k expected shortest paths problem shown in this paper is one of these problems
傳統(tǒng)的最短路徑方法不能解決這種非線性路徑耗費(fèi)的路徑問(wèn)題，尤其是同時(shí)具有隨機(jī)性和時(shí)間依賴性的網(wǎng)絡(luò)使得最優(yōu)路徑問(wèn)題成為np完全問(wèn)題，本文研究的k期望最短路徑就是這樣的問(wèn)題之一。