全文获取类型
收费全文 | 277篇 |
免费 | 59篇 |
国内免费 | 64篇 |
出版年
2024年 | 1篇 |
2023年 | 3篇 |
2022年 | 5篇 |
2021年 | 5篇 |
2020年 | 9篇 |
2019年 | 4篇 |
2018年 | 7篇 |
2017年 | 15篇 |
2016年 | 24篇 |
2015年 | 9篇 |
2014年 | 16篇 |
2013年 | 18篇 |
2012年 | 22篇 |
2011年 | 20篇 |
2010年 | 17篇 |
2009年 | 33篇 |
2008年 | 20篇 |
2007年 | 22篇 |
2006年 | 26篇 |
2005年 | 27篇 |
2004年 | 23篇 |
2003年 | 6篇 |
2002年 | 6篇 |
2001年 | 10篇 |
2000年 | 8篇 |
1999年 | 7篇 |
1998年 | 5篇 |
1997年 | 6篇 |
1996年 | 4篇 |
1995年 | 4篇 |
1994年 | 3篇 |
1993年 | 2篇 |
1992年 | 6篇 |
1991年 | 4篇 |
1990年 | 3篇 |
排序方式: 共有400条查询结果,搜索用时 15 毫秒
381.
基于FNN和RS理论的综合评估及应用实例 总被引:1,自引:0,他引:1
为较好解决武器系统效能综合评价与分析的问题,提出一种新的综合评价与 分析方法。以某一类型的武器装备的效能评估为例,利用模糊神经网络(FNN)的方法和粗 糙集(RS)理论方法对武器装备效能的优劣进行评估。首先,用FNN方法提取用于效能评 估的模糊规则,然后用一种新的处理不确定知识的数学工具,粗糙集理论方法对装备属性进 行约简,删除其中不相关或不重要的知识,选出最重要的且尽可能少的评价指标获得系统效 能评价的最小决策算法,进而分析得到系统效能的关键因素。 相似文献
382.
We present two random search methods for solving discrete stochastic optimization problems. Both of these methods are variants of the stochastic ruler algorithm. They differ from our earlier modification of the stochastic ruler algorithm in that they use different approaches for estimating the optimal solution. Our new methods are guaranteed to converge almost surely to the set of global optimal solutions under mild conditions. We discuss under what conditions these new methods are expected to converge faster than the modified stochastic ruler algorithm. We also discuss how these methods can be used for solving discrete optimization problems when the values of the objective function are estimated using either transient or steady‐state simulation. Finally, we present numerical results that compare the performance of our new methods with that of the modified stochastic ruler algorithm when applied to solve buffer allocation problems. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005. 相似文献
383.
384.
拖航安全性影响因素权值的确定方法 总被引:2,自引:1,他引:1
运用重要度排序指数法对拖航安全性影响因素的权重进行评价,对变权综合决策方法在舰船拖带中的应用进行了探讨.针对舰船拖带这一多目标决策问题建立了决策模型,对于影响拖带安全的主要指标的权重给出了相应的计算方法,并通过实例对拖带方案进行了评估,为舰船拖带的最优化评定提供了有效的解决途径. 相似文献
385.
386.
387.
This article studies the problem of designing Bayesian sampling plans (BSP) with interval censored samples. First, an algorithm for deriving the conventional BSP is proposed. The BSP is shown to possess some monotonicity. Based on the BSP and using the property of monotonicity, a new sampling plan modified by the curtailment procedure is proposed. The resulting curtailed Bayesian sampling plan (CBSP) can reduce the duration time of life test experiment, and it is optimal in the sense that its associated Bayes risk is smaller than the Bayes risk of the BSP if the cost of the duration time of life test experiment is considered. A numerical example to compute the Bayes risks of BSP and CBSP and related quantities is given. Also, a Monte Carlo simulation study is performed to illustrate the performance of the CBSP compared with the BSP. The simulation results demonstrate that our proposed CBSP has better performance because it has smaller risk. The CBSP is recommended. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 604–616, 2015 相似文献
388.
389.
Benjamin Legros 《海军后勤学研究》2023,70(1):53-71
This study aims to determine and evaluate dynamic idling policies where an agent can idle while some customers remain waiting. This type of policies can be employed in situations where the flow of urgent customers does not allow the agent to spend sufficient time on back-office tasks. We model the system as a single-agent exponential queue with abandonment. The objective is to minimize the system's congestion while ensuring a certain proportion of idling time for the agent. Using a Markov decision process approach, we prove that the optimal policy is a threshold policy according to which the agent should idle above (below) a certain threshold on the queue length if the congestion-related performance measure is concave (convex) with respect to the number of customers present. We subsequently obtain the stationary probabilities, performance measures, and idling time duration, expressed using complex integrals. We show how these integrals can be numerically computed and provide simpler expressions for fast-agent and heavy-traffic asymptotic cases. In practice, the most common way to regulate congestion is to control access to the service by rejecting some customers upon arrival. Our analysis reveals that idling policies allow high levels of idling probability that such rejection policies cannot reach. Furthermore, the greatest benefit of implementing an optimal idling policy occurs when the objective occupation rate is close to 50% in highly congested situations. 相似文献
390.
《防务技术》2022,18(9):1697-1714
To solve the problem of realizing autonomous aerial combat decision-making for unmanned combat aerial vehicles (UCAVs) rapidly and accurately in an uncertain environment, this paper proposes a decision-making method based on an improved deep reinforcement learning (DRL) algorithm: the multi-step double deep Q-network (MS-DDQN) algorithm. First, a six-degree-of-freedom UCAV model based on an aircraft control system is established on a simulation platform, and the situation assessment functions of the UCAV and its target are established by considering their angles, altitudes, environments, missile attack performances, and UCAV performance. By controlling the flight path angle, roll angle, and flight velocity, 27 common basic actions are designed. On this basis, aiming to overcome the defects of traditional DRL in terms of training speed and convergence speed, the improved MS-DDQN method is introduced to incorporate the final return value into the previous steps. Finally, the pre-training learning model is used as the starting point for the second learning model to simulate the UCAV aerial combat decision-making process based on the basic training method, which helps to shorten the training time and improve the learning efficiency. The improved DRL algorithm significantly accelerates the training speed and estimates the target value more accurately during training, and it can be applied to aerial combat decision-making. 相似文献