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问题智能化求解观在人工智能课程教学中的探索 被引量:1
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作者 范平 《咸宁学院学报》 2009年第6期21-24,共4页
为了人工智能课程教学和人才培养质量的提高,从计算机专业人工智能课程的自身特点和实际需要出发,对人工智能课程教学方法进行了研究,探索了以问题智能化求解观为核心的教学方法,提高学生学习兴趣,理论联系实际,组织课堂教学,同时逐步... 为了人工智能课程教学和人才培养质量的提高,从计算机专业人工智能课程的自身特点和实际需要出发,对人工智能课程教学方法进行了研究,探索了以问题智能化求解观为核心的教学方法,提高学生学习兴趣,理论联系实际,组织课堂教学,同时逐步建立课程资源库,实现资源共享.经过实际教学运用,该方法能够增强教学效果和提高学生的课程掌握水平. 展开更多
关键词 问题智能化 人工智能课程 教学改革 课程教学
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煤矿“一通三防”智能化技术问题探讨与展望 被引量:2
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作者 郭建雄 《内蒙古煤炭经济》 2023年第1期154-156,共3页
煤炭资源作为我国重要的能源之一,在国家工业发展方面起到不可估量的作用。工业化不断发展,需要消耗的煤炭资源数量越来越多,进一步推动了我国煤矿事业的发展。煤炭开采的难度越来越大,面临的环境越来越复杂,在开采施工过程中面临的各... 煤炭资源作为我国重要的能源之一,在国家工业发展方面起到不可估量的作用。工业化不断发展,需要消耗的煤炭资源数量越来越多,进一步推动了我国煤矿事业的发展。煤炭开采的难度越来越大,面临的环境越来越复杂,在开采施工过程中面临的各种安全隐患明显增加,一旦发生安全事故,会严重影响生命安全,为企业带来较大的经济损失。数据分析表明,我国煤矿事故的发生量有所上升,因此,在发展过程中要不断优化完善管理机制,改进生产工艺流程,提高施工作业的安全性。长期的实践应用表明,“一通三防”技术具有良好的使用效果,能够为工作人员带来更多的安全保障,从而降低安全事故的发生频率。本文对煤矿“一通三防”智能化技术问题的探讨与展望进行研究。 展开更多
关键词 煤矿 “一通三防” 智能化技术问题 展望分析
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综合医院数字化建设中应该重视的几个问题
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作者 叶建云 王东 《智能建筑与城市信息》 2008年第9期106-109,共4页
结合综合医院的特点以及几个医院数字化建设进程,分析提出数字医院建设过程中应该特别重视的几个问题,并针对目前在诸多地域即将建设数字医院有关项目的推进、沟通、规划、实施的经验,对这些问题提出一些看法和建议。
关键词 综合医院特点 数字医院 医院信息化 建筑智能化存在问题
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Membrane-inspired quantum bee colony optimization and its applications for decision engine 被引量:3
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作者 高洪元 李晨琬 《Journal of Central South University》 SCIE EI CAS 2014年第5期1887-1897,共11页
In order to effectively solve combinatorial optimization problems,a membrane-inspired quantum bee colony optimization(MQBCO)is proposed for scientific computing and engineering applications.The proposed MQBCO algorith... In order to effectively solve combinatorial optimization problems,a membrane-inspired quantum bee colony optimization(MQBCO)is proposed for scientific computing and engineering applications.The proposed MQBCO algorithm applies the membrane computing theory to quantum bee colony optimization(QBCO),which is an effective discrete optimization algorithm.The global convergence performance of MQBCO is proved by Markov theory,and the validity of MQBCO is verified by testing the classical benchmark functions.Then the proposed MQBCO algorithm is used to solve decision engine problems of cognitive radio system.By hybridizing the QBCO and membrane computing theory,the quantum state and observation state of the quantum bees can be well evolved within the membrane structure.Simulation results for cognitive radio system show that the proposed decision engine method is superior to the traditional intelligent decision engine algorithms in terms of convergence,precision and stability.Simulation experiments under different communication scenarios illustrate that the balance between three objective functions and the adapted parameter configuration is consistent with the weights of three normalized objective functions. 展开更多
关键词 quantum bee colony optimization membrane computing P system decision engine cognitive radio benchmarkfunction
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Swarm intelligence optimization and its application in geophysical data inversion 被引量:30
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作者 Yuan Sanyi Wang Shangxu Tian Nan 《Applied Geophysics》 SCIE CSCD 2009年第2期166-174,共9页
The inversions of complex geophysical data always solve multi-parameter, nonlinear, and multimodal optimization problems. Searching for the optimal inversion solutions is similar to the social behavior observed in swa... The inversions of complex geophysical data always solve multi-parameter, nonlinear, and multimodal optimization problems. Searching for the optimal inversion solutions is similar to the social behavior observed in swarms such as birds and ants when searching for food. In this article, first the particle swarm optimization algorithm was described in detail, and ant colony algorithm improved. Then the methods were applied to three different kinds of geophysical inversion problems: (1) a linear problem which is sensitive to noise, (2) a synchronous inversion of linear and nonlinear problems, and (3) a nonlinear problem. The results validate their feasibility and efficiency. Compared with the conventional genetic algorithm and simulated annealing, they have the advantages of higher convergence speed and accuracy. Compared with the quasi-Newton method and Levenberg-Marquardt method, they work better with the ability to overcome the locally optimal solutions. 展开更多
关键词 Swarm intelligence optimization geophysical inversion MULTIMODAL particle swarm optimization algorithm
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