搜索资源列表
MDPtoolbox
- The MDP toolbox proposes functions related to the resolution of discrete-time Markov Decision Process : finite horizon, value iteration, policy iteration, linear programming algorithms with some variants. The functions (m-functions) were developpe
MobileModels
- 无线通信的各种运动模型。适用于移动通信、无线传感器网络等领域。 包括：Random walk、random waypoint、random direction、boundless simulation area、 gauss-markov等运动模型 - probabilistic random walk
danymic
- 一种基于概率的动态分配资源的呼叫准入策略-dynamic
location-based-services
- This paper presents a technique which is based on pattern recognition techniques, in order to estimate Mobile Terminal (MT) velocity. The proposed technique applies on received signal strength (RSS) measurements and more precisely on information
HMM
- 基于应隐马尔科夫模型的词性标注实现，Viterbi算法实现 -Should be based on hidden Markov models to achieve part of speech tagging, Viterbi algorithm
1D-Markov-chain
- 无线通信预留信道一维马尔科夫链.rar -The wireless communication reserved channels 1D Markov chain. Rar
1D-Markov-chain---indirect-inference
- 无线通信预留信道一维马尔科夫链间接推论法-1D Markov chain- indirect inference
voice_bianshi
- 语音辨识 单音节字语音辨识 基于隐马尔科夫的语音辨识 内含语音数据-Voice Recognition monosyllabic word speech recognition Hidden Markov speech recognition-based voice and data contents
prediction
- matlab code for channel status prediction using hidden markov model in cognitive radio
ReinforcementLearning
- 马尔卡夫决策过程理论定义了一个数学模型，可用于随机动态系统的最优决策过程。 强化学习利用这个数学模型将一个现实中的问题变成一个数学问题。 强化学习就是：追求最大回报G 追求最大回报G就是：找到最优的策略π?。 策略π?告诉在状态s，应该执行什么行动a。 最优策略可以由最优价值方法v?(s)或者q?(s,a)决定(The Markov decision process theory defines a mathematical model that can be used for the
K-order Markov Chain in Mobile Social Networks
- 针对移动社交网络中节点中心性预测问题，提出基于K阶马尔科夫链的中心性预测方法。在真实移动社交数据集的中计算信息熵分析节点中心性的过去与未来规律性，研究了节点中心性的可预测性。利用节点中心性的历史信息，构建状态转移概率矩阵，预测节点未来中心性值, 并通过分析真实值与预测值之间的误差评估了这些预测方法的性能。结果表明，当阶数K=2时，与四种基于时窗的中心性预测方法比较，基于K阶马尔科夫链的预测模型在MIT数据集和Infocom 06数据集中虽不在个体上优于已提出的预测方法，但在整体上达到了优化。(w
Hidden-Markov
- 通过规定转移概率，构造的隐马尔可夫信道状态转移，生成接受序列，接收端接收到的接受序列(By specifying the transition probability, the state transition of the hidden Markov channel is constructed, and the receiving sequence is generated. The receiving sequence is received by the receiving end.)