基于集成学习的动态链接预测方法

链接预测是社会网络分析领域的关键问题。传统的链接预测方法大多针对社会网络的静态结构预测隐含的链接或者将来可能产生的链接,而忽视了网络在动态演变过程中的潜在信息。为了能更好地利用网络演变的动态信息,从而取得更好的链接预测效果,提出了一种基于网络结构演变规律的链接预测方法。该方法使用机器学习技术对网络结构特征的动态变化信息进行训练,学习每种结构特征的变化并得到一个分类器,为每个分类器加权得到最终集成的结果。在三个现实的合著者网络数据集上的实验结果表明,该方法的性能要高于静态链接预测方法和一个相关的动态链接预测方法。这说明,网络结构演变信息有助于提高链接预测效果。此外,实验还表明,不同的结构特征对网络动态变化的刻画能力也有所差别。     

基于分布式深度强化学习的微电网实时优化调度

随着海量新能源接入到微电网中, 微电网系统模型的参数空间成倍增长, 其能量优化调度的计算难度不断上升. 同时, 新能源电源出力的不确定性也给微电网的优化调度带来巨大挑战. 针对上述问题, 本文提出了一种基于分布式深度强化学习的微电网实时优化调度策略. 首先, 在分布式的架构下, 将主电网和每个分布式电源看作独立智能体. 其次, 各智能体拥有一个本地学习模型, 并根据本地数据分别建立状态和动作空间, 设计一个包含发电成本、交易电价、电源使用寿命等多目标优化的奖励函数及其约束条件. 最后, 各智能体通过与环境交互来寻求本地最优策略, 同时智能体之间相互学习价值网络参数, 优化本地动作选择, 最终实现最小化微电网系统运行成本的目标. 仿真结果表明, 与深度确定性策略梯度算法(Deep Deterministic Policy Gradient, DDPG)相比, 本方法在保证系统稳定以及求解精度的前提下, 训练速度提高了17.6%, 成本函数值降低了67%, 实现了微电网实时优化调度.     

结合状态预测的深度强化学习交通信号控制

深度强化学习（deep reinforcement learning,DRL）可广泛应用于城市交通信号控制领域,但在现有研究中,绝大多数的DRL智能体仅使用当前的交通状态进行决策,在交通流变化较大的情况下控制效果有限。提出一种结合状态预测的DRL信号控制算法。首先,利用独热编码设计简洁且高效的交通状态;然后,使用长短期记忆网络（long short-term memory,LSTM）预测未来的交通状态;最后,智能体根据当前状态和预测状态进行最优决策。在SUMO（simulation of urban mobility）仿真平台上的实验结果表明,在单交叉口、多交叉口的多种交通流量条件下,与三种典型的信号控制算法相比,所提算法在平均等待时间、行驶时间、燃油消耗、CO2排放等指标上都具有最好的性能。     

基于三层集成多标记学习的蛋白质多亚细胞定位预测

针对多标记学习和集成学习在解决蛋白质多亚细胞定位预测问题上应用还不成熟的状况,研究基于集成多标记学习的蛋白质多亚细胞定位预测方法。首先,从多标记学习和集成学习相结合的角度提出了一种三层的集成多标记学习系统框架结构,该框架将学习算法和分类器进行了层次性分类,并把二分类学习、多分类学习、多标记学习和集成学习进行有效整合,形成一个通用型的三层集成多标记学习模型;其次,基于面向对象技术和统一建模语言（UML）对系统模型进行了设计,使系统具备良好的可扩展性,通过扩展手段增强系统的功能和提高系统的性能;最后,使用Java编程技术对模型进行扩展,实现了一个学习系统软件,并成功应用于蛋白质多亚细胞定位预测问题上。通过在革兰氏阳性细菌数据集上进行测试,验证了系统功能的可操作性和较好的预测性能,该系统可以作为解决蛋白质多亚细胞定位预测问题的一个有效工具。     

基于深度LightGBM集成学习模型的谷歌商店顾客购买力预测

针对轻量化梯度促进机（LightGBM）等集成学习模型只对数据信息进行一次挖掘,无法自动地细化数据挖掘粒度或通过深入挖掘得到更多的数据中潜在内部关联信息的问题,提出了深度LightGBM集成学习模型,该模型由滑动窗口和加深两部分组成。首先,通过滑动窗口使得集成学习模型能够自动地细化数据挖掘粒度,从而更加深入地挖掘数据中潜在的内部关联信息,同时赋予模型一定的表示学习能力。然后,基于滑动窗口,用加深步骤进一步地提升模型的表示学习能力。最后,结合特征工程对数据集进行处理。在谷歌商店数据集上进行的实验结果表明,所提深度集成学习模型相较原始集成学习模型的预测精度高出6.16个百分点。所提方法能够自动地细化数据挖掘粒度,从而获取更多数据集中的潜在信息,并且深度LightGBM集成学习模型与传统深度神经网络相比是非神经网络的深度模型,参数更少,可解释性更强。     

Multi-label fault recognition framework using deep reinforcement learning and curriculum learning mechanism

In the actual working site, the equipment often works in different working conditions while the manufacturing system is rather complicated. However, traditional multi-label learning methods need to use the pre-defined label sequence or synchronously predict all labels of the input sample in the fault diagnosis domain. Deep reinforcement learning (DRL) combines the perception ability of deep learning and the decision-making ability of reinforcement learning. Moreover, the curriculum learning mechanism follows the learning approach of humans from easy to complex. Consequently, an improved proximal policy optimization (PPO) method, which is a typical algorithm in DRL, is proposed as a novel method on multi-label classification in this paper. The improved PPO method could build a relationship between several predicted labels of input sample because of designing an action history vector, which encodes all history actions selected by the agent at current time step. In two rolling bearing experiments, the diagnostic results demonstrate that the proposed method provides a higher accuracy than traditional multi-label methods on fault recognition under complicated working conditions. Besides, the proposed method could distinguish the multiple labels of input samples following the curriculum mechanism from easy to complex, compared with the same network using the pre-defined label sequence.     

A novel Long-term degradation trends predicting method for Multi-Formulation Li-ion batteries based on deep reinforcement learning

In the design phase of Li-ion batteries for electric vehicles, battery manufacturers need to carry out cycle life tests on a large number of formulations to get the best one that meets customer demands. However, such tests take considerable time and money due to the long cycle life of power Li-ion batteries. Aiming at reducing the cost of cycle life tests, we propose a prediction method that can learn historical degradation data and extrapolate to predict the remaining degradation trend of the current formulation sample taking the initial stage of partial cycle life test results as input. Compared with existing methods, the proposed deep reinforcement learning based method is able to learn degradation trends with different formulations and predict long-term degradation trends. Based on the deep deterministic policy gradient algorithm, the proposed method builds a degradation trend prediction model. Meanwhile, an interactive environment is designed for the model to explore and learn in the training phase. The proposed method is verified with real test data from battery manufacturers under three different temperature conditions in the formulation design stage. The comparisons indicate that the proposed method is superior to traditional degradation trend prediction methods in both accuracy and stability.     

基于数据过采样和集成学习的软件缺陷数目预测方法

预测软件缺陷的数目有助于软件测试人员更多地关注缺陷数量多的模块,从而合理地分配有限的测试资源。针对软件缺陷数据集不平衡的问题,提出了一种基于数据过采样和集成学习的软件缺陷数目预测方法——SMOTENDEL。首先,对原始软件缺陷数据集进行n次过采样,得到n个平衡的数据集;然后基于这n个平衡的数据集利用回归算法训练出n个个体软件缺陷数目预测模型;最后对这n个个体模型进行结合得到一个组合软件缺陷数目预测模型,利用该组合预测模型对新的软件模块的缺陷数目进行预测。实验结果表明SMOTENDEL相比原始的预测方法在性能上有较大提升,当分别利用决策树回归（DTR）、贝叶斯岭回归（BRR）和线性回归（LR）作为个体预测模型时,提升率分别为7.68%、3.31%和3.38%。     

基于Box-Cox转换的集成跨项目软件缺陷预测方法

软件缺陷预测通过预先识别出被测项目内的潜在缺陷程序模块,可以优化测试资源的分配并提高软件产品的质量。论文对跨项目缺陷预测问题展开了深入研究,在源项目实例选择时,考虑了三种不同的实例相似度计算方法,并发现这些方法的缺陷预测结果存在多样性,因此提出了一种基于Box-Cox转换的集成跨项目软件缺陷预测方法BCEL,具体来说,首先基于不同的实例相似度计算方法,从候选集中选出不同的训练集,随后针对这些数据集,进行针对性的Box-Cox转化,并借助特定分类方法构造出不同的基分类器,最后将这三个基分类器进行有效集成。基于实际项目的数据集,验证了BCEL方法的有效性,并深入分析了BCEL方法内的影响因素对缺陷预测性能的影响。     

High-efficient view planning for surface inspection based on parallel deep reinforcement learning

Machine vision, especially deep learning methods, has become a hot topic for product surface inspection. In practice, capturing high quality images is a base for defect detection. It turns out to be challenging for complex products as image quality suffers from occlusion, illumination, and other issues. Multiple images from different viewpoints are often required in this scenario to cover all the important areas of the products. Reducing the viewpoints while ensuring the coverage is the key to make the inspection system more efficient in production. This paper proposes a high-efficient view planning method based on deep reinforcement learning to solve this problem. First, visibility estimation method is developed so that the visible areas can be quickly identified for a given viewpoint. Then, a new reward function is designed, and the Asynchronous Advantage Actor-Critic method is applied to solve the view planning problem. The effectiveness and efficiency of the proposed method is verified with a set of experiments. The proposed method could also be potentially applied to other similar vision-based tasks.     

Intelligent fault diagnosis for rotating machinery using deep Q-network based health state classification: A deep reinforcement learning approach

Fault diagnosis methods for rotating machinery have always been a hot research topic, and artificial intelligence-based approaches have attracted increasing attention from both researchers and engineers. Among those related studies and methods, artificial neural networks, especially deep learning-based methods, are widely used to extract fault features or classify fault features obtained by other signal processing techniques. Although such methods could solve the fault diagnosis problems of rotating machinery, there are still two deficiencies. (1) Unable to establish direct linear or non-linear mapping between raw data and the corresponding fault modes, the performance of such fault diagnosis methods highly depends on the quality of the extracted features. (2) The optimization of neural network architecture and parameters, especially for deep neural networks, requires considerable manual modification and expert experience, which limits the applicability and generalization of such methods. As a remarkable breakthrough in artificial intelligence, AlphaGo, a representative achievement of deep reinforcement learning, provides inspiration and direction for the aforementioned shortcomings. Combining the advantages of deep learning and reinforcement learning, deep reinforcement learning is able to build an end-to-end fault diagnosis architecture that can directly map raw fault data to the corresponding fault modes. Thus, based on deep reinforcement learning, a novel intelligent diagnosis method is proposed that is able to overcome the shortcomings of the aforementioned diagnosis methods. Validation tests of the proposed method are carried out using datasets of two types of rotating machinery, rolling bearings and hydraulic pumps, which contain a large number of measured raw vibration signals under different health states and working conditions. The diagnosis results show that the proposed method is able to obtain intelligent fault diagnosis agents that can mine the relationships between the raw vibration signals and fault modes autonomously and effectively. Considering that the learning process of the proposed method depends only on the replayed memories of the agent and the overall rewards, which represent much weaker feedback than that obtained by the supervised learning-based method, the proposed method is promising in establishing a general fault diagnosis architecture for rotating machinery.     

基于改进深度强化学习的动态移动机器人协同计算卸载

移动边缘计算是解决机器人大计算量任务需求的一种方法。传统算法基于智能算法或凸优化方法,迭代时间长。深度强化学习通过一次前向传递即可求解,但只针对固定数量机器人进行求解。通过对深度强化学习分析研究,在深度强化学习神经网络中输入层前进行输入规整,在输出层后添加卷积层,使得网络能够自适应满足动态移动机器人数量的卸载需求。最后通过仿真实验验证,与自适应遗传算法和强化学习进行对比,验证了所提出算法的有效性及可行性。     

深度逆向强化学习研究综述

深度逆向强化学习是机器学习领域的一个新的研究热点,它针对深度强化学习的回报函数难以获取问题,提出了通过专家示例轨迹重构回报函数的方法。首先介绍了3类深度强化学习方法的经典算法;接着阐述了经典的逆向强化学习算法,包括基于学徒学习、最大边际规划、结构化分类和概率模型形式化的方法;然后对深度逆向强化学习的一些前沿方向进行了综述,包括基于最大边际法的深度逆向强化学习、基于深度Q网络的深度逆向强化学习和基于最大熵模型的深度逆向强化学习和示例轨迹非专家情况下的逆向强化学习方法等。最后总结了深度逆向强化学习在算法、理论和应用方面存在的问题和发展方向。     

Exploration in deep reinforcement learning: A survey

This paper reviews exploration techniques in deep reinforcement learning. Exploration techniques are of primary importance when solving sparse reward problems. In sparse reward problems, the reward is rare, which means that the agent will not find the reward often by acting randomly. In such a scenario, it is challenging for reinforcement learning to learn rewards and actions association. Thus more sophisticated exploration methods need to be devised. This review provides a comprehensive overview of existing exploration approaches, which are categorised based on the key contributions as: reward novel states, reward diverse behaviours, goal-based methods, probabilistic methods, imitation-based methods, safe exploration and random-based methods. Then, unsolved challenges are discussed to provide valuable future research directions. Finally, the approaches of different categories are compared in terms of complexity, computational effort and overall performance.     

基于多智能集成学习的中短期电煤价格预测

为扩大电力市场交易量与下调市场电价,需要提升电煤价格预测的可靠性与准确性.为此本文提出了多智能集成学习的中短期电煤价格预测方法.首先,阐述了Stacking集成学习的结构和原理;然后,介绍了数种智能电煤价格的预测模型,并通过算例证明了不同单智能模型对数据的感知能力存在差异性;进而,通过比较单智能模型预测结果的差异值均差...     

基于进化集成学习的用户购买意向预测

在电子商务时代背景下,精准预测用户的购买意向已经成为提高销售效率和优化客户体验的关键因素。针对传统集成策略在模型设计阶段往往受人为因素限制的问题,构建了一种自适应进化集成学习模型用于预测用户的购买意向。该模型能够自适应地选择最优基学习器和元学习器,并融合基学习器的预测信息和特征间的差异性扩展特征维度,从而提高预测的准确性。此外,为进一步优化模型的预测效果,设计了一种二元自适应差分进化算法进行特征选择,旨在筛选出对预测结果有显著影响的特征。研究结果表明,与传统优化算法相比,二元自适应差分进化算法在全局搜索和特征选择方面表现优异。相较于六种常见的集成模型和DeepForest模型,所构建的进化集成模型在AUC值上分别提高了2.76%和2.72%,并且能够缓解数据不平衡所带来的影响。     

基于深度强化学习的动态装配算法

针对动态装配环境中存在的复杂、动态的噪声扰动,提出一种基于深度强化学习的动态装配算法。将一段时间内的接触力作为状态,通过长短时记忆网络进行运动特征提取;定义序列贴现因子,对之前时刻的分奖励进行加权得到当前时刻的奖励值;模型输出的动作为笛卡尔空间位移,使用逆运动学调整机器人到达期望位置。与此同时,提出一种对带有资格迹的时序差分算法改进的神经网络参数更新方法,可缩短模型训练时间。在实验部分,首先在圆孔–轴的简单环境中进行预训练,随后在真实场景下继续训练。实验证明提出的方法可以很好地适应动态装配任务中柔性、动态的装配环境。     

基于集成学习的口令强度评估模型

针对现有的口令评估模型通用性差,没有一个可以对从简单口令到非常复杂口令都适用的评估模型的问题,设计了一种基于多模型的集成学习的口令评估模型。首先,使用真实的口令训练集训练多个现有的口令评估模型作为子模型;其次,将多个经过训练的子模型作为基学习器进行集成学习,采用偏弱项投票法的结合策略实现各个子模型的优势集成;最后,实现一个以高准确性为前提的通用口令评估模型。实验中使用网络泄露的真实用户口令数据集作为实验数据,实验结果表明,基于多模型集成学习模型针对不同复杂程度的口令进行口令强度评估,其评估结果准确率高、通用性强,所提模型在口令评估方面具有较好的适用性。     

基于深度强化学习种群优化的演化式分拣调度算法

机械制造中的产线分拣作业具有问题与数据的双重复杂性,为了对分拣操作进行优化以提高生产效率,设计了一套分拣作业的数据表示方法与一种基于种群优化的演化式算法,同时整理并公开了一个真实的工业数据集。数据表示方法通过借鉴词袋模型对原始作业数据进行抽象表示;演化式算法使用深度强化学习初始化遗传算法中的种群,同时引入了精英保留策略以提高算法的优化能力。最后,将提出的算法与其他算法在真实的工业数据集与旅行商问题数据集上进行了对比。结果表明,该算法能找到更优的分拣顺序与访问路径,验证了算法的有效性。