多源信息融合的测井曲线分层算法研究

测井数据解释中,针对单一测井曲线无法真实反映地层属性问题,提出以多条测井曲线的滤波因子为权值,融合出一条综合特征曲线,对该特征曲线相继采用层内差异法细分层与模糊聚类校正分层,实现特征曲线的合理分层。实验结果表明:该方法避免了海量数据处理过程,剔除了噪点数据的影响,提高了分层的速度与精度,能够为应用测井资料进行岩性识别、测井相分析、储层划分等研究提供有利的技术支撑。     

一类正规模糊神经网络及其在复杂水淹层识别中的应用

考虑了一种五层结构的正规化模糊神经网络模型,针对网络结构的优化问题给出了该网络模型的规则层节点的选取方法和相应的反传播学习规则.对于具有较少数输入节点的情况,该网络有较快的训练速度.油藏测井解释中水淹层的识别是石油开发中特别是开发中后期比较突出的一个问题,复杂的地质条件在测井曲线的表现中具有许多模糊性,在各种模糊条件的组合下油藏水淹表现为强水淹、中水淹、弱水淹和无水淹等情形.将正规模糊神经网络用于油藏测井解释中水淹层的识别以提取测井曲线与水淹级别之间的映射关系,从而实现模糊性油藏测井解释中水淹层的识别.实验表明此方法对解决水淹层识别问题具有良好的适应性和实用性.     

基于BiLSTM-XGBoost混合模型的储层岩性识别

储层岩性分类是地质研究基础, 基于数据驱动的机器学习模型虽然能较好地识别储层岩性, 但由于测井数据是特殊的序列数据, 模型很难有效提取数据的空间相关性, 造成模型对储层识别仍存在不足. 针对此问题, 本文结合双向长短期循环神经网络(bidirectional long short-term memory, BiLSTM)和极端梯度提升决策树(extreme gradient boosting decision tree, XGBoost), 提出双向记忆极端梯度提升(BiLSTM-XGBoost, BiXGB)模型预测储层岩性. 该模型在传统XGBoost基础上融入了BiLSTM, 大大增强了模型对测井数据的特征提取能力. BiXGB模型使用BiLSTM对测井数据进行特征提取, 将提取到的特征传递给XGBoost分类模型进行训练和预测. 将BiXGB模型应用于储层岩性数据集时, 模型预测的总体精度达到了91%. 为了进一步验证模型的准确性和稳定性, 将模型应用于UCI公开的Occupancy序列数据集, 结果显示模型的预测总体精度也高达93%. 相较于其他机器学习模型, BiXGB模型能准确地对序列数据进行分类, 提高了储层岩性的识别精度, 满足了油气勘探的实际需要, 为储层岩性识别提供了新的方法.     

一类正则模糊神经网络及在沉积微相识别中的应用

考虑一种5层结构的正则化模糊神经网络模型，针对网络结构的优化问题给出了该网络模型规则层节点的选取方法和相应的反传学习规则；针对样本筛选问题，提出一种按模糊隶属函数值相近样本向量类别矫正策略。将正则模糊神经网络用于油藏沉积微相的识别，可自动提取测井曲线与微相类型之间复杂的映射关系，实现沉积微相的连续识别。实际资料处理结果表明，该方法对解决沉积微相识别问题具有良好的适应性和实用性。     

自动生成基于模糊神经网络及综合评判技术的油井压裂方案

本文考虑了一种五层结构的模糊神经网络模型，针对网络结构的优化问题给出了该网络模型规则层点的选取方法和相应的反传播学习规则。同时利用了模糊综合评判技术对油井压裂中的选井和选层过程进行了优选，实现压裂方案的自动生成。实验资料处理结果表明此方法对解决油井压裂方案问题具有良好的实用性。     

基于正则化模糊神经网络的油井压裂效果预测

论文考虑了一种五层结构的正则化模糊神经网络模型,针对网络结构的优化问题给出了该网络模型规则层节点的选取方法和相应的反传播学习规则。同时利用该网络模型对油井压裂的效果进行了预测,起到了辅助决策的作用。实际资料处理结果表明此网络模型对油井压裂效果预测问题具有良好的实用性。     

基于EM和GMM的朴素贝叶斯岩性识别

朴素贝叶斯分类器可以应用于岩性识别.该算法常使用高斯分布来拟合连续属性的概率分布,但是对于复杂的测井数据,高斯分布的拟合效果欠佳.针对该问题,提出基于EM算法的混合高斯概率密度估计.实验选取苏东41-33区块下古气井的测井数据作为训练样本,并选取44-45号井数据作为测试样本.实验采用基于EM算法的混合高斯模型来对测井数据变量进行概率密度估计,并将其应用到朴素贝叶斯分类器中进行岩性识别,最后用高斯分布函数的拟合效果作为对比.结果表明混合高斯模型具有更好的拟合效果,对于朴素贝叶斯分类器进行岩性识别的性能有不错的提升.     

基于模糊聚类分析的交通状态识别方法

针对城市道路交通状态识别的问题,提出了一种改进的模糊C-均值(FCM)算法。首先,该算法要解决聚类数目和模糊指数的选取问题。本文在对交通状态基本特征的分析基础上,结合交通工程理论知识,将城市道路交通状态分为四个等级,从而解决了聚类数目的选取问题;采用启发式方法来确定模糊指数,使隶属函数尽量覆盖整个输入空间;其次,在对上海市某交叉路口的实际交通数据进行实证研究和仿真分析基础上,结合交通的实际情况以及饱和度与交通状态相关性分析,得出了饱和度的辅助判定依据;最后,以饱和度为辅助判定依据,结合实际交通数据重新进行判定。仿真研究表明该方法能够有效地对道路交通状态进行识别。     

基于神经网络的模糊决策树改进算法

传统决策树通过对特征空间的递归划分寻找决策边界,给出特征空间的“硬”划分。但对于处理大数据和复杂模式问题时,这种精确决策边界降低了决策树的泛化能力。为了让决策树算法获得对不精确知识的自动获取,把模糊理论引进了决策树,并在建树过程中,引入神经网络作为决策树叶节点,提出了一种基于神经网络的模糊决策树改进算法。在神经网络模糊决策树中,分类器学习包含两个阶段：第一阶段采用不确定性降低的启发式算法对大数据进行划分,直到节点划分能力低于真实度阈值[ε]停止模糊决策树的增长;第二阶段对该模糊决策树叶节点利用神经网络做具有泛化能力的分类。实验结果表明,相较于传统的分类学习算法,该算法准确率高,对识别大数据和复杂模式的分类问题能够通过结构自适应确定决策树规模。     

两阶段模糊聚类算法在气测资料解释中的应用

针对气测解释的随机性和模糊性的特点,提出一种两阶段模糊聚类算法.该算法通过引入密度参数对最大最小距离算法作了改进,以改进后的最大最小距离算法对数据集进行粗聚类,再以粗聚类所得的聚类中心为初始聚类中心执行标准模糊C-均值算法,得到类中心以及各数据类别.用于某油田某区块的储层油气性识别的实践表明,该算法实现简单、准确率较高、稳定性好,优于标准FCM算法.     

一种新的基于神经模糊推理网络的复杂系统模糊辨识方法

针对基于输入输出数据的复杂系统的模糊辨识问题,提出了一种新的神经模糊推理网络及相应的学习算法．学习算法被应用于系统的结构辨识与参数辨识．在结构辨识阶段,介绍了一种新的直接从输入输出数据中抽取和优化模糊规则的学习算法;在参数辨识阶段,提出和推导了一种非监督学习和监督学习相结合的混合式学习算法,实现模糊隶属函数的初步调整和优化．仿真结果表明,本文的方法可以同时满足对辨识精度、收敛速度、可读性和规则数的要求．     

A robust approach for root causes identification in machining processes using hybrid learning algorithm and engineering knowledge

To improve product quality and productivity, one of the most critical factors for most manufacturers lies in quickly identifying root causes in machining process during ramp-up and production time. Though multivariate statistical process monitoring techniques using control charts have been successfully used to detect anomalies in machining processes, they cannot provide guidelines to identify and isolate root causes. One novel robust approach for root causes identification (RCI) in machining process using hybrid learning algorithm and engineering-driven rules is developed in this study. Firstly, off-line pattern match relationships between fixture fault patterns and part variation motion patterns are derived. Then, a hybrid learning algorithm is explored for identifying the part variation motion patterns. An unknown root cause is identified and isolated using the output of hybrid learning algorithm and engineering-driven rules. Finally, the data from the real-world cylinder head of engine machining processes are collected to validate the developed approach. The results indicate that the developed approach can perform effectively for identifying root causes of fixture in machining processes. All of the analysis from this study provides guidelines in developing root causes identification systems based on hybrid learning algorithm and engineering knowledge.     

扩展AL log挖掘日志本体的ILP方法*

为发现Web使用记录中所蕴涵的用户访问模式,在深入分析日志本体中事件间的抽象关系后,提出适用于原子事件和复合事件间整分关系推理的ALC传播规则扩展已有的推理模式,并在此基础上提出一种挖掘日志本体的ILP方法。该方法结合描述逻辑和Horn规则在知识表示和推理过程中互补的特点,采用ALlog混合系统构建知识库,利用约束SLD反驳消解和扩展ALC传播规则从日志本体中学习用户访问模式,达到站点商业智能和个性化的目的。最后给出验证该方法的实例,实验结果表明了该方法的可行性和有效性。     

Fuzzy modeling of fluidized catalytic cracking unit

The paper deals with the fuzzy system identification of reactor–regenerator–stripper–fractionator's (RRSF) section of a fluidized catalytic cracking unit (FCCU). The fuzzy system identification based on the data collected from an operating refinery of FCCU of capacity, 1.2 MMPTA, with a sample time of 10 min. A generalized fuzzy model (GFM) and identification of structure and model parameter for multi-input/single output is presented. The GFM has the capability of representing both the CRI model and TS model under certain conditions. The structure identification and the parameter estimation are carried out using hybrid learning approach comprising modified mountain clustering and gradient descent learning with least square estimation (LSE) for the identification of a fuzzy model. The modified mountain clustering considers every data point as a potential cluster center in x × y hyperspace. The optimum number of clusters, which leads to an optimum number of rules, is determined with the help of validity function that guides the search. The obtained result from the modified mountain clustering initializes the GFM. Further hybrid of the gradient descent technique and LSE is aimed at learning of the GFM parameters in two phases. In the first phase of an epoch of learning gradient descent tunes the premise parameter and index of fuzziness of each rule. In second phase, LSE utilizes the results of first phase for evaluating the coefficient of local linear model of corresponding rules.     

A hybrid clustering and gradient descent approach for fuzzymodeling

In this paper, a hybrid clustering and gradient descent approach is proposed for automatically constructing a multi-input fuzzy model where only the input-output data of the identified system are available. The proposed approach is composed of two steps: structure identification and parameter identification. In the process of structure identification, a clustering method is proposed to provide a systematic procedure to determine the number of fuzzy rules and construct an initial fuzzy model from the given input-output data. In the process of parameter identification, the gradient descent method is used to tune the parameters of the constructed fuzzy model to obtain a more precise fuzzy model from the given input-output data. Finally, two examples of nonlinear system are given to illustrate the effectiveness of the proposed approach.     

Person identification using multiple cues

This paper presents a person identification system based on acoustic and visual features. The system is organized as a set of non-homogeneous classifiers whose outputs are integrated after a normalization step. In particular, two classifiers based on acoustic features and three based on visual ones provide data for an integration module whose performance is evaluated. A novel technique for the integration of multiple classifiers at an hybrid rank/measurement level is introduced using HyperBF networks. Two different methods for the rejection of an unknown person are introduced. The performance of the integrated system is shown to be superior to that of the acoustic and visual subsystems. The resulting identification system can be used to log personal access and, with minor modifications, as an identity verification system     

An on-line hybrid learning algorithm for multilayer perceptron in identification problems

A hybrid learning algorithm for multilayered perceptrons (MLPs) and pattern-by-pattern training, based on optimized instantaneous learning rates and the recursive least squares method, is proposed. This hybrid solution is developed for on-line identification of process models based on the use of MLPs, and can speed up the learning process of the MLPs substantially, while simultaneously preserving the stability of the learning process. For illustration and test purposes the proposed algorithm is applied to the identification of a non-linear dynamic system.     

学习自动机在混杂系统切换控制上的初步应用

提出了学习自动机应用于混杂系统切换控制的方法,其主要思路是首先确定一个作为控制信号的行为集,利用学习自动机的学习能力,学习优化行为的发生概率,最终实现满意的控制.对一个实际应用中的混杂对象的切换控制的例子作了仿真,实际结果体现了学习自动机在混杂系统切换控制上的优化能力,证明了它的可实现性.     

基于混合人工免疫算法的流程挖掘事件日志融合方法

流程挖掘是流程管理和数据挖掘交叉领域中的一个研究热点.在实际业务环境中,流程执行的数据往往分散记录到不同的事件日志中,需要将这些事件日志融合成为单一事件日志文件,才能应用当前基于单一事件日志的流程挖掘技术.然而,由于流程日志间存在着执行实例的多对多匹配关系、融合所需信息可能缺失等问题,导致事件日志融合问题具有较高挑战性.本文对事件日志融合问题进行了形式化定义,指出该问题是一个搜索优化问题,并提出了一种基于混合人工免疫算法的事件日志融合方法：以启发式方法生成初始种群,人工免疫系统的克隆选择理论基础,通过免疫进化获得“最佳”的融合解,从而支持包含多对多的实例匹配关系的日志融合;考虑两个实例级别的因素：流程执行路径出现的频次和流程实例间的时间匹配关系,分别从“量”匹配和“时间”匹配两个维度来评价进化中的个体;通过设置免疫记忆库、引入模拟退火机制,保证新一代种群的多样性,减少进化早熟几率.实验结果表明,本文的方法能够实现多对多的实例匹配关系的事件日志融合的目标,相比随机方法生成初始种群,启发式方法能加快免疫进化的速度.文中还针对利用分布式技术提高事件日志融合性能,探讨了大规模事件日志的分布式融合中的数据划问题.     

Parallel Reinforcement Learning-Based Energy Efficiency Improvement for a Cyber-Physical System

As a complex and critical cyber-physical system (CPS), the hybrid electric powertrain is significant to mitigate air pollution and improve fuel economy. Energy management strategy (EMS) is playing a key role to improve the energy efficiency of this CPS. This paper presents a novel bidirectional long short-term memory (LSTM) network based parallel reinforcement learning (PRL) approach to construct EMS for a hybrid tracked vehicle (HTV). This method contains two levels. The high-level establishes a parallel system first, which includes a real powertrain system and an artificial system. Then, the synthesized data from this parallel system is trained by a bidirectional LSTM network. The lower-level determines the optimal EMS using the trained action state function in the model-free reinforcement learning (RL) framework. PRL is a fully data-driven and learning-enabled approach that does not depend on any prediction and predefined rules. Finally, real vehicle testing is implemented and relevant experiment data is collected and calibrated. Experimental results validate that the proposed EMS can achieve considerable energy efficiency improvement by comparing with the conventional RL approach and deep RL.