浅谈机器学习方法简

本文以什么是机器学习、机器学习的发展历史和机器学习的主要策略这一线索,对机器学习进行系统性的描述。接着,着重介绍了流形学习、李群机器学习和核机器学习三种新型的机器学习方法,为更好的研究机器学习提供了新的思路。     

基于PaaS和ESB的分布式集群框架服务平台

设计了一种分布式集群框架服务平台。它提供平台级服务,通过ESB集群消息总线进行交互。平台上能够发布多种业务应用或服务产品,充当了基础资源和上层业务应用的桥梁和纽带。随着所集成的系统数目增多,该平台的负载均衡技术、失效转移技术和集群技术等特性效果就更加突出。     

基于新型免疫粒子群优化的热连轧负荷分配策略*

针对带钢热连轧精轧中的负荷分配问题,提出了一种新型的优化策略。首先提出了一种新型的免疫粒子群混合优化算法,通过克隆选择算子来调节群体的浓度,实现混合算法的个体高亲和力和群体的多样性; 然后通过提出的混合算法对负荷分配进行优化,得到优化的压下量数据,通过这些数据建立了计算负荷分配输出的人工神经网络。实验表明,提出的混合算法和负荷分配优化策略给出了很好的优化效果,能有效地指导实际生产应用。     

蚁群优化算法在求解随机组合优化问题中的应用综述*

不确定条件下的优化问题更贴近真实世界环境,因而日益受到广泛关注。综述了蚁群优化在求解一组不确定条件下的组合优化问题,即随机组合优化问题方面的应用。首先介绍了不确定条件下组合优化问题的概念分类模型,给出了随机组合优化问题的一般定义;然后指出了其与求解传统确定性组合优化问题的不同之处,即目标函数的计算存在不确定性,并详细论述了目前解决方法的进展;最后分析了该领域值得重点关注的几个研究方向,并对其未来发展进行了展望。     

Fault-Diagnosis Method Based on Support Vector Machine and Artificial Immune for Batch Process

A new fault-diagnosis method to be used in batch processes based on multi-phase regression is presented to overcome the difficulty arising in the processes due to non-uniform sample data in each phase. Support vector machine is first used for phase identification, and for each phase, improved artificial immune network is developed to analyze and recognize fault patterns. A new cell elimination role is proposed to enhance the incremental clustering capability of the immune network. The proposed method has been applied to glutamic acid fermentation, comparison results have indicated that the proposed approach can better classify fault samples and yield higher diagnosis precision.     

Application of artificial neural networks to the prediction of tunnel boring machine penetration rate

Rate of penetration of a Tunnel Boring Machine(TBM)in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project.This paper presents the results of a study into the application of an Artificial Neural Network(ANN)technique for modeling the penetration rate of tunnel boring machines.A database,including actual,measured TBM penetration rates,uniaxial compressive strengths of the rock,the distance between planes of weakness in the rock mass and rock quality designation was established.Data collected from three different TBM projects(the Queens Water Tunnel,USA,the Karaj-Tehran water transfer tunnel,Iran,and the Gilgel Gibe Ⅱ hydroelectric project,Ethiopia).A five-layer ANN was found to be optimum,with an architecture of three neurons in the input layer,9,7 and 3 neurons in the first,second and third hidden layers,respectively,and one neuron in the output layer.The correlation coefficient determined for penetration rate predicted by the ANN was 0.94.     

A recognition method of vibration parameter image based on improved immune negative selection algorithm for rotating machinery

To overcome the limitations of traditional monitoring methods, based on vibration parameter image of rotating machinery, this paper presents an abnormality online monitoring method suitable for rotating machinery using the negative selection mechanism of biology immune system. This method uses techniques of biology clone and learning mechanism to improve the negative selection algorithm to generate detectors possessing different monitoring radius, covers the abnormality space effectively, and avoids such problems as the low efficiency of generating detectors, etc. The result of an example applying the presented monitoring method shows that this method can solve the difficulty of obtaining fault samples preferably and extract the turbine state character effectively, it also can detect abnormality by causing various fault of the turbine and obtain the degree of abnormality accurately. The exact monitoring precision of abnormality indicates that this method is feasible and has better on-line quality, accuracy and robustness.     

An evaluation of deep thin coal seams and water-beating/resisting layers in the quaternary system using seismic inversion

Non-liner wave equation inversion, wavelet analysis and artificial neural networks were used to obtain stratum parame-ters and the distribution of thin coal seams. The lithology of the water-bearing/resisting layer in the Quaternary system was also predicted. The implementation process included calculating the well log parameters, stratum contrasting the seismic data and the well logs, and extracting, studying and predicting seismic attributes. Seismic inversion parameters, including the layer velocity and wave impedance, were calculated and effectively used for prediction and analysis. Prior knowledge and seismic interpretation were used to remedy a dearth of seismic data during the inversion procedure. This enhanced the stability of the inversion method. Non-linear seismic inversion and artificial neural networks were used to interpret coal seismic lithology and to study the wa-ter-bearing/resisting layer in the Quaternary system. Interpretation of the 1-2 m thin coal seams, and also of the wa-ter-bearing/resisting layer in the Quaternary system, is provided. The upper mining limit can be lifted from 60 m to 45 m. The pre-dictions show that this method can provide reliable data useful for thin coal seam exploitation and for lifting the upper mining limit, which is one of the principles of green mining.     

简支梁多位置损伤识别研究

曲率模态是结构损伤识别的敏感标示量,采用数值仿真方法,把曲率模态用于桥梁损伤识别.利用简支梁损伤前后一阶曲率变化率作为识别参数,便可明显确定其多处损伤位置.对相关样本参数进行归一化处理并引入噪声干扰,利用BP人工神经网络进行数值仿真计算,也能对其损伤程度进行有效模拟,验证了BP神经网络也有很强的泛化能力及容错能力.     

可拓集合论在专家系统知识表示中的应用

引入了可拓集合论来优化专家系统的设计过程,阐述了可拓集合论应用于专家系统的可行性,在分析知识表示的集合与分类特征的基础上,探讨了可拓集合论在知识表示中的应用,结合人工智能技术的发展趋势,对专家系统在可拓学中的应用前景做出了展望.