Mining the change of customer behavior in fuzzy time-interval sequential patterns

Comprehending changes of customer behavior is an essential problem that must be faced for survival in a fast-changing business environment. Particularly in the management of electronic commerce (EC), many companies have developed on-line shopping stores to serve customers and immediately collect buying logs in databases. This trend has led to the development of data-mining applications. Fuzzy time-interval sequential pattern mining is one type of serviceable data-mining technique that discovers customer behavioral patterns over time. To take a shopping example, (Bread, Short, Milk, Long, Jam), means that Bread is bought before Milk in a Short period, and Jam is bought after Milk in a Long period, where Short and Long are predetermined linguistic terms given by managers. This information shown in this example reveals more general and concise knowledge for managers, allowing them to make quick-response decisions, especially in business. However, no studies, to our knowledge, have yet to address the issue of changes in fuzzy time-interval sequential patterns. The fuzzy time-interval sequential pattern, (Bread, Short, Milk, Long, Jam), became available in last year; however, is not a trend this year, and has been substituted by (Bread, Short, Yogurt, Short, Jam). Without updating this knowledge, managers might map out inappropriate marketing plans for products or services and dated inventory strategies with respect to time-intervals. To deal with this problem, we propose a novel change mining model, MineFuzzChange, to detect the change in fuzzy time-interval sequential patterns. Using a brick-and-mortar transactional dataset collected from a retail chain in Taiwan and a B2C EC dataset, experiments are carried out to evaluate the proposed model. We empirically demonstrate how the model helps managers to understand the changing behaviors of their customers and to formulate timely marketing and inventory strategies.     

Estimation of elastic constant of rocks using an ANFIS approach

The engineering properties of the rocks have the most vital role in planning of rock excavation and construction for optimum utilization of earth resources with greater safety and least damage to surroundings. The design and construction of structure is influenced by physico-mechanical properties of rock mass. Young's modulus provides insight about the magnitude and characteristic of the rock mass deformation due to change in stress field. The determination of the Young's modulus in laboratory is very time consuming and costly. Therefore, basic rock properties like point load, density and water absorption have been used to predict the Young's modulus. Point load, density and water absorption can be easily determined in field as well as laboratory and are pertinent properties to characterize a rock mass. The artificial neural network (ANN), fuzzy inference system (FIS) and neuro fuzzy are promising techniques which have proven to be very reliable in recent years. In, present study, neuro fuzzy system is applied to predict the rock Young's modulus to overcome the limitation of ANN and fuzzy logic. Total 85 dataset were used for training the network and 10 dataset for testing and validation of network rules. The network performance indices correlation coefficient, mean absolute percentage error (MAPE), root mean square error (RMSE), and variance account for (VAF) are found to be 0.6643, 7.583, 6.799, and 91.95 respectively, which endow with high performance of predictive neuro-fuzzy system to make use for prediction of complex rock parameter.     

Prediction of scouring around an arch-shaped bed sill using Neuro-Fuzzy model

In this study, Adaptive Neuro-Fuzzy Inference System (ANFIS) has been used to model local scouring depth and pattern scouring around concave and convex arch shaped circular bed sills. The experimental part of this research study includes seven sets of laboratory test cases which were performed in an experimental flume under different flow conditions. A data set consists of 2754 data points of scouring depth were collected to use in the ANFIS model. The ratio of arch diameter, D, to flume width, W, is used as a non dimensional variables in all test cases. The results from ANFIS model were compared with the results of ANN model obtained by Homayoon et al. [24] and previously presented models. The results indicated that for D/W equal to 1 and 1.2, the ANFIS models produced a good performance for convex and concave bed sills. As a result, the ANFIS models can be used as an alternative to ANN for estimation of scour depth and scour pattern around a concave bed sill installed with a bridge pier.     

基于变论域理论的模糊自适应控制方法研究

针对一类非线性、快时变性以及模型不确定性问题,设计了一种基于变论域的模糊自适应控制器。利用模糊模型逼近控制系统的非线性部分,以及采用李亚普诺夫方法证明所设计的非线性控制系统的渐进稳定性并推导出自适应律。最后将设计的控制器应用于倒立摆,仿真结果表明跟踪性能良好。     

一种简化的三维模糊制器

针对三维模糊控制器规则多,结构复杂,难以实现的问题,提出了一种简化的三维模糊控制器。该方法是把三个输入量分别为偏差、偏差变化、偏差的偏差变化的一维模糊控制器加权融合实现简化的三维模糊控制器。并提出了根据不同类型的被控对象设置三个加权系数。加权系数具有粗调、细调和微调的作用,类似于传统PID调节器的比例、积分和微分,实现对系统静差的消除。     

神经网络PID在PLC系统控制中的应用研究

针对传统的PID算法由于难以给出精确的数学模型,使得系统参数设定困难,同时系统控制效果上存在一定的缺陷,造成系统安全性和可靠性降低,系统控制质量不高。为了解决传统的PID算法所带来的问题,提出了基于模糊神经网络的PID算法,将PID算法、模糊控制算法以及神经网络算法相结合,形成了一种智能控制算法。将算法应用在PLC控制系统中,实验表明算法有效的实现了PID参数的自整定,并且提高了控制质量,具有一定的实际应用推广价值。     

火炮磁流变后坐系统仿真

将磁流变技术应用于火炮反后坐装置是目前正在发展的一种降低火炮后坐力的新技术,优化火炮反后坐装置控制,达到实时调节阻力。针对某型号火炮,在建立动力学模型和电磁模型的基础上设计了磁流变反后坐系统。为实现理想的后坐控制规律,提出了PID和模糊控制算法。利用ADAMS和MATLAB进行联合仿真,仿真结果表明,在后坐行程范围内,最大后坐阻力分别为3.71×105N,3.53×105N,相比传统火炮减小了13%和17%,并且模糊控制的后坐阻力曲线具有良好的"平台效应",实现了控制目标,表明磁流变阻尼器良好的可控性和应用于火炮后坐系统中的可行性。     

基于模糊控制的汽车主动悬架系统仿真研究

汽车悬架直接影响汽车在行驶过程中抑制不平路面对车身的冲击力及车身倾斜度,传统被动悬架遇冲击自动调节能力较差,抗振能力不强,针对上述问题,通过对悬架受力特点分析,建立了1/4车体二自由度主动悬架数学模型,结合自动控制理论,设计车辆的主动悬架模糊控制器,利用MATLAB/Simulink模糊工具箱对其进行仿真,在相同输入的情况下,对主动悬架与被动悬架模型部分性能参数分析比较,仿真结果表明采用此模糊控制器的主动悬架在提高车辆乘坐的舒适性和操纵的稳定性方面明显优于被动悬架。实验证明,研究结果对汽车主动悬架系统的设计具有一定参考价值。     

基于改进蚁群算法的模糊控制器优化设计

研究控制器优化问题,由于模糊控制系统参数无法同时优化,使得系统选择参数困难,使系统控制效果存在一定的缺陷,安全性和可靠性降低。为解决上述问题,提出了一种多种群进化蚁群算法对模糊控制器优化设计。采用懒蚂蚁效应的改进蚁群算法进行优化,在传统蚁群算法的基础上,采用多个种群并行,对算法的初始化、路径构建以及信息素更新改进,并引入到模糊控制器的隶属函数、模糊规则的优化搜索中,搜索出适应于不同控制阶段的模糊控制器参数及控制规则,并进行仿真。仿真结果证明了改进算法对模糊控制器的参数具有良好的搜索速度和精度,使系统有很强的鲁棒性。     

PID控制器设计与仿真研究

研究优化PID控制器性能问题,在过程控制中,系统存在纯滞后性、时变性、非线性的复杂系统,传统模糊控制和PID控制方法解决上述问题效果不佳,导致超调量大和调节时间长。为了解决控制系统中超调量大和调节时间长等难题,在Smith预估器和传统PID控制的基础上,结合模糊PID控制的优点,提出一种模糊PID-Smith的智能控制方法。方法通过对模型的纯滞后进行补偿,来减小纯滞后系统的超调量,从而增强系统的稳定性。通过对火电机组主汽温控制系统进行仿真,结果表明,方法降低了超调量,减少了调节时间,极大地改善了控制性能,提高了控制系统的自适应性和鲁棒性。