Traffic Flow Data Forecasting Based on Interval Type-2 Fuzzy Sets Theory

This paper proposes a long-term forecasting scheme and implementation method based on the interval type-2 fuzzy sets theory for traffic flow data. The type-2 fuzzy sets have advantages in modeling uncertainties because their membership functions are fuzzy. The scheme includes traffic flow data preprocessing module, type-2 fuzzification operation module and long-term traffic flow data forecasting output module, in which the Interval Approach acts as the core algorithm. The central limit theorem is adopted to convert point data of mass traffic flow in some time range into interval data of the same time range (also called confidence interval data) which is being used as the input of interval approach. The confidence interval data retain the uncertainty and randomness of traffic flow, meanwhile reduce the influence of noise from the detection data. The proposed scheme gets not only the traffic flow forecasting result but also can show the possible range of traffic flow variation with high precision using upper and lower limit forecasting result. The effectiveness of the proposed scheme is verified using the actual sample application.      

基于模糊神经网络的短时交通流预测方法研究

为满足交通控制和诱导系统的实时性需求,减少交通拥挤状况,降低交通事故突发频率,需要对短时交通流进行预测;当前的短时交通流预测方法是采用K-近邻的非参数回归对其进行预测,预测过程中没有将预测模型中关键因素对交通流的影响进行详细的说明,导致预测结果不准确,存在短时交通流预测误差较大的问题;为此,提出一种基于模糊神经网络的短时交通流预测方法;该方法首先以历史短时交通流数据样本序列为基础,将提取的关联维数作为短时交通流的混沌特征量,然后以该特征量为依据,对短时交通流数据进行聚类,使相同的短时交通流聚合类样本比不同的交通流聚合类样本更为贴近,采用高斯过程回归对短时交通流预测模型进行建设,建设过程中利用差分方法对短时交通流预测序列进行平稳化操作之后,对短时交通流预测模型进行训练,将GPR模型引入至短时交通流预测过程中,得到交通流预测方差估计值,并确定交通流预测值置信区间,由此实现短时交通流的预测;由此实现短时交通流的预测;实验结果证明,所提方法可以准确地预测交通运输系统的实时状况,为车辆行驶的最佳路线进行了有效引导,减少了自然影响方面和人为因素对短时交通流预测结果的干扰,为交通部门对交通路况的控制管理提供了依据。     

A Self-Evolving Interval Type-2 Fuzzy Neural Network With Online Structure and Parameter Learning

This paper proposes a self-evolving interval type-2 fuzzy neural network (SEIT2FNN) with online structure and parameter learning. The antecedent parts in each fuzzy rule of the SEIT2FNN are interval type-2 fuzzy sets and the fuzzy rules are of the Takagi–Sugeno–Kang (TSK) type. The initial rule base in the SEIT2FNN is empty, and the online clustering method is proposed to generate fuzzy rules that flexibly partition the input space. To avoid generating highly overlapping fuzzy sets in each input variable, an efficient fuzzy set reduction method is also proposed. This method independently determines whether a corresponding fuzzy set should be generated in each input variable when a new fuzzy rule is generated. For parameter learning, the consequent part parameters are tuned by the rule-ordered Kalman filter algorithm for high-accuracy learning performance. Detailed learning equations on applying the rule-ordered Kalman filter algorithm to the SEIT2FNN consequent part learning, with rules being generated online, are derived. The antecedent part parameters are learned by gradient descent algorithms. The SEIT2FNN is applied to simulations on nonlinear plant modeling, adaptive noise cancellation, and chaotic signal prediction. Comparisons with other type-1 and type-2 fuzzy systems in these examples verify the performance of the SEIT2FNN.      

基于区间二型模糊集合的人工交通系统可信度评估

提出了一种基于区间二型模糊集合理论的人工交通系统可信度评估方法.该方法以二型模糊集合算法为核心数据处理方法,构建了人工交通系统的评估体系.利用置信区间方法提取实际交通数据和人工交通数据的统计特征,同时为二型模糊集合提供了输入数据.利用二型模糊集合处理不确定性、随机性和噪声数据的能力,得到刻画实际交通系统和人工交通系统特性的输出数据集.并基于Jaccard算法对两个系统二型模糊集合的输出集进行了相似度运算,以Cronbach系数值为依据,实现了人工交通系统的可信度评估.与传统可信度评估方法相比,该评估方法具有较强的数据处理能力,有效地实现了基于数据驱动方法理念下人工系统与实际系统之间的比较.本文基于面向对象编程语言搭建开发的基于Agent的人工交通系统模型,对其进行了可信度评估验证,评估结果说明了所提出方法的合理性和有效性.     

面向移动App流量的多特征集合集成聚类方法研究与应用

针对移动互联网流量识别问题,基于多项性能评估指标,分析K-均值和谱聚类算法在不同特征集合或不同识别目标流量数据集上的聚类性能,并提出基于多特征集合的集成聚类方法。比较分析实验表明,相同聚类方法在不同特征集合或不同识别目标数据集上性能有所不同,集成聚类方法能够有效提高利用单个特征集合聚类方法的性能。进一步将集成聚类方法应用于App关联分析,分析结果可为移动App的划分和用户行为分析提供客观依据。     

MPEG VBR video traffic modeling and classification using fuzzytechnique

We present an approach for MPEG variable bit rate (VBR) video modeling and classification using fuzzy techniques. We demonstrate that a type-2 fuzzy membership function, i.e., a Gaussian MF with uncertain variance, is most appropriate to model the log-value of I/P/B frame sizes in MPEG VBR video. The fuzzy c-means (FCM) method is used to obtain the mean and standard deviation (std) of T/P/B frame sizes when the frame category is unknown. We propose to use type-2 fuzzy logic classifiers (FLCs) to classify video traffic using compressed data. Five fuzzy classifiers and a Bayesian classifier are designed for video traffic classification, and the fuzzy classifiers are compared against the Bayesian classifier. Simulation results show that a type-2 fuzzy classifier in which the input is modeled as a type-2 fuzzy set and antecedent membership functions are modeled as type-2 fuzzy sets performs the best of the five classifiers when the testing video product is not included in the training products and a steepest descent algorithm is used to tune its parameters     

Interval type-2 neuro-fuzzy system with implication-based inference mechanism

Neuro-fuzzy systems have been proved to be an efficient tool for modelling real life systems. They are precise and have ability to generalise knowledge from presented data. Neuro-fuzzy systems use fuzzy sets – most commonly type-1 fuzzy sets. Type-2 fuzzy sets model uncertainties better than type-1 fuzzy sets because of their fuzzy membership function. Unfortunately computational complexity of type reduction in general type-2 systems is high enough to hinder their practical application. This burden can be alleviated by application of interval type-2 fuzzy sets. The paper presents an interval type-2 neuro-fuzzy system with interval type-2 fuzzy sets both in premises (Gaussian interval type-2 fuzzy sets with uncertain fuzziness) and consequences (trapezoid interval type-2 fuzzy set). The inference mechanism is based on the interval type-2 fuzzy Łukasiewicz, Reichenbach, Kleene-Dienes, or Brouwer–Gödel implications. The paper is accompanied by numerical examples. The system can elaborate models with lower error rate than type-1 neuro-fuzzy system with implication-based inference mechanism. The system outperforms some known type-2 neuro-fuzzy systems.     

An evolving recurrent interval type-2 intuitionistic fuzzy neural network for online learning and time series prediction

The prediction of time series has both the theoretical value and practical significance in reality. However, since the high nonlinear and noises in the time series, it is still an open problem to tackle with the uncertainties and fuzziness in the forecasting process. In this article, an evolving recurrent interval type-2 intuitionistic fuzzy neural network (eRIT2IFNN) is proposed for time series prediction and regression problems. The eRIT2IFNN employs interval type-2 intuitionistic fuzzy sets to enhance the modeling of uncertainties by intuitionistic evaluation and noise tolerance of the system. In the eRIT2IFNN, the antecedent part of each fuzzy rule is defined using intuitionistic interval type-2 fuzzy sets, and the consequent realizes the Takagi–Sugeno–Kang type fuzzy inference mechanism. In order to utilize the prior knowledge including intuitionistic information, a local internal feedback is established by feeding the rule firing strength of each rule to itself eRIT2IFNN is fully adaptive to the evolving of sequence data by online learning of structure and parameters. A modified density-based clustering is implemented for the structure learning, where both densities and membership degrees are involved to determine the fuzzy rules. Performance of eRIT2IFNN is evaluated using a set of benchmark problems and compared with existing fuzzy inference systems. Moreover, the eRIT2IFNN is tested for identification of dynamics under both noise-free and noisy environments. Finally, a group of practical financial price-tracking problems including high-frequency data of financial future, commodity future and precious metal are used for the evaluation of the proposed inference system.     

Interval type-2 fuzzy expert system for prediction of carbon monoxide concentration in mega-cities

This paper presents an indirect approach to interval type-2 fuzzy logic system modeling to forecaste the level of air pollutants. The type-2 fuzzy logic system permits us to model the uncertainties among rules and the parameters related to data analysis. In this paper, we propose an indirect method to create an interval type-2 fuzzy logic system from a historical data, where Footprint of Uncertainties of fuzzy sets are extracted by implementation of an interval type-2 FCM algorithm and based on an upper and lower value for the level of fuzziness m in FCM. Finally, the proposed model is applied for prediction of carbon monoxide concentration in Tehran air pollution. It is shown that the proposed type-2 fuzzy logic system is superior in comparison to type-1 fuzzy logic systems in terms of two performance indices.     

Uncertain Fuzzy Clustering: Insights and Recommendations

In this article, interval type-2 fuzzy sets were used to model the uncertainty that is associated with the various parameters in objective function-based clustering. The purpose was to represent and manage the uncertainty in the cluster memberships by incorporating interval type-2 fuzzy sets. As a result, interval type-2 clustering methods were obtained by modifying the prototype-updating and hard-partitioning procedures in the type-1 fuzzy objective function-based clustering. As a consequence, the management of uncertainty by an interval type-2 fuzzy approach aids cluster prototypes to converge to a more desirable location than a type-1 fuzzy approach. Several examples illustrated the effectiveness of interval type-2 fuzzy approach methods. Furthermore, the uncertainty associated with the parameters for other existing clustering algorithms can be considered in the development of several other interval type-2 clustering algorithms. They are currently under investigation     

An effective neural network and fuzzy time series-based hybridized model to handle forecasting problems of two factors

Fuzzy time series forecasting method has been applied in several domains, such as stock market price, temperature, sales, crop production and academic enrollments. In this paper, we introduce a model to deal with forecasting problems of two factors. The proposed model is designed using fuzzy time series and artificial neural network. In a fuzzy time series forecasting model, the length of intervals in the universe of discourse always affects the results of forecasting. Therefore, an artificial neural network- based technique is employed for determining the intervals of the historical time series data sets by clustering them into different groups. The historical time series data sets are then fuzzified, and the high-order fuzzy logical relationships are established among fuzzified values based on fuzzy time series method. The paper also introduces some rules for interval weighing to defuzzify the fuzzified time series data sets. From experimental results, it is observed that the proposed model exhibits higher accuracy than those of existing two-factors fuzzy time series models.     

基于混合聚类的城市道路多时段控制研究*

多方案控制具有简单实用、可靠性好,稳定性高,性价比高等的优点,在城市交通控制中得到了广泛应用。本研究在利用指数平滑异同移动平均法对缺失交通数据进行修补的基础上,运用快速聚类与系统聚类相结合的混合聚类方法对交通数据进行分析,并以经典的Silhouttte评价指标作为聚类终止条件,同时兼顾/考虑了交通数据时序性,消除聚类结果中的三类“奇异点”,最后利用交通信号配时软件SYNCHRO制定了相应时段的控制方案。相关的案例分析显示,优化后的多时段控制方案可以更好地适应交通流的波动,车均延误减少9.79%,其中时序性考虑可以有效改善控制方案频繁切换对交通流的扰动,避免其负效应对混合聚类改善效果的影响。     

Interval type-2 fuzzy logic systems: theory and design

We present the theory and design of interval type-2 fuzzy logic systems (FLSs). We propose an efficient and simplified method to compute the input and antecedent operations for interval type-2 FLSs: one that is based on a general inference formula for them. We introduce the concept of upper and lower membership functions (MFs) and illustrate our efficient inference method for the case of Gaussian primary MFs. We also propose a method for designing an interval type-2 FLS in which we tune its parameters. Finally, we design type-2 FLSs to perform time-series forecasting when a nonstationary time-series is corrupted by additive noise where SNR is uncertain and demonstrate an improved performance over type-1 FLSs     

区间二型模糊相似度与包含度

相似度与包含度是模糊集合理论中的两个重要概念,但对于二型模糊集合的研究还较为少见．鉴于此,提出了新的区间二型模糊相似度与包含度．首先选择了二者的公理化定义;然后基于公理化定义提出了新的计算公式,并讨论了二者的相互转换关系;最后通过实例来验证二者的性能,并将区间二型模糊相似度与Yang-Shih聚类方法相结合,用于高斯区间二型模糊集合的聚类分析,得到了合理的层次聚类树．仿真实例表明新测度具有一定的实用价值．     

基于K-means和改进KNN算法的风电功率短期预测系统

为提高风电功率短期预测的准确性,针对KNN（K-Nearest Neighbor algorithm）算法在风电功率预测中的不足,提出了基于K-means和改进KNN算法的风电功率短期预测方法。利用K-means聚类方法确定风电历史样本的类别,对KNN算法中搜索相似历史样本集的方式进行了改进和优化,构建了预测模型,并采用C/S架构实现了预测系统的设计。该系统具有自修正功能,能够随着预测次数的增加,不断修正预测模型,逐渐降低预测的误差率。以吉林省某风电场历史数据为样本进行了仿真分析,结果显示该算法与其它算法相比平均绝对误差和均方根误差最大下降1.08%和0.48%,运算时间提升了5.45%,在风电功率超短期多步预测中具有推广应用价值。     

Parallel Interval Type-2 Subsethood Neural Fuzzy Inference System

Neuro-fuzzy models are being increasingly employed in the domains like weather forecasting, stock market prediction, computational finance, control, planning, physics, economics and management, to name a few. These models enable one to predict system behavior in a more human-like manner than their crisp counterparts. In the present work, an interval type-2 neuro-fuzzy evolutionary subsethood based model has been proposed for its use in finding solutions to some well-known problems reported in the literature such as regression analysis, data mining and research problems relevant to expert and intelligent systems. A novel subsethood based interval type-2 fuzzy inference system, named as Interval Type-2 Subsethood Neural Fuzzy Inference System (IT2SuNFIS) is proposed in the present work. Mathematical modeling and empirical studies clearly bring out the efficacy of this model in a wide variety of practical problems such as Truck backer-upper control, Mackey–Glass time-series prediction, Narazaki–Ralescu and bell function approximation. The simulation results demonstrate intelligent decision making capability of the proposed system based on the available data. The major contribution of this work lies in identifying subsethood as an efficient measure for finding correlation in interval type-2 fuzzy sets and applying this concept to a wide variety of problems pertaining to expert and intelligent systems. Subsethood between two type-2 fuzzy sets is different from the commonly used sup-star methods. In the proposed model, this measure assists in providing better contrast between dissimilar objects. This method, coupled with the uncertainty handling capacity of type-2 fuzzy logic system, results in better trainability and improved performance of the system. The integration of subsethood with type-2 fuzzy logic system is a novel idea with several advantages, which is reported for the first time in this paper.     

Modeling data uncertainty on electric load forecasting based on Type-2 fuzzy logic set theory

Real applications based on type-2 (T2) fuzzy sets are rare. The main reason is that the T2 fuzzy set theory requires massive computation and complex determination of secondary membership function. Thus most real-world applications are based on one simplified method, i.e. interval type-2 (IT2) fuzzy sets in which the secondary membership function is defined as interval sets. Consequently all computations in three-dimensional space are degenerated into calculations in two-dimensional plane, computing complexity is reduced greatly. However, ability on modeling information uncertainty is also reduced. In this paper, a novel methodology based on T2 fuzzy sets is proposed i.e. T2SDSA-FNN (Type-2 Self-Developing and Self-Adaptive Fuzzy Neural Networks). Our novelty is that (1) proposed system is based on T2 fuzzy sets, not IT2 ones; (2) it tackles one difficult problem in T2 fuzzy logic systems (FLS), i.e. massive computing time of inference so as not to be applicable to solve real world problem; and (3) membership grades on third dimensional space can be automatically determined from mining input data. The proposed method is validated in a real data set collected from Macao electric utility. Simulation and test results reveal that it has superior accuracy performance on electric forecasting problem than other techniques shown in existing literatures.     

A Type-2 Self-Organizing Neural Fuzzy System and Its FPGA Implementation

This paper proposes a type-2 self-organizing neural fuzzy system (T2SONFS) and its hardware implementation. The antecedent parts in each T2SONFS fuzzy rule are interval type-2 fuzzy sets, and the consequent part is of Mamdani type. Using interval type-2 fuzzy sets in T2SONFS enables it to be more robust than type-1 fuzzy systems. T2SONFS learning consists of structure and parameter identification. For structure identification, an online clustering algorithm is proposed to generate rules automatically and flexibly distribute them in the input space. For parameter identification, a rule-ordered Kalman filter algorithm is proposed to tune the consequent-part parameters. The learned T2SONFS is hardware implemented, and implementation techniques are proposed to simplify the complex computation process of a type-2 fuzzy system. The T2SONFS is applied to nonlinear system identification and truck backing control problems with clean and noisy training data. Comparisons between type-1 and type-2 neural fuzzy systems verify the learning ability and robustness of the T2SONFS. The learned T2SONFS is hardware implemented in a field-programmable gate array chip to verify functionality of the designed circuits.      

Ⅱ-型模糊集截集的定义及特征研究

经典模糊集的截集概念是经典模糊集合与经典集合联系的桥梁，对于II-型模糊集，该文在分析II-型模糊集、区间值II-型模糊集、I-型模糊集以及经典集合之间关系的基础上，定义了II-型模糊集的截集概念，分析了II-型模糊集截集的特征，仿真证明了II-型模糊集截集的有效性，为基于II-型模糊集的决策、聚类等实际应用提供了新的方法。     

交通流时间序列分离方法

采用聚类分析方法对交通流时间序列进行分析可以发现典型的交通流变化模式。通常 可采用欧式距离及K均值算法进行时间序列聚类,但经分析发现单凭此方法还难以实现不同变化趋 势的交通流时间序列的有效分离。针对此问题,提出了将动态时间弯曲及灰色关联度引入交通流时 间序列相似性度量,且结合层次化聚类方法对交通流时间序列进一步分离的方法。通过实验研究,发 现基于灰色关联度的层次化聚类方法能较好地实现交通流时间序列的进一步有效分离。