基于BMO算法混合神经网络的短时交通流预测

准确预测短时间内某路段的交通流量，可以极大提升城市交通效率，而城市交通流预测的核心是各种交差路口附近的车流预测，尤以十字路口最为常见和复杂。针对具有极强的时空相关性且稳定性交差的情况，提出使用改进鸟类繁殖算法（Bird Mating Optimizer,BMO）混合BP神经网络（Back Propagation Neural Network,BPNN）模型对交通流进行非线性拟合。文章使用基于适应度方差的参数自适应调整策略改进了BMO算法，并结合模拟退火思路改善算法早熟问题。使用改进的BMO算法解决了训练时间长和收敛速度慢的缺陷。仿真结果显示，该模型具有更好的非线性拟合能力，使十字路口交通流预测准确率提高了11.4%。     

基于PSO-DE-BP的光伏发电功率短期预测

提高光伏发电功率预测精度对保障智能电网安全稳定运行有重要意义;针对传统BP神经网络存在预测精度不高且收敛速度慢的弊端,提出一种基于粒子群(PSO)差分进化(DE)并行计算优化BP神经网络的光伏发电短期预测方法;首先分析影响因素重要程度,通过带权重的欧式距离筛选相似的训练样本集;其次,对粒子群分组,通过粒子群和差分进化混合算法对粒子组内和组间优化,以保证种群多样性、提高预测稳定和精度、避免局部最优;然后,建立预测模型,通过基于spark的内存计算平台,将PSO-DE-BP算法并行优化以提高算法运行效率;最后,根据不同天气类型的预测结果对模型进行分析验证,此方法比PSO-BP、BP算法模型具有更高的稳定性和预测精度。     

基于混沌搜索的人工蜂群优化神经网络交通流预测方法

为了提高长时交通流的预测精度,提出一种改进的人工蜂群优化BP神经网络分时段预测交通流的方法。利用Tent混沌映射采蜜蜂放弃的新解,实现具有混沌搜索策略的人工蜂群算法,然后优化BP神经网络的权值和阈值,最终训练BP神经网络以求得最优值。利用该预测方法对合肥市黄天路全天的交通流分时段预测,实现了对长时交通流的准确预测,与传统的人工蜂群优化BP神经网络预测对比,能有效改善预测精度,降低预测误差。     

ACO-BP神经网络在电梯交通流预测中的应用

电梯交通流预测为电梯配置与群控调度提供必要的乘客流数据信息.针对基于BP神经网络的电梯交通流预测模型在网络训练过程中表现出的对初值敏感、易陷入极小值等问题,提出利用全局寻优的蚁群优化(ACO)算法优化BP神经网络.同时,利用精英蚂蚁和排序策略对基本ACO算法进行改进.采集天津某办公大厦实际交通流数据进行实例分析,分别对基于传统的BP神经网络和蚁群优化的BP(ACO-BP)神经网络的电梯交通流预测模型进行仿真验证.结果表明:ACO-BP神经网络的预测效果远优于传统的BP神经网络,适用于电梯交通流预测系统.     

改进混沌遗传算法优化BP神经网络的短时交通流预测

BP神经网络算法被广泛地应用于短时交通流预测模型中,但是该算法存在的缺陷降低了预测的准确性.为克服上述缺陷,引入混沌遗传算法(CGA)来进行改进,用混沌遗传算法得到的最优解作为BP神经网络算法的初始值改进算法的缺陷.通过实验结果分析,改进后的算法模型对短时交通流的预测具有了更高的准确性.     

航空发动机包容性数值仿真并行计算效率研究

研究如何提高航空发动机包容性数值仿真并行计算效率的问题。由于仿真需要庞大的网格数量、高度的非线性和复杂的接触算法,并行计算效率一直比较低,已经成为制约工程应用的重要因素。为了提高航空发动机包容性数值仿真并行计算效率,提出了在共享内存并行模式(Share-Memory Parallel,SMP)下,采用自接触算法进行显式动力学分析,提高并行计算效率的方法。实际算例的比较表明,相比传统的面-面接触算法,采用自接触算法可以有效提高航空发动机包容性数值仿真并行计算效率。     

基于CNN-XGBoost的短时交通流预测

为准确预测短时交通流,缓解交通拥堵提高交通运行效率,提出一种基于CNN-XGBoost的短时交通流预测方法。结合短时交通流数据的时间相关性和空间相关性,将本路段和邻近路段的历史数据一同作为输入进行预测。利用卷积神经网络(convolutional neural networks,CNN)实现特征提取以减少数据冗余性,提出一种参数经果蝇算法优化的XGBoost模型用于交通流量预测。实例验证结果表明,CNN可对时间和空间结合下的交通流数据进行有效特征提取;相比SVR、LSTM等模型,改进的XGBoost模型下的交通流量预测误差明显减小。     

基于改进属性约简的细粒度并行AP聚类算法

Affinity Propagation(AP)聚类算法将所有数据点作为潜在的聚类中心,在相似度矩阵的基础上通过消息传递进行聚类.与传统聚类方法相比,对于规模很大的数据集,AP是一种快速、有效的聚类方法.正是这样,属性约简对于AP算法非常重要.另外,在大规模并行系统的设计中,细粒度并行是实现高性能的基本策略.提出了一种基于改进属性约简的细粒度并行AP聚类算法(IRPAP),将粒度思想引入到并行计算中.首先分析了并行计算中的粒度原理.然后用改进的属性约简算法对数据集预处理.此算法并行计算并选择差别矩阵元素,降低了时间空间复杂度,最后用AP算法聚类.整个IRPAP算法将任务划分到多个线程同时处理.实验证明,对于大规模数据集的聚类,IRPAP算法比AP算法效率更高.     

多体动力学优化的并行SQP方法

并行计算的发展大大提高计算机的计算效率,降低计算时间.针对多体动力学的优化问题,分析了求解灵敏度的三种方法的并行性,建立了有限差分法与直接微分法的并行算法.同时采用并行Armijo线性搜索,构成了完整的并行序列二次规划(SQP)算法.将上述算法应用到曲柄滑块的优化中,并与串行SQP算法进行了比较,证实了并行SQP算法可以大大降低计算时间.上述研究为多体动力学优化提供了一种并行求解思路.     

遗传算法优化BP 神经网络的短时交通流混沌预测

为了提高BP神经网络预测模型对混沌时间序列的预测准确性,提出了一种基于遗传算法优化BP神经网络的改进混沌时间序列预测方法.利用遗传算法优化BP神经网络的权值和阈值,然后训练BP神经网络预测模型以求得最优解,并将该预测方法应用到几个典型混沌时间序列和实测短时交通流时间序列进行有效性验证.仿真结果表明,该方法对典型混沌时间序列和短时交通流具有较好的非线性拟合能力和更高的预测准确性.     

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.      

Short-term traffic flow forecasting: parametric and nonparametric approaches via emotional temporal difference learning

Information signal from real case and natural complex dynamical systems such as traffic flow are usually specified by irregular motions. Chaotic nonlinear dynamics approach is now the most powerful tool for scientists to deal with complexities in real cases, and neural networks and neuro-fuzzy models are widely used for their capabilities in nonlinear modeling of chaotic systems more than the traditional methods. As mentioned, the traffic flow conditions caused the forecasting values of traffic flow to lack robustness and accuracy. In this paper, the traffic flow forecasting is analyzed with emotional concepts and multi-agent systems (MASs) points of view as a new method in this field. The findings enabled the researchers to develop a newly object-oriented method of forecasting traffic flow. Its architecture is based on a temporal difference (TD) Q-learning with a neuro-fuzzy structure, which is the nonparametric approach. The performance of TD Q-learning is improved by emotional learning. The proposed method on the present conditions and the action of the system according to the criteria could forecast traffic signals so that the objectives are reached in minimum time. The ability of presented learning algorithm to prospect gains from future actions and obtain rewards from its past experiences allows emotional TD Q-learning algorithm to improve its decisions for the best possible actions. In addition, to study in a more practical situation, the neuro-fuzzy behaviors could be modeled by MAS. The proposed method (intelligent/nonparametric approach) is compared by parametric approach, autoregressive integrated moving average (ARIMA) method, which is implemented by multi-layer perceptron neural networks and called ARIMANN. Here, the ARIMANN is updated by backpropagation and temporal difference backpropagation for the first time. The simulation results revealed that the studied forecaster could discover the optimal forecasting by means of the Q-learning algorithm. Difficult to handle through parametric and classic methods, the real traffic flow signals used for fitting the algorithms is obtained from a two-lane street I-494 in Minnesota City.     

并行广义神经网络的交通流预测

实时、准确的交通流预测是智能交通诱导实现的前提和关键。针对BP神经网络学习过程收敛速度慢、容易陷入局部极小的缺点,引入智能神经元组成的广义神经网络建立交通流预测模型,同时给出基于训练集分解和动态通信模式的并行学习算法来提高广义神经网络的收敛速度,并利用大连市的实际交通流数据进行预测分析。实验结果表明,并行广义神经网络能够满足交通流量预测实时性、精确性的要求,具有一定的应用价值。     

CPSO算法优化神经网络重构的网络流量预测

为了提高网络流量的预测精度,提出了一种混沌粒子群算法优化相空间重构和神经网络的网络流量预测模型(CPSO-BPNN)。利用混沌粒子群算法对BP神经网络初始参数、延迟时间、嵌入维数进行优化,根据延迟时间、嵌入维数对网络流量数据进行重构,BP神经网络根据初始参数进行训练建立网络流量预测模型,通过仿真实验对模型性能进行测试。结果表明,CPSO-BPNN可以准确描述网络流量的复杂变化趋势,提高了网络流量的预测精度。     

模拟退火PSO的神经网络的网络流量预测模型

为解决网络流量时间序列的预测问题,针对传统BP神经网络的网络流量时间序列预测模型容易陷入局部极小值的不足,提出一种基于模拟退火的微粒群算法训练神经网络的网络流量时间序列预测模型.将模拟退火算法和基本粒子微粒群算法相结合,设计出一种基于模拟退火的微粒群算法.利用基于模拟退火微粒群算法优化BP神经网络的权值和阀值,对实际采集的网络流量时间序列进行建模.实验结果表明,基于模拟退火的微粒群算法训练的神经网络具有较高的预测效果,相对于传统的神经网络模型具有更高的预测精度和良好的自适应性.     

Application of seasonal SVR with chaotic immune algorithm in traffic flow forecasting

Accurate forecasting of inter-urban traffic flow has been one of the most important issues globally in the research on road traffic congestion. Because the information of inter-urban traffic presents a challenging situation, the traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during daily peak periods, traffic flow data reveals cyclic (seasonal) trend. In the recent years, the support vector regression model (SVR) has been widely used to solve nonlinear regression and time series problems. However, the applications of SVR models to deal with cyclic (seasonal) trend time series had not been widely explored. This investigation presents a traffic flow forecasting model that combines the seasonal support vector regression model with chaotic immune algorithm (SSVRCIA), to forecast inter-urban traffic flow. Additionally, a numerical example of traffic flow values from northern Taiwan is used to elucidate the forecasting performance of the proposed SSVRCIA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal autoregressive integrated moving average, back-propagation neural network, and seasonal Holt–Winters models. Therefore, the SSVRCIA model is a promising alternative for forecasting traffic flow.     

Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm

Accurate forecasting of inter-urban traffic flow has been one of the most important issues globally in the research on road traffic congestion. However, the information of inter-urban traffic presents a challenging situation; the traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during daily peak periods, traffic flow data reveals cyclic (seasonal) trend. In the recent years, the support vector regression model (SVR) has been widely used to solve nonlinear regression and time series problems. However, the applications of SVR models to deal with cyclic (seasonal) trend time series have not been widely explored. This investigation presents a traffic flow forecasting model that combines the seasonal support vector regression model with chaotic simulated annealing algorithm (SSVRCSA), to forecast inter-urban traffic flow. Additionally, a numerical example of traffic flow values from northern Taiwan is employed to elucidate the forecasting performance of the proposed SSVRCSA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal autoregressive integrated moving average (SARIMA), back-propagation neural network (BPNN) and seasonal Holt-Winters (SHW) models. Therefore, the SSVRCSA model is a promising alternative for forecasting traffic flow.     

Application of temporal difference learning rules in short‐term traffic flow prediction

Studying dynamic behaviours of a transportation system requires the use of the system mathematical models as well as prediction of traffic flow in the system. Therefore, traffic flow prediction plays an important role in today's intelligent transportation systems. This article introduces a new approach to short‐term daily traffic flow prediction based on artificial neural networks. Among the family of neural networks, multi‐layer perceptron (MLP), radial basis function (RBF) neural network and wavenets have been selected as the three best candidates for performing traffic flow prediction. Moreover, back‐propagation (BP) has been adapted as the most efficient learning scheme in all the cases. It is shown that the coefficients produced by temporal signals improve the performance of the BP learning (BPL) algorithm. Temporal signals provide researchers with a new model of temporal difference BP learning algorithm (TDBPL). The capability and performance of TDBPL algorithm are examined by means of simulation in order to prove that the wavelet theory, with its multi‐resolution ability in comparison to RBF neural networks, is a suitable algorithm in traffic flow forecasting. It is also concluded that despite MLP applications, RBF neural networks do not provide negative forecasts. In addition, the local minimum problems are inevitable in MLP algorithms, while RBF neural networks and wavenet networks do not encounter them.     

改进的FS算法选取支持向量回归机参数及应用*

针对支持向量回归机SVR的拟合精度和泛化能力取决于相关参数的选取,提出了基于改进FS算法的SVR参数选择方法,并应用于交通流预测的研究。FS(free search)算法是一种新的进化计算方法,提出基于相对密集度的灾变策略改进FS算法的个体初始位置选择机制,以扩大搜索空间,提高全局搜索能力。对实测交通流量进行滚动预测仿真实验,结果表明该方法优化SVR参数是有效、可行的,与经验估计法和遗传算法相比,得到的SVR模型具有更好的泛化性能和预测精度。