Prediction Model of Wax Deposition Rate in Waxy Crude Oil Pipelines by Elman Neural Network Based on Improved Reptile Search Algorithm

A hard problem that hinders the movement of waxy crude oil is wax deposition in oil pipelines.To ensure the safe operation of crude oil pipelines,an accurate model must be developed to predict the rate of wax deposition in crude oil pipelines.Aiming at the shortcomings of the ENN prediction model,which easily falls into the local minimum value and weak generalization ability in the implementation process,an optimized ENN prediction model based on the IRSA is proposed.The validity of the new model was confirmed by the accurate prediction of two sets of experimental data on wax deposition in crude oil pipelines.The two groups of crude oil wax deposition rate case prediction results showed that the average absolute percentage errors of IRSA-ENN prediction models is 0.5476% and 0.7831%,respectively.Additionally,it shows a higher prediction accuracy compared to the ENN prediction model.In fact,the new model established by using the IRSA to optimize ENN can optimize the initial weights and thresholds in the prediction process,which can overcome the shortcomings of the ENN prediction model,such as weak generalization ability and tendency to fall into the local minimum value,so that it has the advantages of strong implementation and high prediction accuracy.

Prediction Model of Weekly Retail Price for Eggs Based on Chaotic Neural Network

This paper establishes a short-term prediction model of weekly retail prices for eggs based on chaotic neural network with the weekly retail prices of eggs from January 2008 to December 2012 in China.In the process of determining the structure of the chaotic neural network,the number of input layer nodes of the network is calculated by reconstructing phase space and computing its saturated embedding dimension,and then the number of hidden layer nodes is estimated by trial and error.Finally,this model is applied to predict the retail prices of eggs and compared with ARIMA.The result shows that the chaotic neural network has better nonlinear fitting ability and higher precision in the prediction of weekly retail price of eggs.The empirical result also shows that the chaotic neural network can be widely used in the field of short-term prediction of agricultural prices.

Multi-step-prediction of chaotic time series based on co-evolutionary recurrent neural network

This paper proposes a co-evolutionary recurrent neural network （CERNN） for the multi-step-prediction of chaotic time series, it estimates the proper parameters of phase space reconstruction and optimizes the structure of recurrent neural networks by coevolutionary strategy. The searching space was separated into two subspaces and the individuals are trained in a parallel computational procedure. It can dynamically combine the embedding method with the capability of recurrent neural network to incorporate past experience due to internal recurrence. The effectiveness of CERNN is evaluated by using three benchmark chaotic time series data sets： the Lorenz series, Mackey-Glass series and real-world sun spot series. The simulation results show that CERNN improves the performances of multi-step-prediction of chaotic time series.

Combined Method of Chaotic Theory and Neural Networks for Water Quality Prediction

Chaos theory was introduced for water quality, prediction, and the model of water quality prediction was established by combining phase space reconstruction theory and BP neural network forecasting method. Through the phase space reconstruction, the one-dimensional water quality time series were mapped to be multi-dimensional sequence, which enriched the spatial information of water quality change and expanded mapping region of training samples of BP neural network. Established model of combining chaos theory and BP neural network were applied to forecast turbidity time series of a certain reservoir. Contrast to BP neural network method, the relative error and the mean squared error of the combined method had all varying degrees of lower. Results indicated the neural network model with chaos theory had the higher prediction accuracy, at the same time, it had better fault-tolerant capability and generalization performance .

Real-Time Prediction of Urban Traffic Problems Based on Artificial Intelligence-Enhanced Mobile Ad Hoc Networks(MANETS)

Traffic in today’s cities is a serious problem that increases travel times,negatively affects the environment,and drains financial resources.This study presents an Artificial Intelligence(AI)augmentedMobile Ad Hoc Networks(MANETs)based real-time prediction paradigm for urban traffic challenges.MANETs are wireless networks that are based on mobile devices and may self-organize.The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts.This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network(CSFPNN)technique to assess real-time data acquired from various sources within theMANETs.The framework uses the proposed approach to learn from the data and create predictionmodels to detect possible traffic problems and their severity in real time.Real-time traffic prediction allows for proactive actions like resource allocation,dynamic route advice,and traffic signal optimization to reduce congestion.The framework supports effective decision-making,decreases travel time,lowers fuel use,and enhances overall urban mobility by giving timely information to pedestrians,drivers,and urban planners.Extensive simulations and real-world datasets are used to test the proposed framework’s prediction accuracy,responsiveness,and scalability.Experimental results show that the suggested framework successfully anticipates urban traffic issues in real-time,enables proactive traffic management,and aids in creating smarter,more sustainable cities.

Sound Quality Prediction of Vehicle Interior Noise under Multiple Working Conditions Using Back-Propagation Neural Network Model

This paper presents a back-propagation neural network model for sound quality prediction (BPNN-SQP) of multiple working conditions’ vehicle interior noise. According to the standards and regulations, four kinds of vehicle interior noises under operating conditions, including idle, constant speed, accelerating and braking, are acquired. The objective psychoacoustic parameters and subjective annoyance results are respectively used as the input and output of the BPNN-SQP model. With correlation analysis and significance test, some psychoacoustic parameters, such as loudness, A-weighted sound pressure level, roughness, articulation index and sharpness, are selected for modeling. The annoyance values of unknown noise samples estimated by the BPNN-SQP model are highly correlated with the subjective annoyances. Conclusion can be drawn that the proposed BPNN-SQP model has good generalization ability and can be applied in sound quality prediction of vehicle interior noise under multiple working conditions.

A prediction comparison between univariate and multivariate chaotic time series

The methods to determine time delays and embedding dimensions in the phase space delay reconstruction of multivariate chaotic time series are proposed. Three nonlinear prediction methods of multivariate chaotic time series including local mean prediction, local linear prediction and BP neural networks prediction are considered. The simulation results obtained by the Lorenz system show that no matter what nonlinear prediction method is used, the prediction error of multivariate chaotic time series is much smaller than the prediction error of univariate time series, even if half of the data of univariate time series are used in multivariate time series. The results also verify that methods to determine the time delays and the embedding dimensions are correct from the view of minimizing the prediction error.

Prediction on Failure Pressure of Pipeline Containing Corrosion Defects Based on ISSA-BPNNModel

Oil and gas pipelines are affected by many factors,such as pipe wall thinning and pipeline rupture.Accurate prediction of failure pressure of oil and gas pipelines can provide technical support for pipeline safety management.Aiming at the shortcomings of the BP Neural Network(BPNN)model,such as low learning efficiency,sensitivity to initial weights,and easy falling into a local optimal state,an Improved Sparrow Search Algorithm(ISSA)is adopted to optimize the initial weights and thresholds of BPNN,and an ISSA-BPNN failure pressure prediction model for corroded pipelines is established.Taking 61 sets of pipelines blasting test data as an example,the prediction model was built and predicted by MATLAB software,and compared with the BPNN model,GA-BPNN model,and SSA-BPNN model.The results show that the MAPE of the ISSA-BPNN model is 3.4177%,and the R2 is 0.9880,both of which are superior to its comparison model.Using the ISSA-BPNN model has high prediction accuracy and stability,and can provide support for pipeline inspection and maintenance.

Neural Network Predictive Control of Variable-pitch Wind Turbines Based on Small-world Optimization Algorithm

通过将混沌映射用于产生初始节点集和进行算子构造,提出一种新的基于实数编码的混沌小世界优化算法。采用4种算法对多例复杂函数的优化问题进行仿真试验,表明所提算法具有能够有效避免陷入局部极小值、快速搜索到最优值的能力。将上述方法应用于变桨距风电机组启动并网时的转速控制,提出一种基于混沌小世界优化算法的神经网络预测控制策略,其预测模型由基于现场数据的神经网络模型建立。仿真与实际测试结果表明,该系统可以根据风速扰动提前预测电机的转速变化,使控制器超前动作,保证系统输出跟踪参考轨迹的方向稳步改变,确保风电机组平稳并网。

Nonlinear chaotic characteristic in leaching process and prediction of leaching cycle period

A laboratory leaching experiment with samples of different grades was carried out, and an analytical method of concentration of leaching solution was put forward. For each sample, respectively, by applying phase space reconstruction for time series of monitoring data, the saturated embedding dimension and the correlation dimension were obtained, and the evolution laws between neighboring points in the reconstructed phase space were revealed. With BP neural network, a prediction model of concentration of leaching solution was set up and the maximum error of which was less than 2%. The results show that there exist chaotic characteristics in leaching system, and samples of different grades have different nonlinear dynamic features; the higher the grade of sample, the smaller the correlation dimension; furthermore, the maximum Lyapunov index, energy dissipation and chaotic extent of the leaching system increase with grade of the sample; by phase space reconstruction, the subtle change features of concentration of leaching solution can be magnified and the inherent laws can be fully demonstrated. According to the laws, a prediction model of leaching cycle period has been established to provide a theoretical foundation for solution mining.

GEKF,GUKF and GGPF based prediction of chaotic time-series with additive and multiplicative noises

On the assumption that random interruptions in the observation process are modelled by a sequence of independent Bernoulli random variables, this paper generalize the extended Kalman filtering （EKF）, the unscented Kalman filtering （UKF） and the Gaussian particle filtering （GPF） to the case in which there is a positive probability that the observation in each time consists of noise alone and does not contain the chaotic signal （These generalized novel algorithms are referred to as GEKF, GUKF and GGPF correspondingly in this paper）. Using weights and network output of neural networks to constitute state equation and observation equation for chaotic time-series prediction to obtain the linear system state transition equation with continuous update scheme in an online fashion, and the prediction results of chaotic time series represented by the predicted observation value, these proposed novel algorithms are applied to the prediction of Mackey-Glass time-series with additive and multiplicative noises. Simulation results prove that the GGPF provides a relatively better prediction performance in comparison with GEKF and GUKF.

STUDY ON PREDICTION METHODS FOR DYNAMIC SYSTEMS OF NONLINEAR CHAOTIC TIME SERIES＊

The prediction methods for nonlinear dynamic systems which are decided by chaotic time series are mainly studied as well as structures of nonlinear self-related chaotic models and their dimensions. By combining neural networks and wavelet theories, the structures of wavelet transform neural networks were studied and also a wavelet neural networks learning method was given. Based on wavelet networks, a new method for parameter identification was suggested, which can be used selectively to extract different scales of frequency and time in time series in order to realize prediction of tendencies or details of original time series. Through pre-treatment and comparison of results before and after the treatment, several useful conclusions are reached: High accurate identification can be guaranteed by applying wavelet networks to identify parameters of self-related chaotic models and more valid prediction of the chaotic time series including noise can be achieved accordingly.

Research on Chaotic Time Series Prediction Based on K-entropy and RBF Neural Networks

In this paper, a method of direct multi-step prediction of chaotic time series is proposed, which is based on Kolmogorov entropy and radial basis functions neural networks. This is done first by reconstructing a phase space using chaotic time series, then using K-entropy as a quantitative parameter to obtain the maximum predictability time of chaotic time series, finally the predicted chaotic time series data can be acquired by using RBFNN. The application considered is Lorenz system. Simulation results for direct multi-step prediction method are compared with recurrence multi-step prediction method. The results indicate that the direct multi-step prediction is more accurate and rapid than the recurrence multi-step prediction within the maximum predictability time of chaotic time series. So, it is convenient to forecast and control with real time using the method of direct multi-step prediction.

A New Approach for Controlling Chaos Based on Direct Optimizing Predictive Control

We introduce the predictive control theory into predictive control algorithm based on a neural network model the study of chaos control and propose a direct optimizing The proposed control system stabilizes the chaotic motion in an unknown chaotic system onto the desired target trajectory. Compared with the existing similar algorithms, the proposed control system has faster response, so it requires much shorter time for the stabilization of the chaotic systems. The proposed approach can control hyperchaos and the algorithm is simple. The convergence of the control algorithm and the stability of the control system can be guaranteed. The theoretic analysis and simulations demonstrate the effectiveness of the algorithm.

基于FMCW雷达的人体生命体征信号预测算法

将FMCW雷达检测到的人体生命体征信号,用于预测未来一段时间内人体生命体征信号是否异常,具有明显的应用价值。该方向当前研究主要针对如何进一步降低重构误差、提升生命体征信号的预测精度。为此,本文提出一种自适应变分模态分解-长短期记忆神经网络的生命体征信号预测方法。针对静止状态下的人体,通过雷达采集到的生命体征信号,采用粒子群算法优化变分模态分解VMD的模态分量个数K和惩罚系数α的值,实现自适应选取后用于VMD分解,再将分解后的模态分量进行叠加重构。采用粒子群算法优化长短期记忆网络模型中的网络层数、学习率、正则化系数等3个参数,自适应选取合适的参数组合,将重构后的信号通过优化后的LSTM网络进行预测。实验结果显示本文所提预测方法在10位志愿者的预测结果与原始数据的均方根误差平均值为0.017 188 9,平均绝对误差的平均值为0.007 158,相较于当前其他研究,预测精度上有明显提升。

基于误差补偿的多模态协同交通流预测模型

交通流量因受周期性特征、突发状况等多重因素影响,现有模型的预测精度无法满足实际要求.对此,本文提出了基于误差补偿的多模态协同交通流预测模型(Multimodal Collaborative traffic flow prediction model based on Error Compensation,MCEC).针对传统预测模型不能兼顾时间序列和协变量的问题,提出基于小波分析的特征拓展方法,该方法引入聚类算法得到节假日标签特征,将拥堵指数、交通事故图、天气信息作为拓展特征,对特征进行多尺度分解.在训练阶段,为达到充分学习各部分数据、最优匹配模型的效果,采用差分整合移动平均自回归模型(Autoreg Ressive Integrated Moving Average Model,ARIMA)、长短期记忆神经网络(Long Short-Term Memory network,LSTM)、限制动态时间规整技术(Dynamic Time Warping,DTW)以及自注意力机制(Self-Attention),设计了多模态协同模型训练.在误差补偿阶段,将得到的相应过程值输入基于支持向量机回归(Support Vector Regression,SVR)的误差补偿模块,对各分量的误差进行学习、补偿,并重构得到预测结果.使用公开的高速公路数据集对MCEC进行验证,在多个时间间隔下对比实验结果表明,MCEC在交通流量预测中的平均绝对百分比误差(Mean Absolute Percentage Error,MAPE)达到17.02%,比LSTM-SVR、ConvLSTM(Convolutional Long Short-Term Memory network)、ST-GCN(Spatial Temporal Graph Convolutional Networks)、MFFB(Multi-stream Feature Fusion Block)、Transformer等预测模型具有更高的预测精度,MCEC模型具有较好的有效性与合理性.

基于人工神经网络的工质基础物性预测

烃类及卤代烃是制冷及余热发电等热力学循环系统潜在的理想工质,但其数量繁多且多数物性参数未知,建立准确的物性预测模型对新型工质的开发至关重要。从多个公开数据库中收集了2500多种烃类及卤代烃分子(含C,H,F,Cl)的基础物性参数,包括正常沸点(T_(b))、临界温度(T_(c))、临界压力(p_(c))、偏心因子(ω),构建了一个工质物性数据库;进一步,通过改进基团贡献-人工神经网络(GC-ANN)的方法,模型的输入参数除基团频率外,还加入相对分子质量、T_(b)、约化维纳指数,建立了预测烃类及卤代烃分子T_(b),T_(c),p_(c),ω的神经网络模型,所开发模型的预测误差小于传统的GC-ANN的误差。

基于生产间歇改进Elman的转炉煤气发生量预测

针对钢铁企业转炉煤气发生量间歇时长波动大,预测精度低的问题,基于生产间歇特征分类,提出基于混沌映射粒子群算法(CPSO)优化Elman神经网络的转炉煤气发生量预测模型(CPSO-Elman)。提取转炉煤气发生量时间序列中生产间歇特征,并根据间歇时长进行分类;引入经混沌扰动改进的PSO算法优化ENN的初始权值和阈值,利用非线性更新的惯性权重以平衡全局搜索与局部搜索能力,并在粒子初始化中添加了混沌映射;构建CPSO-Elman转炉煤气发生量组合预测模型;在预测未来时间内间歇时长基础上,预测转炉煤气发生量。仿真结果表明:所提方法在预测精度上比未经过优化而预测的方法提高了5%左右。

基于改进邻域粗糙集和优化BPNN的火灾预测算法

针对传统森林火灾检测算法精度低,以及大规模、多特征的火灾数据存在冗余信息等问题,该文提出了一种基于改进邻域粗糙集的优化反向传播神经网络(BPNN)火灾预测方法。首先,考虑到数据集具有高维特征空间和高度特征冗余等特点,设计出一种基于混沌反学习蝙蝠(BA)算法的邻域粗糙集特征选择算法,对火灾原始数据集进行特征寻优,得到约简属性子集;然后,构建BA算法优化的BPNN预测模型,将约简属性子集输入该模型中,得到火灾预测的结果;最后,通过平均分类准确度、F1值、精确度、曲线面积、召回率、平均误差率这6种评价指标,在UCI公开森林火灾数据集上分析和检验模型的分类性能。在2个数据集上的实验结果显示,基于混沌反学习策略的算法准确率为94.3%和52.7%,与邻域粗糙集结合后准确率达到98.1%和59.6%,证明了该文算法具备较高的检测精度。

基于CNN计算局部复杂度的可逆信息隐藏算法

在可逆信息隐藏领域,选择较小的预测误差有助于减少信息嵌入过程引起的失真.现有选择方法主要计算预测误差的局部复杂度,将信息嵌入到局部复杂度小的预测误差中.这些局部复杂度计算方法只使用部分相邻像素计算局部复杂度,且计算方法与预测部分使用的预测器无关,因此其选择性能有待提高.本文提出了一种基于CNN的局部复杂度计算方法LCCN,该方法与预测部分的预测器相关,可以使用更多的相邻像素计算局部复杂度,提高局部复杂度与预测误差的相似性.与现有的局部复杂度计算方法相比,所提LCCN利用了CNN的多感受野特性和深度学习的全局优化能力,能使用更多相邻像素计算局部复杂度,从而选择更多数值较小的预测误差,提高嵌入算法性能.此外,我们还提出了一种适用于LCCN的两阶段RDH方案,该方案可以将LCCN应用于多种嵌入方法.实验结果表明,与现有的几种局部复杂度计算方法相比,本文所提方法在标准测试图像和Kodak数据集上嵌入性能更好,在基于LPVO的嵌入技术中,本文所提LCCN模型嵌入10000bits后图像Lena的PSNR为62.09dB,分别高于LV、FV、EE和LAE算法1.05dB、1.02dB、0.78dB和0.90dB.与现有的先进RDH算法比较,本文所提LCCN模型在不同图像上均取得较好效果.