基于MFR-GEP的高阶常微分方程预测模型

股价预测一直是金融投资领域的热点问题，但是股票市场相关指标数据的波动性和不确定性使得股价预测问题成为难点。因此对于非线性且受到多因素影响的股票系统，传统的预测方法无法准确地表达股价的变化规律，预测效果较差。针对复杂的股价预测问题，建立了基于多指标正则化GEP算法（Multiple Factor Regularization Gene Expression Programming，MFR-GEP）的高阶常微分方程模型，利用数值差分拟合股价数据，并且加入影响股价的其他指标作为正则项，其中利用指标相关性确定正则项权重参数，应用模糊粗糙集的原理确定子函数映射。该模型能够刻画股价随时间的变化趋势，更好地描述数据波动，正则项的加入使得模型可以根据多指标进行预测，避免因单一指标引起的预测精度低等问题。最后将提出的算法与标准GEP算法及传统预测算法进行对比实验，结果充分验证了该算法的有效性和准确性。     

高阶常微分方程的演化建模用于时间序列的分析

本文提出采用高阶常微分方程模型代替传统的时序分析中所用的ＡＲＭＡ模型来实现一维动态系统的建模,并针对传统方法建模过程中所遇到的困难,设计了将遗传程序设计与遗传算法个嵌套的混合演化建模算法,以遗传程序设计优化模型结构,以遗传算法优化模型参数,首次成功地实现了动态系统的高阶微分方程建模过程自动化,对三个典型时间序列实例的实验结果表明：采用此算法可由计算机自动发现适合描述该动态系统的高阶常微分方程模型,     

一种改进的GEP方法及其在演化建模预测中的应用

为了提高预测的准确性,结合基于突变算子的基因表达式和一种基于群体搜索技术的演化算法,提出了改进的GEP方法,并用其对数据进行演化建模。实例测试的结果表明,使用改进的GEP方法得到的模型要优于GP和单纯的GEP方法得到的模型,本方法具有良好的拟合度和预测精度。     

我国各地区经济产出的演化建模

基因表达式编程（GEP）是一种基于基因组和表现型组的新型遗传算法。由于区域经济的投入与产出的映射关系的非线性、复杂性,本文基于GEP算法对对我国各地区的经济产出进行了演化建模,选取人力资本、固定资产和耕地面积作为经济投入要素,并选取各地区经济数据中的2/3作为训练样本,1/3数据进行预测,结果表明用GEP算法对经济产出建模得到的模型,除了对训练样本有较好的拟合精度之外,也具有较好的预测功能。     

基因表达式程序设计在信息系统建模预测中的应用

基因表达式程序设计(GEP)是一种基于基因组和表现型组的新型遗传算法,该算法具有很好的健壮性和高效性采用基因表达式的方法进行演化建模,实例测试的结果表明使用基因表达式程序设计的方法得到的模型要优于普通的线性回归方法和传统的遗传程序设计方法得到的模型,提高了拟合和预测精度。     

基因表达式程序设计在信息系统建模预测中的应用

基因表达式程序设计(GEP)是一种基于基因组和表现型组的新型遗传算法,该算法具有很好的健壮性和高效性.采用基因表达式的方法进行演化建模,实例测试的结果表明使用基因表达式程序设计的方法得到的模型要优于普通的线性回归方法和传统的遗传程序设计方法得到的模型,提高了拟合和预测精度.     

常微分方程组的演化建模

利用演化算法的自适应,自组织,自学习的特性,设计了遗传程序设计与遗传算法和相嵌套的常微分方程组混合演化建模算法,以遗传程序设计优化模型结构,以遗传算法优化模型参数,首次实现了常微分方程组建模过程自动化并可进行有效的预测。     

基于基因表达式编程的私人汽车拥有量建模和预测*

准确预测私人汽车拥有量,对制定经济政策和进行经济宏观调控、保证社会经济和谐发展有重要的作用。基因表达式编程（GEP）是新的进化模型,在数据挖掘领域得到了广泛的关注和研究,对符号回归任务表现了很强的优势。阐述了GEP基本原理,GEP进行序列分析的基本方法;根据1990—2007年全国和人汽车拥有量,基于GEP技术挖掘到了其模型。实验表明,基于GEP技术得到的私人汽车拥有量模型预测精度高、泛化能力强。     

基因表达式编程在重金属形态预测建模中的应用研究

提出一种改进的GEP(Gene Expression Programming)算法。根据重金属(HM)形态随时间变化(HMFT)的特点,建立基于跳跃基因表达式编程(JM-GEP)的重金属形态预测模型。跳跃算子是该模型的关键。为了保持种群多样性,研究最优保留策略GEP的收敛特性,跳跃算子采用自适应的跳跃概率。针对改进后的JM-GEP算法,提出一种基于GEP的重金属形态预测建模方法。仿真结果表明新模型更适合于HMFT的特性函数,找到全局最优解,且明显优于经典GEP算法及其他算法。该新模型方法还可广泛用于其他时间序列预测问题的研究。     

基于基因表达式编程的邮电业务量预测

基因表达式编程(GEP)是将进化的遗传操作和个体的适应度评价相分离的进化模型,具有比遗传编程快2-4个数量级能力.邮电业务量是反映经济发展的重要指标之一,其预测技术得到了广泛的研究,主要工作包括:阐述了GEP基本原理,以及GEP进行时间序列分析的基本方法;运用了GEP技术,对邮电业务总量进行建模研究,并进行了预测检验和分析.实验结果表明,基于GEP得到的邮电业务总量模型有较好的泛化能力,在测试数据上平均相对误差为4.44%.     

A new approach for predicting and collaborative evaluating the cutting force in face milling based on gene expression programming

Cutting force is one of the fundamental elements that can provide valuable insight in the investigation of cutter breakage, tool wear, machine tool chatter, and surface finish in face milling. Analyzing the relationship between process factors and cutting force is helpful to set the process parameters of the future cutting operation and further improve production quality and efficiency. Since cutting force is impacted by the inherent uncertainties in the machining process, how to predict the cutting force presents a significant challenge. In the meantime, face milling is a complex process involving multiple experts with different domain knowledge, collaborative evaluation of the cutting force model should be conducted to effectively evaluate the constructed predictive model. Gene Expression Programming (GEP) combines the advantages of the Genetic Algorithm (GA) and Genetic Programming (GP), and has been successfully applied in function mining and formula finding. In this paper, a new approach to predict the face milling cutting force based on GEP is proposed. At the basis of defining a GEP environment for the cutting force prediction, an explicit predictive model has been constructed. To verify the effectiveness of the proposed approach, a case study has been conducted. The comparisons between the proposed approach and some previous works show that the constructed model fits very well with the experimental data and can predict the cutting force with a high accuracy. Moreover, in order to better apply the constructed predictive models in actual face milling process, a collaborative model evaluation method is proposed to provide a distributed environment for geographical distributed experts to evaluate the constructed predictive model collaboratively, and four kinds of collaboration mode are discussed.     

Prediction of splitting tensile strength from the compressive strength of concrete using GEP

Splitting tensile strength is one of the important mechanical properties of concrete that is used in structural design. In this paper, it is aimed to propose formulation for predicting cylinder splitting tensile strength of concrete by using gene expression programming (GEP). The database used for training, testing, and validation sets of the GEP models is obtained from the literature. The GEP formulations are developed for prediction of splitting tensile strength of concrete as a function of water-binder ratio, age of specimen, and 100-mm cube compressive strength. The training and testing sets of the GEP models are randomly selected from the complete experimental data. The GEP formulations are also validated with additional experimental data except from the data used in training and testing sets of the GEP models. GEP formulations’ results are compared with experimental results. Results of this study revealed that GEP formulations exhibited better performance to predict the splitting tensile strength of concrete.     

基因表达式程序设计进行复杂函数参数识别

在介绍了基因表达式程序设计方法的基础上,采用基因表达式程序设计建立了复杂函数参数识别的模型,给出了算法结构与算法程序流程,并利用基因表达式程序设计方法进行未知函数拟合的实验,定义合适的终止条件,得到精确的预测结果.实验结果表明,此方法具有很高的预测精度,明显优于传统方法.最后指出该方法只需要提供足够的实验或实验数据,知道目标函数,就可以达到准确预测的目的,因而可以广泛应用于复杂函数拟合中,具有广阔的应用前景.     

A novel approach to prediction of rheological characteristics of jet grout cement mixtures via genetic expression programming

Highly nonlinear flow behavior of cement-based grout mixtures has always become an important issue for experimenters during jet grouting applications. In this viewpoint, an investigation has been addressed in this paper on the applicability of a recent soft computing prediction tool, genetic expression programming (GEP), to the prediction of rheological characteristics (i.e., shear stress, viscosity) of the grout mixtures with various stabilizers (clay, sand, lime) for jet grouting purposes. The experimental data (shear stress versus shear rate with respect to stabilizer dosages) of grout mixtures obtained from rheometer tests have been collected from previous study conducted in a wide range of stabilizer dosage rates (0–100 %, by dry weight of binder). For predicting the shear stress and viscosity as the output variables during the train and testing stages, the input variables in the GEP models included shear rate and stabilizer dosage primarily. As a consequence of GEP modeling compared with measured data, this study reveals satisfactory GEP formulations in a good accuracy (R ≥ 0.86) for predictions of shear stress and viscosity regarding the stabilizer additions. The GEP formulas are also found adequate for modeling the flow behavior of the shear stress–shear rate, alternatively to conventional nonlinear regression and rheological models (Herschel–Bulkley, Robertson–Stiff). For assistance of preliminary evaluations, the derived GEP formulas could be potentially considered in practice for estimations of pumping pressure (shear stress), pumping rate (shear rate) and viscosity of jet grout mixtures.

     

Flexural buckling load prediction of aluminium alloy columns using soft computing techniques

This paper presents the application of soft computing techniques for strength prediction of heat-treated extruded aluminium alloy columns failing by flexural buckling. Neural networks (NN) and genetic programming (GP) are presented as soft computing techniques used in the study. Gene-expression programming (GEP) which is an extension to GP is used. The training and test sets for soft computing models are obtained from experimental results available in literature. An algorithm is also developed for the optimal NN model selection process. The proposed NN and GEP models are presented in explicit form to be used in practical applications. The accuracy of the proposed soft computing models are compared with existing codes and are found to be more accurate.     

Modeling of transfer length of prestressing strands using genetic programming and neuro-fuzzy

In this study, the efficiency of neuro-fuzzy inference system (ANFIS) and genetic expression programming (GEP) in predicting the transfer length of prestressing strands in prestressed concrete beams was investigated. Many models suggested for the transfer length of prestressing strands usually consider one or two parameters and do not provide consistent accurate prediction. The alternative approaches such as GEP and ANFIS have been recently used to model spatially complex systems. The transfer length data from various researches have been collected to use in training and testing ANFIS and GEP models. Six basic parameters affecting the transfer length of strands were selected as input parameters. These parameters are ratio of strand cross-sectional area to concrete area, surface condition of strands, diameter of strands, percentage of debonded strands, effective prestress and concrete strength at the time of measurement. Results showed that the ANFIS and GEP models are capable of accurately predicting the transfer lengths used in the training and testing phase of the study. The GEP model results better prediction compared to ANFIS model.     

基因表达式编程在软件可靠性建模中的应用

基因表达式编程是一种基于遗传算法和遗传编程的新型机器学习技术,其具有更为优秀的数据挖掘能力,已被成功应用于函数发现领域。提出一种基于基因表达式编程的非参软件可靠性建模方法,该方法将基因表达式编程算法中的若干关键步骤(如初始种群函数集、适应度函数、终止条件等)与软件可靠性建模的若干重要特征相融合,在失效数据集上进行训练,从而获得基于基因表达式编程算法的非参软件可靠性模型。在若干组真实失效数据集上,将所提出的模型与若干典型的基于人工神经网络以及遗传编程的非参软件可靠性模型进行对比实例研究。实例结果表明,基因表达式编程算法的非参软件可靠性模型具有更为显著的模型拟合与预计性能。     

Multi-model ensemble prediction model for carbon efficiency with application to iron ore sintering process

Iron ore sintering is one of the most energy-consuming process in steel industry. Accurate prediction of carbon efficiency for this process is beneficial to energy savings and consumption reduction. Considering the sintering process exhibits strong nonlinearities, multiple parameters, multiple operating conditions, etc., a multi-model ensemble prediction model based on the actual run data is developed to achieve the high-precision prediction of carbon efficiency. It takes the comprehensive coke ratio (CCR) as a metric (index) of carbon efficiency in the sintering process. First, an affinity propagation clustering algorithm is used to realize the automatic identification of multiple operating conditions. Then, different models are established under different operating conditions by using the proposed least squares support vector machine (LS-SVM) with hybrid kernel modeling method. Finally, a partial least-squares regression method is employed as an ensemble strategy to combine the different models to form the multi-model ensemble prediction model for the CCR. The simulation results involving the actual run data demonstrate that the proposed model can predict the CCR accurately when compared with other prediction methods. The results of actual runs show that the coefficient of determination for the proposed model is 0.877. The proposed model satisfies the requirements of actual sintering process and enables the real-time prediction.     

An implicit gas-kinetic scheme for turbulent flow on unstructured hybrid mesh

In this study, an implicit scheme for the gas-kinetic scheme (GKS) on the unstructured hybrid mesh is proposed. The Spalart–Allmaras (SA) one equation turbulence model is incorporated into the implicit gas-kinetic scheme (IGKS) to predict the effects of turbulence. The implicit macroscopic governing equations are constructed and solved by the matrix-free lower-upper symmetric-Gauss–Seidel (LU-SGS) method. To reduce the number of cells and computational cost, the hybrid mesh is applied. A modified non-manifold hybrid mesh data(NHMD) is used for both unstructured hybrid mesh and uniform grid. Numerical investigations are performed on different 2D laminar and turbulent flows. The convergence property and the computational efficiency of the present IGKS method are investigated. Much better performance is obtained compared with the standard explicit gas-kinetic scheme. Also, our numerical results are found to be in good agreement with experiment data and other numerical solutions, demonstrating the good applicability and high efficiency of the present IGKS for the simulations of laminar and turbulent flows.     

基于混合集成建模的硅单晶直径自适应非线性预测控制

大尺寸、电子级直拉硅单晶生长过程中物理变化复杂、多场多相耦合、模型不确定且存在大滞后和非线性等特性, 因此如何实现硅单晶直径控制是一个具有理论意义和实际价值的问题. 本文结合工程实际提出一种基于混合集成建模的晶体直径自适应非线性预测控制方法. 首先, 为了准确辨识晶体直径模型, 提出基于互相关函数的时滞优化估计方法和基于Lipschitz商准则与模型拟合优度的模型阶次辨识方法; 其次, 基于“分而治之”原理构建晶体直径混合集成模型. 其中, 采用小波包分解(Wavelet packet decomposition, WPD)方法将原始数据分解成若干个子序列, 以减少其非平稳性和随机噪声. 极限学习机(Extreme learning machine, ELM)和长短时记忆网络(Long-short-term memory networks, LSTM)分别建立近似(低频)子序列和细节(高频)子序列的预测模型, 最终晶体直径预测输出由各子序列的预测结果汇总而成; 然后, 针对晶体直径混合集成模型失配问题以及目标函数难以求解问题, 提出一种基于蚁狮优化(Ant lion optimizer, ALO)的自适应非线性预测控制策略. 最后, 基于工程实验数据仿真分析, 验证了所提建模及控制方法的有效性.