Projection pursuit cluster model and its application in water quality assessment

One of the difficulties frequently encountered in water quality assessment is that there are many factors and they cannot be assessed according to one factor, all the effect factors associated with water quality must be used. In order to overcome this issues the projection pursuit principle is introduced into water quality assessment, and projection pursuit cluster(PPC) model is developed in this study. The PPC model makes the transition from high dimension to one-dimension. In other words, based on the PPC model, multifactor problem can be converted to one factor problem. The application of PPC model can be divided into four parts: (1) to estimate projection index function Q(); (2) to find the right projection direction ; (3) to calculate projection characteristic value of the i th sample z-i, and (4) to draw comprehensive analysis on the basis of z-i. On the other hand, the empirical formula of cutoff radius R is developed, which is benefit for the model to be used in practice. Finally, a case study of water quality assessment is proposed in this paper. The results showed that the PPC model is reasonable, and it is more objective and less subjective in water quality assessment. It is a new method for multivariate problem comprehensive analysis.

Projection Pursuit Dynamic Cluster Model and its Application to Water Resources Carrying Capacity Evaluation

The research shows that projection pursuit cluster (PPC) model is able to form a suitable index for overcom-ing the difficulties in comprehensive evaluation, which can be used to analyze complex multivariate prob-lems. The PPC model is widely used in multifactor cluster and evaluation analysis, but there are a few prob-lems needed to be solved in practice, such as cutoff radius parameter calibration. In this study, a new model-projection pursuit dynamic cluster (PPDC) model-based on projection pursuit principle is developed and used in water resources carrying capacity evaluation in China for the first time. In the PPDC model, there are two improvements compared with the PPC model, 1) a new projection index is constructed based on dynamic cluster principle, which avoids the problem of parameter calibration in the PPC model success-fully;2) the cluster results can be outputted directly according to the PPDC model, but the cluster results can be got based on the scatter points of projected characteristic values or the re-analysis for projected character-istic values in the PPC model. The results show that the PPDC model is a very effective and powerful tool in multifactor data exploratory analysis. It is a new method for water resources carrying capacity evaluation. The PPDC model and its application to water resources carrying capacity evaluation are introduced in detail in this paper.

Application of Projection Pursuit Evaluation Model Based on Real-Coded Accelerating Genetic Algorithm in Evaluating Wetland Soil Quality Variations in the Sanjiang Plain, China

A new technique of dimension reduction named projection pursuit is applied to model and evaluatewetland soil quality variations in the Sanjiang Plain, Helongjiang Province, China. By adopting the im-proved real-coded accelerating genetic algorithm (RAGA), the projection direction is optimized and multi-dimensional indexes are converted into low-dimensional space. Classification of wetland soils and evaluationof wetland soil quality variations are realized by pursuing optimum projection direction and projection func-tion value. Therefore, by adopting this new method, any possible human interference can be avoided andsound results can be achieved in researching quality changes and classification of wetland soils.

Approach to Weighted Geometric Evaluation Based on Projection Pursuit

Weighted geometric evaluation approach based on Projection pursuit (PP) model is presented in this paper to optimize the choice of schemes. By using PP model, the multi-dimension evaluation index values of schemes can be synthesized into projection value with one dimension. The scheme with a bigger projection value is much better, so the schemes sample can be an optimized choice according to the projection value of each scheme. The modeling of PP based on accelerating genetic algorithm can predigest the realized process of projection pursuit technique, can overcome the shortcomings of large computation amount and the difficulty of computer programming in traditional projection pursuit methods, and can give a new method for application of projection pursuit technique to optimize choice of schemes by using weighted geometric evaluation. The analysis of an applied sample shows that applying PP model driven directly by samples data to optimize choice of schemes is both simple and feasible, that its projection values are relatively decentralized and profit decision-making, that its applicability and maneuverability are high. It can avoid the shortcoming of subjective weighing method, and its results are scientific and objective.

基于WTD-PPAR的中国碳排放预测研究

碳排放预测对我国的碳排放规划和政策颁布具有一定的参考价值。使用小波阈值去噪模型对碳排放数据建模能够有效缓解其存在的非线性和波动性问题,筛选得到数据的有效增长趋势。在此基础上,利用碳排放数据的时间序列特征建立其投影寻踪自回归(PPAR)模型,从而对碳排放进行预测分析。将该模型与未进行去噪的PPAR、BP、LSSVM、SVR、LSTM模型进行对比,发现WTD-PPAR模型的预测精度更高,预测结果更准确。分析结果显示,我国碳排放将在2029年达到峰值,约为1081.89 mt,能够实现碳达峰的目标。

基于博弈论原理的PP-AHP权重优化模型在河流健康评价中的应用

为了解决传统层次分析法在指标权重赋值中主观意识影响过大导致河流健康评价结果具有较强主观随意性的问题,本文利用客观性较强的投影寻踪法来改进层次分析法,综合考虑主客观影响,通过博弈论原理对指标权重进行优化,并运用优化模型权重对扬州市城区三条城市河流进行了丰枯期健康评价。结果表明:优化后的权重能综合主、客观权重的特点,使评价结果更加合理;在进行权重优化的过程中,得到最优加权系数分别为0.5435、0.4565。为了简化权重处理步骤,同时选取0.5、0.5作为加权系数进行等权处理,该处理对评价结果影响较小,可为今后河流建康评价中的主客观权重耦合提供一定的参考。

基于改进PPC模型的江苏省水资源承载力动态评价

为切实解决水资源承载力分类评价的高维问题,揭示水资源承载力动态变化规律,提出了改进的投影寻踪聚类模型。对于改进的投影寻踪聚类模型和基于最大信息熵原理的投影寻踪聚类模型,通过分析投影值的负熵变化规律,推导得出了密度窗宽的合理取值范围以及最佳取值;当密度窗宽为最佳取值时,改进的投影寻踪聚类模型比基于最大信息熵原理的投影寻踪聚类模型分类评价效果更好。针对江苏省水资源承载状态,采用改进的投影寻踪聚类模型进行了动态评价,2009年至2020年、2022年水资源承载力等级为Ⅲ级,2021年水资源承载力等级为Ⅱ级。建立灰色GM(1,1)模型,预测该省2023年至2030年水资源承载力状态为Ⅱ级。改进的投影寻踪聚类模型更加有效地提取了水资源承载力评价指标高维数据的结构特征信息,进一步提升了水资源承载力分类评价模型的精确性,使评价结果更加客观合理。通过2022年与2009年指标贡献率数值比较分析,该省采取的工农业节水、加快社会经济发展、水资源保护等措施,促进了水资源承载力状态持续转好。根据评价指标对江苏省水资源承载力状态以及评价标准的贡献率,深入分析该省水资源承载力存在的短板,提出了提升水资源承载力状态的相关建议,确保江苏省水资源系统承载力状态尽早达到Ⅰ级,实现水资源可持续利用。

基于FA-ISSA-PPR模型的旋风分离器分离效率预测

旋风分离器是气田开发中常用的气固分离设备,准确预测旋风分离器的分离效率对于指导其结构设计和方法优化具有重要意义。在对数据集进行相关性分析的基础上,采用因子分析(factor analysis, FA)简化变量,降低预测模型的复杂程度,利用改进的樽海鞘群算法(improved salp swarm algorithm, ISSA)对投影寻踪(projection pursuit regression, PPR)的模型参数进行优化,形成FA-ISSA-PPR组合模型。结果表明,利用FA模型,原数据集的10个变量可以简化合并为4个公因子,分别代表尺寸参数、颗粒沉降特性、粒子运行轨迹和等效分割粒径对分离效率的影响;与半经验模型和其余机器学习模型相比,组合模型在预测精度和训练时间上具有一定的优越性,在测试样本上的平均绝对误差(MAE)为0.005 91,R^(2)可达0.995,证明了其在小样本、非线性数据分析上的准确性、鲁棒性和泛化性。

A NEW APPROACH FOR UNSUPERVISED RESTORING IMAGES BASED ON WAVELET-DOMAIN PROJECTION PURSUIT LEARNING NETWORK

The Wavelet-Domain Projection Pursuit Learning Network (WDPPLN) is proposedfor restoring degraded image. The new network combines the advantages of both projectionpursuit and wavelet shrinkage. Restoring image is very difficult when little is known about apriori knowledge for multisource degraded factors. WDPPLN successfully resolves this problemby separately processing wavelet coefficients and scale coefficients. Parameters in WDPPLN,which are used to simulate degraded factors, are estimated via WDPPLN training, using scalecoefficients. Also, WDPPLN uses soft-threshold of wavelet shrinkage technique to suppress noisein three high frequency subbands. The new method is compared with the traditional methodsand the Projection Pursuit Learning Network (PPLN) method. Experimental results demonstratethat it is an effective method for unsupervised restoring degraded image.

基于Projection Pursuit-RAGA-NK-GERT的质量绩效提升路径优化——以组织质量特异性免疫和产品生命周期为主导逻辑的解释框架

依据组织免疫和组织质量特异性免疫的相关理论,引入情境变量产品生命周期,构建基于组织质量监视、组织质量防御软要素、组织质量防御硬要素和组织质量记忆的质量绩效提升路径理论框架。使用基于RAGA的投影寻踪模型、适应度景观和NK模型对产品生命周期不同阶段的质量绩效提升路径进行实证分析,实证结果表明:产品生命周期不同阶段有不同的质量绩效提升路径,产品导入期遵循的质量绩效提升路径为组织质量监视→组织质量防御→组织质量记忆,产品成长期遵循的质量绩效提升路径为组织质量防御软要素→组织质量防御硬要素→组织质量监视→组织质量记忆,产品成熟期遵循的质量绩效提升路径为组织质量防御硬要素→组织质量防御软要素→组织质量监视→组织质量记忆,产品衰退期遵循的质量绩效提升路径为组织质量记忆→组织质量监视→组织质量防御。并建立产品生命周期不同阶段质量绩效提升路径优化的GERT网络,优化产品生命周期不同阶段的质量绩效提升路径。

基于PP-GA的乡村发展潜力评价模型与应用

通过分析乡村发展潜力的内涵特点,构建乡村发展潜力综合评价模型,提出基于投影寻踪模型和遗传算法的评价方法,以安徽省来安县为例,应用评价模型和方法计算来安县乡村发展潜力指数,将全县划分为高、较高、中等、较低和低5类乡村发展潜力分区,进而将来安县乡村分为集聚提升、城郊融合、特色保护与搬迁撤并4类,并提出针对性策略。研究结果可为乡村发展潜力评价、乡村规划分类提供理论方法参考和实践应用借鉴。

IPP-PNN模型在川藏铁路深埋长大隧道岩爆预测中的应用

为了准确预测在高地应力、高地温铁路隧道中的岩爆灾害,以川藏铁路前期拉林段的重要隧道节点工程为研究背景,系统、全面地总结应力水平、埋深、温度、围岩岩性及地质构造、岩体系统刚度等影响因素对川藏铁路深埋长大隧道岩爆的孕育作用,重点分析高地应力和高地温对岩爆发生的影响相关性。构建川藏铁路深埋长大隧道岩爆预测指标体系,测试并量化岩体岩爆的倾向性指标。由于各影响因素与岩爆的非线性关系,选用能充分提取数据信息、处理多因素复杂非线性问题的改进投影寻踪(Improved Projection Pursuit,IPP)评价模型对川藏铁路拉林段典型高地应力、高地温深埋长大隧道桑珠岭隧道在施工期发生的岩爆问题做初步评价,并引入密度函数估计和贝叶斯最小风险准则,将IPP模型和概率神经网络(Probabilistic Neural Networks,PNN)模型相结合,实现对岩爆等级的聚类划分。研究结果表明:根据岩爆等级预测结果可知IPP-PNN模型预测结果相比于传统PP-PNN模型和GSA-PP模型其准确度更高,在对桑珠岭隧道11~19号隧道路段的岩爆预测中,岩爆预测等级与实测等级相符合程度由66.67%和77.78%提高到100%。研究结果具有一定的应用价值和工程意义,为目前在建的川藏铁路类似隧道工程的岩爆预测提供参考。

基于PPR数据建模技术的砂砾石料应力应变规律拟合

土的本构关系是岩土工程界备受关注的重要理论之一,其应力应变关系的准确描述是本构模型的关键问题。本工作基于大型静三轴试验,研究了两种不同级配砂砾石料的应力应变关系,采用PPR无假定数据建模技术对试验结果进行了建模拟合,并与邓肯-张E-B双曲线模型进行对比分析。结果表明:PPR数据建模技术能够较好地拟合砂砾石料在三轴试验下的应力应变关系,计算值与试验值的相对误差小于6%时的合格率达到90%以上,偏应力与体应变的最大相对误差分别为13.2%和20.9%,相较邓肯-张双曲线模型具有更高的精度;PPR模型能准确描述砂砾石料受剪切过程中的体胀特性,并且对三轴试验结果具有较好的预测功能。本工作提出了一种数据建模方法来表示土体应力应变关系,研究结果为岩土工程材料的应力应变关系描述提供了新的思路。

基于KH-PP的城市多元包容评价研究——以华北某城市体检为例

以华北地区某城市关于城市体检中的多元包容指标为研究对象,结合熵值法对城市多元包容的评价指标进行计算,建立磷虾群算法-投影寻踪模型确定指标权重,与华北地区相邻10个城市的多元包容发展水平进行对比,判断相关指标对城市包容性发展水平的影响程度。对于不同发展水平的城市因地制宜地提出不同的发展要求。

基于ISSO-PP组合赋权的双向区间约束正态云模型的换流站阀冷系统冷却能力研究

阀冷系统是保证换流站安全运行的关键一环,其作用在于排出阀体中元件形成的热量,因此其冷却能力的准确评估至关重要。针对上述问题,提出基于ISSO-PP组合赋权的双向区间约束正态云模型的换流阀冷却能力评价模型。首先,对采集数据预处理并确定阀冷系统冷却能力评价指标体系;其次,利用层次分析法和改进CRITIC法计算评价指标主、客观权重,并由改进鲨鱼优化投影寻踪法(ISSO-PP)得到综合权重;最后,由数据等级评价标准为中间型这一特性,提出双向区间约束的正向云数字特征计算方法,同时对逆向云数字特征计算方法进行改进,结合综合权重计算得到最终隶属度,依据最大隶属度原则确定最终评价结果。

Regional partitioning of agricultural non-point source pollution in China using a projection pursuit cluster model

A projection pursuit cluster（PPC） model was used to analyze the regional partitioning of agricultural non-point source pollution in China.The environmental factors impacting the agricultural non-point source pollution were compiled into a projection index to set up the projection index function.A novel optimization algorithm called Free search（FS） was introduced to optimize the projection direction of the PPC model.By making the appropriate improvements as we explored the use of the algorithm,it became simpler,and developed better exploration abilities.Thus,the multi-factor problem was converted into a single-factor cluster,according to the projection,which successfully avoided subjective disturbance and produced objective results.The cluster results of the PPC model mirror the actual regional partitioning of the agricultural non-point source pollution in China,indicating that the PPC model is a powerful tool in multi-factor cluster analysis,and could be a new method for the regional partitioning of agricultural non-point source pollution.

Projection Pursuit Flood Disaster Classification Assessment Method Based on Multi-Swarm Cooperative Particle Swarm Optimization

The indicators of flood damage assessment in the flood classification are often incompatible, and it is very difficult to use those indicators value directly for classification assessment. Projection pursuit technology can project higher dimensional incompatible data into lower dimensional sub-space, and find the projection values for optimal projection index function to get the higher dimensional data structure features, which has been improved to be reasonable and effective for flood disaster classification assessment. However, it is a bit difficult to optimize the parameters of projection index functions, as a result, that limits the applications of this method. As an emerging heuristic global optimization algorithm based on swarm intelligence, particle swarm optimization algorithm has the ability of solving complex optimization problem, but it still be easily convergent early, and can not search the global optimal solution. In this paper, a flood disaster classification assessment method based on multi-swarm cooperative particle swarm optimization is proposed, which adopts a tri-parameter Logistic curve to construct the flood disaster projection pursuit model, and uses mul-ti-swarm system particle swarm optimization method to optimize the parameters of the projection index functions. The typical test function experiment shows that this optimization method can solve the early convergence commonly found in standard particle swarm optimization algorithm, which global optimized ability is improved greatly. Applied in flood disaster assessment in HeNan Province, the results using this method comparing with others indicates that it can assess effectively the flood disaster, and has better assessment accuracy and disaster resolution.

On grey relation projection model based on projection pursuit

Multidimensional grey relation projection value can be synthesized as one-dimensional projection value by using projection pursuit model. The larger the projection value is,the better the model. Thus,according to the projection value,the best one can be chosen from the model aggregation. Because projection pursuit modeling based on accelerating genetic algorithm can simplify the implementation procedure of the projection pursuit technique and overcome its complex calculation as well as the difficulty in implementing its program,a new method can be obtained for choosing the best grey relation projection model based on the projection pursuit technique.

CLUSTERING VIA DIMENSIONAL REDUCTION METHOD FOR THE PROJECTION PURSUIT BASED ON THE ICSA

The performance of the classical clustering algorithm is not always satisfied with the high-dimensional datasets, which make clustering method limited in many application. To solve this problem, clustering method with Projection Pursuit dimension reduction based on Immune Clonal Selection Algorithm (ICSA-PP) is proposed in this paper. Projection pursuit strategy can maintain consistent Euclidean distances between points in the low-dimensional embeddings where the ICSA is used to search optimizing projection direction. The proposed algorithm can converge quickly with less iteration to reduce dimension of some high-dimensional datasets, and in which space, K-mean clustering algorithm is used to partition the reduced data. The experiment results on UCI data show that the presented method can search quicker to optimize projection direction than Genetic Algorithm (GA) and it has better clustering results compared with traditional linear dimension reduction method for Principle Component Analysis (PCA).