Control of pH neutralization processes is challenging in the chemical process industry because of their inherent strong nonlinearity. In this paper, the model algorithmic control (MAC) strategy is extended to nonlinea...Control of pH neutralization processes is challenging in the chemical process industry because of their inherent strong nonlinearity. In this paper, the model algorithmic control (MAC) strategy is extended to nonlinear processes using Hammerstein model that consists of a static nonlinear polynomial function followed in series by a linear impulse response dynamic element. A new nonlinear Hammerstein MAC algorithm (named NLH-MAC) is presented in detail. The simulation control results of a pH neutralization process show that NLH-MAC gives better control performance than linear MAC and the commonly used industrial nonlinear propotional plus integral plus derivative (PID) controller. Further simulation experiment demonstrates that NLH-MAC not only gives good control response, but also possesses good stability and robustness even with large modeling errors.展开更多
理解环境过滤、生物相互作用与中性过程如何共同塑造物种分布与群落结构,是现代群落生态学的核心问题。然而,传统多样性指数、排序分析及单物种分布模型(single-species distribution models, SDMs)难以同时整合物种间关联、环境梯度、...理解环境过滤、生物相互作用与中性过程如何共同塑造物种分布与群落结构,是现代群落生态学的核心问题。然而,传统多样性指数、排序分析及单物种分布模型(single-species distribution models, SDMs)难以同时整合物种间关联、环境梯度、性状与谱系等多维信息,导致对群落构建机制的解析能力受限。联合物种分布模型(joint species distribution models, JSDMs)特别是生物群落层次建模(hierarchical modelling of species communities, HMSC)框架的提出,为群落尺度的机制推断提供了统一而灵活的贝叶斯工具。本文系统综述了HMSC的统计结构、数学原理与推断机制,构建了一个从数据组织、模型设定、马尔可夫链蒙特卡洛(Markov Chain Monte Carlo, MCMC)估计、模型评估到生态解释与预测的完整分析流程。同时结合苔藓群落数据配套编写了《联合物种分布模型HMSC的应用分步教程》,通过分步讲解与可运行R代码,助力研究者快速掌握该方法的实操应用。在理论部分,本文明确了HMSC如何在统一的贝叶斯层级框架下整合环境梯度、物种性状、系统发育关系以及空间结构,从而分离环境过滤、生物过滤与扩散限制的统计信号。在方法层面,本文通过解析潜变量模型的数学结构,阐明了残差相关在生态解释中的边界,为理解物种共现信号、区分环境效应与未观测因子提供了理论依据;对比了HMSC与其他主流JSDMs工具及传统群落统计方法的优势及适用性。在应用层面,综述了其在森林、湿地、草原、海洋、城市及微生物生态学中的应用进展,展示了其在保护规划、入侵种风险评估、共现网络分析及情景预测中的广泛价值;随着图形处理器加速与迁移学习与大规模高维数据框架的发展, HMSC可提升稀有物种生态位估计与分布预测,使数十万物种的群落建模成为可能。综上, JSDMs及HMSC不仅在生态统计方法论上实现了从单物种预测到多物种-多维信息整合的跨越,更为生态理论检验、群落构建机制解析及保护决策制定提供了高效、可扩展且能量化不确定性的工具平台。展开更多
文摘Control of pH neutralization processes is challenging in the chemical process industry because of their inherent strong nonlinearity. In this paper, the model algorithmic control (MAC) strategy is extended to nonlinear processes using Hammerstein model that consists of a static nonlinear polynomial function followed in series by a linear impulse response dynamic element. A new nonlinear Hammerstein MAC algorithm (named NLH-MAC) is presented in detail. The simulation control results of a pH neutralization process show that NLH-MAC gives better control performance than linear MAC and the commonly used industrial nonlinear propotional plus integral plus derivative (PID) controller. Further simulation experiment demonstrates that NLH-MAC not only gives good control response, but also possesses good stability and robustness even with large modeling errors.
文摘理解环境过滤、生物相互作用与中性过程如何共同塑造物种分布与群落结构,是现代群落生态学的核心问题。然而,传统多样性指数、排序分析及单物种分布模型(single-species distribution models, SDMs)难以同时整合物种间关联、环境梯度、性状与谱系等多维信息,导致对群落构建机制的解析能力受限。联合物种分布模型(joint species distribution models, JSDMs)特别是生物群落层次建模(hierarchical modelling of species communities, HMSC)框架的提出,为群落尺度的机制推断提供了统一而灵活的贝叶斯工具。本文系统综述了HMSC的统计结构、数学原理与推断机制,构建了一个从数据组织、模型设定、马尔可夫链蒙特卡洛(Markov Chain Monte Carlo, MCMC)估计、模型评估到生态解释与预测的完整分析流程。同时结合苔藓群落数据配套编写了《联合物种分布模型HMSC的应用分步教程》,通过分步讲解与可运行R代码,助力研究者快速掌握该方法的实操应用。在理论部分,本文明确了HMSC如何在统一的贝叶斯层级框架下整合环境梯度、物种性状、系统发育关系以及空间结构,从而分离环境过滤、生物过滤与扩散限制的统计信号。在方法层面,本文通过解析潜变量模型的数学结构,阐明了残差相关在生态解释中的边界,为理解物种共现信号、区分环境效应与未观测因子提供了理论依据;对比了HMSC与其他主流JSDMs工具及传统群落统计方法的优势及适用性。在应用层面,综述了其在森林、湿地、草原、海洋、城市及微生物生态学中的应用进展,展示了其在保护规划、入侵种风险评估、共现网络分析及情景预测中的广泛价值;随着图形处理器加速与迁移学习与大规模高维数据框架的发展, HMSC可提升稀有物种生态位估计与分布预测,使数十万物种的群落建模成为可能。综上, JSDMs及HMSC不仅在生态统计方法论上实现了从单物种预测到多物种-多维信息整合的跨越,更为生态理论检验、群落构建机制解析及保护决策制定提供了高效、可扩展且能量化不确定性的工具平台。
