基于RBF网络的北方温室温湿度控制机理的研究
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摘要
温室环境模型是温室结构设计与环境控制的重要基础,由于温室系统是一个具有强耦合、大惯性、非线性的复杂大系统,采用机理分析法,难以建立其准确的数学模型,致使温室环境控制效果不理想、生产能耗大。神经网络建模能更灵活地获得温室系统的参数和非线性。目前我国对温室环境建模与控制的研究多数集中在南方地区,而研究北方温室环境建模与控制的较少,北方温室加热能耗大,制约北方温室的发展,尤其在春秋季节昼夜温差大,控制变量耦合强,建模和控制难度大。因此,本文针对北方温室环境的控制机理进行了深入研究。主要研究内容如下：
由于机理法难以建立准确的温室环境模型,而RBF网络具有结构简单、能逼近任意非线性过程的特性,因此,本文将RBF网络结构与优化算法相结合,采用了不同的算法学习与优化网络结构和参数,建立了北方温室环境模型。OLS算法具有计算量小、运算速度快、能够找到较小的网络结构的特点,在设计网络结构时,选择的隐中心与样本的正交化顺序有关,因此,不能找到最优的网络结构。
RBF网络性能只与所选的隐中心有关,而与隐中心的选择顺序无关。因此,可以将隐中心的选择看作为组合优化问题,进化策略在搜索过程中主要采用选择和变异操作,适合于隐中心的寻优,本文采用进化策略在整个样本集中搜索最优的隐节点数和隐中心,建立了温室环境RBF网络模型。
如果样本中包含较强的噪声数据,网络模型可能会过多地拟合噪声数据,影响网络的泛化性能,正则化方法是改善网络泛化性能的有效方法,而正则化系数需要优化确定；基于OLS算法和ES算法的网络模型都是在扩展常数确定的情况下,进行网络结构的设计,而扩展常数的优劣也直接影响着网络的性能。因此,本文采用粒子群算法全局优化正则化系数和扩展常数,正则化OLS算法利用所得优化结果设计网络结构。
RBF网络的输出层为线性层,通常采用线性最小二乘法求其回归系数,但是,如果温室系统输入变量间有较高程度的相关性,或出于计算成本考虑而样本点容量不充足,将会使网络模型的回归系数误差增加,模型精度降低。PLS算法能够对输入信息进行筛选和重组,有效地解决变量间的多重相关性或样本点不充足问题。因此,本文采用了PLS回归算法提取输入变量的主成分,主成分间是直交互补的,利用主成分进行网络模型的回归,主成分提取和隐中心分级确定。
北方温室生产的能耗制约着温室农业的发展,目前,温室温度控制通常强调控制精度,而忽视了生产能耗。利用作物的积温特性,可以大量减少生产能耗。作物的积温控制受湿度控制的影响较大,本文根据蝴蝶兰的生活习性和积温控制原则,研究并制定了北方温室春季环境的积温控制策略,为北方温室环境的优化调控及节能生产奠定理论基础。
针对传统控制理论和现代控制理论在温室环境控制应用中存在的问题,本文根据所得的温室环境模型和积温控制策略,完成了北方温室环境神经网络控制器的设计,控制器根据温度和湿度的偏差,调节各执行设备,采用正交最小二乘法加快网络学习速度,妥善解决了湿度等影响因素对积温控制的影响。
通过对北方温室温湿度模型和控制器的研究,为北方温室农业的健康发展和可持续生产,提供了理论和技术基础。
Greenhouse environment models are the important basis of greenhouse structural design and environmental control. Greenhouse is a complex system with characteristics of strong coupling, long delay and nonlinear. Therefore, mechanism analysis methods difficultly established its models. This caused high-energy consumption for greenhouse production and unsatisfactory control effect. Neural network (NN) modeling technology can flexibilly acquire parameters and nonlinear characteristics of greenhouse. Now research objects on greenhouse environmental model and control mostly lay in southern region of china. Similar study on northern greenhouse was deficient. High-energy consumption for northern greenhouse production restricts its development, temperature difference between day and night in spring and autumn is great, variables strongly couple each other, modeling and control for it is very difficult. Environmental control mechanism of northern greenhouse was furtherlly studied in this dissertation. Main topics are as follows:
According to the problem that mechanism analysis methods difficultilly establish accurate model of greenhouse. Because radial basis function NN (RBFNN) has such characteristics that simpler structure and approximating any nonlinear procedure. RBFNN structure and different optimal algorithms were combined to learn and optimize NN structure and parameters, northern greenhouse models were established in this dissertation. OLS algorithm has such characteristics that smaller coumputing amount, quicker operation speed and designing simpler NN structure. When OLS algorithm designs structure of NN, hidden centers selected are related to orthogonalized order. Therefore, it can not establish the simplest structure of NN.
In fact, RBFNN performances are only related to hidden centers selected, not related to its'order. Hence, selection of hidden centers is thought as combination optimization problem. ES mainly use selection and variation operation during evolutionary, this is fit for optimizing hidden unit centers. ES algorithm was applied to search the optimal hidden centers and its number in sample set. RBFNN model of northern greenhouse was established.
If stronger noise data were included in sample set, NN may overfit these data to decrease its generalization. Regularizing method is an effective measure to improve NN generalization. But regularizing factor must be optimized. NN models based on OLS and ES algorithm were designed after spreads of RBF were assumed. Accuracy of spreads influences NN performance. Therefore, PSO algorithm was used to optimize regularizing factor and spreads, ROLS algorithm used optimal results to design model structure in this dissertation.
The output layer of RBFNN is linear layer, its output weights are usually acquired by linear least squares algorithm. However, greenhouse is a large-scale system with multiple input-output. If strong correlation among input variables exists, or data quantity is deficient for calculating cost, this will increase error of model regression coefficients, and decrease model accuracy. PLS algorithm can select and recombine the input information, effectively resolve the problems mentioned above. Hence, PLS algorithm was applied to extract principal components from the input variables. These components are orthogonal and complementary. The model was regressed by these components. These principal components and hidden centers were hierarchically determined.
The development of northern greenhouse was restricted by energy consumption for production. Now control accuracy of temperature in greenhouse was usually emphasized, but energy consumption was ignored. Large mount of energy is saved by means of the characteristics of integrating temperature. Humidity control greatly restricts application of integrating temperature control. According to growing habit of phalaenopsis aphrodite and control scheme of integrating temperature, integrating temperature control schemes of northern greenhouse environment during spring were studied in this dissertation. These lay theoretical basis for controlling greenhouse environment and energy-saving production.
In accordance with the problems of traditional and modern control theory applied on greenhouse environment control, according to the model and control schemes acquired in this dissertation, NN controller was designed to regulate the temperature and humidity in northern greenhouse. NN controller regulates all actuators according to the bias of temperature and humidity. OLS algorithm was applied to accelerate NN learing. The effect of humidity on integrating temperature control was perfectly resolved.
Environmental model and controller of northern greenhouse are studied in this dissertation. These research results provide theoretical and technical basis for healthy development and sustainable production of greenhouse agriculture in northern region.
由于机理法难以建立准确的温室环境模型,而RBF网络具有结构简单、能逼近任意非线性过程的特性,因此,本文将RBF网络结构与优化算法相结合,采用了不同的算法学习与优化网络结构和参数,建立了北方温室环境模型。OLS算法具有计算量小、运算速度快、能够找到较小的网络结构的特点,在设计网络结构时,选择的隐中心与样本的正交化顺序有关,因此,不能找到最优的网络结构。
RBF网络性能只与所选的隐中心有关,而与隐中心的选择顺序无关。因此,可以将隐中心的选择看作为组合优化问题,进化策略在搜索过程中主要采用选择和变异操作,适合于隐中心的寻优,本文采用进化策略在整个样本集中搜索最优的隐节点数和隐中心,建立了温室环境RBF网络模型。
如果样本中包含较强的噪声数据,网络模型可能会过多地拟合噪声数据,影响网络的泛化性能,正则化方法是改善网络泛化性能的有效方法,而正则化系数需要优化确定；基于OLS算法和ES算法的网络模型都是在扩展常数确定的情况下,进行网络结构的设计,而扩展常数的优劣也直接影响着网络的性能。因此,本文采用粒子群算法全局优化正则化系数和扩展常数,正则化OLS算法利用所得优化结果设计网络结构。
RBF网络的输出层为线性层,通常采用线性最小二乘法求其回归系数,但是,如果温室系统输入变量间有较高程度的相关性,或出于计算成本考虑而样本点容量不充足,将会使网络模型的回归系数误差增加,模型精度降低。PLS算法能够对输入信息进行筛选和重组,有效地解决变量间的多重相关性或样本点不充足问题。因此,本文采用了PLS回归算法提取输入变量的主成分,主成分间是直交互补的,利用主成分进行网络模型的回归,主成分提取和隐中心分级确定。
北方温室生产的能耗制约着温室农业的发展,目前,温室温度控制通常强调控制精度,而忽视了生产能耗。利用作物的积温特性,可以大量减少生产能耗。作物的积温控制受湿度控制的影响较大,本文根据蝴蝶兰的生活习性和积温控制原则,研究并制定了北方温室春季环境的积温控制策略,为北方温室环境的优化调控及节能生产奠定理论基础。
针对传统控制理论和现代控制理论在温室环境控制应用中存在的问题,本文根据所得的温室环境模型和积温控制策略,完成了北方温室环境神经网络控制器的设计,控制器根据温度和湿度的偏差,调节各执行设备,采用正交最小二乘法加快网络学习速度,妥善解决了湿度等影响因素对积温控制的影响。
通过对北方温室温湿度模型和控制器的研究,为北方温室农业的健康发展和可持续生产,提供了理论和技术基础。
Greenhouse environment models are the important basis of greenhouse structural design and environmental control. Greenhouse is a complex system with characteristics of strong coupling, long delay and nonlinear. Therefore, mechanism analysis methods difficultly established its models. This caused high-energy consumption for greenhouse production and unsatisfactory control effect. Neural network (NN) modeling technology can flexibilly acquire parameters and nonlinear characteristics of greenhouse. Now research objects on greenhouse environmental model and control mostly lay in southern region of china. Similar study on northern greenhouse was deficient. High-energy consumption for northern greenhouse production restricts its development, temperature difference between day and night in spring and autumn is great, variables strongly couple each other, modeling and control for it is very difficult. Environmental control mechanism of northern greenhouse was furtherlly studied in this dissertation. Main topics are as follows:
According to the problem that mechanism analysis methods difficultilly establish accurate model of greenhouse. Because radial basis function NN (RBFNN) has such characteristics that simpler structure and approximating any nonlinear procedure. RBFNN structure and different optimal algorithms were combined to learn and optimize NN structure and parameters, northern greenhouse models were established in this dissertation. OLS algorithm has such characteristics that smaller coumputing amount, quicker operation speed and designing simpler NN structure. When OLS algorithm designs structure of NN, hidden centers selected are related to orthogonalized order. Therefore, it can not establish the simplest structure of NN.
In fact, RBFNN performances are only related to hidden centers selected, not related to its'order. Hence, selection of hidden centers is thought as combination optimization problem. ES mainly use selection and variation operation during evolutionary, this is fit for optimizing hidden unit centers. ES algorithm was applied to search the optimal hidden centers and its number in sample set. RBFNN model of northern greenhouse was established.
If stronger noise data were included in sample set, NN may overfit these data to decrease its generalization. Regularizing method is an effective measure to improve NN generalization. But regularizing factor must be optimized. NN models based on OLS and ES algorithm were designed after spreads of RBF were assumed. Accuracy of spreads influences NN performance. Therefore, PSO algorithm was used to optimize regularizing factor and spreads, ROLS algorithm used optimal results to design model structure in this dissertation.
The output layer of RBFNN is linear layer, its output weights are usually acquired by linear least squares algorithm. However, greenhouse is a large-scale system with multiple input-output. If strong correlation among input variables exists, or data quantity is deficient for calculating cost, this will increase error of model regression coefficients, and decrease model accuracy. PLS algorithm can select and recombine the input information, effectively resolve the problems mentioned above. Hence, PLS algorithm was applied to extract principal components from the input variables. These components are orthogonal and complementary. The model was regressed by these components. These principal components and hidden centers were hierarchically determined.
The development of northern greenhouse was restricted by energy consumption for production. Now control accuracy of temperature in greenhouse was usually emphasized, but energy consumption was ignored. Large mount of energy is saved by means of the characteristics of integrating temperature. Humidity control greatly restricts application of integrating temperature control. According to growing habit of phalaenopsis aphrodite and control scheme of integrating temperature, integrating temperature control schemes of northern greenhouse environment during spring were studied in this dissertation. These lay theoretical basis for controlling greenhouse environment and energy-saving production.
In accordance with the problems of traditional and modern control theory applied on greenhouse environment control, according to the model and control schemes acquired in this dissertation, NN controller was designed to regulate the temperature and humidity in northern greenhouse. NN controller regulates all actuators according to the bias of temperature and humidity. OLS algorithm was applied to accelerate NN learing. The effect of humidity on integrating temperature control was perfectly resolved.
Environmental model and controller of northern greenhouse are studied in this dissertation. These research results provide theoretical and technical basis for healthy development and sustainable production of greenhouse agriculture in northern region.
引文
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