微粒群优化算法与动态神经网络建模预测研究
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摘要
随着现代科学技术的快速发展,控制系统复杂度越来越高,规模越来越大,已经无法采用精确的数学模型描述各种工业被控对象,常规控制方法已经难以满足人类对自动控制系统的要求。如何快速精确地对模型未知非线性延迟系统建模,进而实施准确高效的预测控制,己成为当今控制领域的一个重要难题。作为一种群体智能领域内新型的寻优方法,微粒群在模型参数优化问题中性能卓越。因此,本文在重点研究微粒群优化算法的基础上,将其与模糊C均值聚类方法用于动态神经网络离线或在线的大规模参数寻优,从而构建模型未知非线性延迟系统建模和预测控制方法,以期获得更加适应于实际工业过程需求的控制方案。主要研究内容包括以下三个方面：
(1)提出基于高斯微粒群算法的模型参数在线优化方法。微粒群算法结构简单不受目标函数连续可导等限制,计算速度快可扩展性强。本文基于高斯函数和混沌映射提出一种高斯微粒群算法,该方法能够自适应调整微粒搜索范围,不仅可以保持算法前期的全局搜索能力,又可以保证算法后期的持续局部搜索能力,解决“早熟”问题。进一步,利用鲁棒控制理论对高斯微粒群算法稳定性条件进行分析。通过各类型标准函数测试对比,所提高斯微粒群方法在函数优化精度方面得到显著的提高。在此基础上,将高斯微粒群算法用于回声状态神经网络在线参数优化问题中,随着输入数据在线调整网络参数,克服离线训练神经网络无法随环境变化及时调整的弊端,通过非线性延迟系统和混沌时间序列等数据验证所提方法的有效性。
(2)提出基于模糊C均值聚类的微粒群大规模参数优化方法。针对已有经典方法在大规模参数优化中存在“维数灾难”等问题,本文将微粒群算法与模糊C均值聚类相结合,提出动态多种群协同微粒群优化方法。首先,针对模糊C均值聚类算法对初始点敏感的问题,利用线性分配的思想进行初始点选择,增强算法的稳定性,提出两阶段模糊聚类算法。然后提出利用部分标记样本进行单点逼近和加权的半监督模糊C均值算法,通过机器学习、图像分割标准数据库和实际湿地遥感数据验证所提方法的有效性。在此基础上,采用改进模糊C均值算法自适应构造多种群算法结构,并结合信赖域方法调整搜索空间,将大规模参数优化问题分解为多个子问题进行协同寻优和信息共享。最终利用大规模标准测试函数分析优化结果,验证所提方法具有较好的优化能力。
(3)提出基于微粒群优化的动态前馈神经网络预测控制方法。为了提高神经网络的预测精度,本文提出一种动态前馈神经网络结构,通过引入动态延迟算子增强神经网络的动态表达能力,而且可以辨识出延迟系统中包含的延迟时间。此外,将高斯微粒群方法用于所提动态前馈神经网络参数优化,加快网络收敛速度,进而深入分析两者结合之后模型的稳定性。另外针对较复杂被控对象时,采用多种群协同大规模微粒群算法优化动态前馈神经网络模型参数,并将其作为辨识器和预估器分别应用于所提出的Smith双控制器预测控制框架和多变量有约束模型预测控制结构之中。仿真实例验证所提控制方法可以对模型未知非线性延迟系统进行有效地辨识和预测控制。
With the rapid development of modern science and technology, the complexity of the control system is getting higher and higher, and the scale is also increasing. Thus it has been unable for industrial controlled plants to be described by a precise mathematical model. It is difficult for conventional control approaches to meet human's requirements on the automatic control systems. It is a key problem how to model unknown nonlinear delay system quickly and accurately, and implement the efficient and accurate predictive control. As an emerging optimal approach in the swarm intelligence field, particle swarm optimization algorithm has shown the excellent performance in model parameter optimization. Therefore, the focus of this paper is the particle swarm optimization algorithm. And then it is combined with fuzzy C-means clustering intelligent methods which are employed to achieve large-scale dynamic neural network offline or online parameter optimization. After that, we construct unknown nonlinear delay system identification approaches and predictive control frameworks. The research topics include the following three aspects:
(1) The online model parameter optimization based on Gaussian particle swarm optimization is proposed. Particle swarm optimization algorithm has the advantages of fast calculation and strong scalability, whose structure is simple and independent of continuously differentiable constraints of objective functions. On the basis of Gaussian functions and chaotic mappings, a novel Gaussian particle swarm method is presented, which could adjust particle swarm searching adaptively. It could maintain the global search capability and the late continued local search capability. Hence, the "prematurity" problem is solved. Further, the robust control theory is adopted to analyze the stability conditions of the Gaussian particle swarm optimization. Through various types of standard function test, the proposed Gaussian particle swarm optimization (GPSO) method significantly improves the accuracy. On this basis, the GPSO approach is used for echo state neural network online parameter optimization problem. The network parameters are adjusted online based on the input data, so the drawbacks of offline training can be overcome. Nonlinear delay system and chaotic time series data are employed to verify the effectiveness of the proposed method.
(2) The large-scale optimization based on fuzzy C-means cluster and particle swarm optimization is proposed. To solve the problem of "curse of dimensionality" faced by traditional optimal methods, the particle swarm optimization algorithm and fuzzy C-means clustering method are combined to complete multi-group cooperative particle swarm optimization algorithm. First of all, considering that the fuzzy C-means clustering algorithm is sensitive to the initial points, a two-stage fuzzy clustering algorithm with a linear assignment strategy is presented for the initial point selection, which enhances the stability of the whole algorithm. Then with the part of some prior samples, a novel single point approximation and the weighted semi-supervised fuzzy C-means algorithm is proposed. The validity of the proposed approach is verified by using the machine learning, image segmentation standard database and the actual wetland remote sensing data to verify the validity of the method. On this basis, combined with trust region methods, the modified fuzzy C-means algorithm is adopted to construct multi-group structure, and to decompose the large-scale parameter optimization problem into sub-problems. Each group collaborative optimizes and shares the information. The large-scale standard test functions are used to verify the optimization capability of the proposed method.
(3) A dynamic feedforward neural network predictive control based on particle swarm optimization is proposed. In order to improve the predictive accuracy, a new dynamic feedforward neural network structure is presented. The added dynamic delay operators not only enhance the ability of the dynamic expression, but also identify the pure delay time in the system. In addition, GPSO is adopted for the above parameter optimization of the mentioned dynamic feedforward neural network. It could speed up the network convergence. The stability of the combined model is also analyzed deeply. Furthermore, when the neural network consists of higher number of neurons, the cooperative particle swarm optimization is employed to the large-scale neural network parameter optimization. The proposed neural network model as an identifier and predictor is used in Smith predictive control with dual controllers and multivariable constrained model predictive control structure, respectively. The simulation examples demonstrate that the proposed control method can perform effectively on the unknown nonlinear delay system identification and predictive control.
(1)提出基于高斯微粒群算法的模型参数在线优化方法。微粒群算法结构简单不受目标函数连续可导等限制,计算速度快可扩展性强。本文基于高斯函数和混沌映射提出一种高斯微粒群算法,该方法能够自适应调整微粒搜索范围,不仅可以保持算法前期的全局搜索能力,又可以保证算法后期的持续局部搜索能力,解决“早熟”问题。进一步,利用鲁棒控制理论对高斯微粒群算法稳定性条件进行分析。通过各类型标准函数测试对比,所提高斯微粒群方法在函数优化精度方面得到显著的提高。在此基础上,将高斯微粒群算法用于回声状态神经网络在线参数优化问题中,随着输入数据在线调整网络参数,克服离线训练神经网络无法随环境变化及时调整的弊端,通过非线性延迟系统和混沌时间序列等数据验证所提方法的有效性。
(2)提出基于模糊C均值聚类的微粒群大规模参数优化方法。针对已有经典方法在大规模参数优化中存在“维数灾难”等问题,本文将微粒群算法与模糊C均值聚类相结合,提出动态多种群协同微粒群优化方法。首先,针对模糊C均值聚类算法对初始点敏感的问题,利用线性分配的思想进行初始点选择,增强算法的稳定性,提出两阶段模糊聚类算法。然后提出利用部分标记样本进行单点逼近和加权的半监督模糊C均值算法,通过机器学习、图像分割标准数据库和实际湿地遥感数据验证所提方法的有效性。在此基础上,采用改进模糊C均值算法自适应构造多种群算法结构,并结合信赖域方法调整搜索空间,将大规模参数优化问题分解为多个子问题进行协同寻优和信息共享。最终利用大规模标准测试函数分析优化结果,验证所提方法具有较好的优化能力。
(3)提出基于微粒群优化的动态前馈神经网络预测控制方法。为了提高神经网络的预测精度,本文提出一种动态前馈神经网络结构,通过引入动态延迟算子增强神经网络的动态表达能力,而且可以辨识出延迟系统中包含的延迟时间。此外,将高斯微粒群方法用于所提动态前馈神经网络参数优化,加快网络收敛速度,进而深入分析两者结合之后模型的稳定性。另外针对较复杂被控对象时,采用多种群协同大规模微粒群算法优化动态前馈神经网络模型参数,并将其作为辨识器和预估器分别应用于所提出的Smith双控制器预测控制框架和多变量有约束模型预测控制结构之中。仿真实例验证所提控制方法可以对模型未知非线性延迟系统进行有效地辨识和预测控制。
With the rapid development of modern science and technology, the complexity of the control system is getting higher and higher, and the scale is also increasing. Thus it has been unable for industrial controlled plants to be described by a precise mathematical model. It is difficult for conventional control approaches to meet human's requirements on the automatic control systems. It is a key problem how to model unknown nonlinear delay system quickly and accurately, and implement the efficient and accurate predictive control. As an emerging optimal approach in the swarm intelligence field, particle swarm optimization algorithm has shown the excellent performance in model parameter optimization. Therefore, the focus of this paper is the particle swarm optimization algorithm. And then it is combined with fuzzy C-means clustering intelligent methods which are employed to achieve large-scale dynamic neural network offline or online parameter optimization. After that, we construct unknown nonlinear delay system identification approaches and predictive control frameworks. The research topics include the following three aspects:
(1) The online model parameter optimization based on Gaussian particle swarm optimization is proposed. Particle swarm optimization algorithm has the advantages of fast calculation and strong scalability, whose structure is simple and independent of continuously differentiable constraints of objective functions. On the basis of Gaussian functions and chaotic mappings, a novel Gaussian particle swarm method is presented, which could adjust particle swarm searching adaptively. It could maintain the global search capability and the late continued local search capability. Hence, the "prematurity" problem is solved. Further, the robust control theory is adopted to analyze the stability conditions of the Gaussian particle swarm optimization. Through various types of standard function test, the proposed Gaussian particle swarm optimization (GPSO) method significantly improves the accuracy. On this basis, the GPSO approach is used for echo state neural network online parameter optimization problem. The network parameters are adjusted online based on the input data, so the drawbacks of offline training can be overcome. Nonlinear delay system and chaotic time series data are employed to verify the effectiveness of the proposed method.
(2) The large-scale optimization based on fuzzy C-means cluster and particle swarm optimization is proposed. To solve the problem of "curse of dimensionality" faced by traditional optimal methods, the particle swarm optimization algorithm and fuzzy C-means clustering method are combined to complete multi-group cooperative particle swarm optimization algorithm. First of all, considering that the fuzzy C-means clustering algorithm is sensitive to the initial points, a two-stage fuzzy clustering algorithm with a linear assignment strategy is presented for the initial point selection, which enhances the stability of the whole algorithm. Then with the part of some prior samples, a novel single point approximation and the weighted semi-supervised fuzzy C-means algorithm is proposed. The validity of the proposed approach is verified by using the machine learning, image segmentation standard database and the actual wetland remote sensing data to verify the validity of the method. On this basis, combined with trust region methods, the modified fuzzy C-means algorithm is adopted to construct multi-group structure, and to decompose the large-scale parameter optimization problem into sub-problems. Each group collaborative optimizes and shares the information. The large-scale standard test functions are used to verify the optimization capability of the proposed method.
(3) A dynamic feedforward neural network predictive control based on particle swarm optimization is proposed. In order to improve the predictive accuracy, a new dynamic feedforward neural network structure is presented. The added dynamic delay operators not only enhance the ability of the dynamic expression, but also identify the pure delay time in the system. In addition, GPSO is adopted for the above parameter optimization of the mentioned dynamic feedforward neural network. It could speed up the network convergence. The stability of the combined model is also analyzed deeply. Furthermore, when the neural network consists of higher number of neurons, the cooperative particle swarm optimization is employed to the large-scale neural network parameter optimization. The proposed neural network model as an identifier and predictor is used in Smith predictive control with dual controllers and multivariable constrained model predictive control structure, respectively. The simulation examples demonstrate that the proposed control method can perform effectively on the unknown nonlinear delay system identification and predictive control.
引文
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