共查询到18条相似文献,搜索用时 125 毫秒
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KM降阶算法是目前区间二型模糊集合常用的降阶算法,针对其效率低、难以用于实时辨识与控制的缺点,提出了一种简化的区间二型模糊系统辨识方法。该方法采用二型T-S模糊模型,前件参数为区间二型模糊集合,后件参数为普通T-S模糊模型形式。二型T-S模糊模型的解模糊化采用简化的降阶算法,提高了模型的辨识效率,可用于实时辨识与控制。仿真实例表明,所提算法在不降低辨识精度的情况下能够有效提高辨识效率。 相似文献
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混沌系统的快速模糊控制算法 总被引:4,自引:4,他引:0
讨论了用模糊推理方法实现混沌系统的控制问题。利用递推模糊聚类算法实时对系统的输入空间进行模糊划分,利用卡尔曼滤波算法确定参数。在此基础上,给出了模糊模型的在线辨识算法。该方法不需要被控混沌系统的解析模型,控制的目标可以为周期轨道,也可以为连续变化的目标函数,在模型参数发生摄动和存在噪声情况下控制仍然有效。针对Henon吸引子的仿真结果,表明该方法的有效性和可行性。 相似文献
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基于T-S 模型的模糊预测控制研究 总被引:13,自引:1,他引:13
提出一种基于T—S模型的模糊预测控制策略.利用模糊聚类算法高线辨识T—S模型,采用带遗忘因子的递推最小二乘法进行模型参数的选择性在线学习;对模糊模型在每一采样点进行线性化,将T—S模型表示的非线性系统转化为线性时变状态空间模型,并将约束非线性优化问题转化为线性二次规划问题,解决了非线性预测控制中如何获得非线性模型和非线性优化在线求解的难题.将预测域内的线性模型序列作为预测模型,减小了模型误差,提高了控制性能.pH中和过程的仿真验证了该方法的有效性. 相似文献
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在传统T-S模型的基础上,提出一种扩展T-S模型。该模型由一组模糊规则组成,由规则前件实现输入空间的划分,将成员函数及其函数变换引入规则后件以实现对输入子空间的非线性映射。对于该模型的建立,使用改进量子遗传算法优化规则前件,递推最小二乘法确定规则后件参数。通过对两个典型非线性系统辨识,仿真结果表明了该模型可以显著提高辨识精度,且具有很好的泛化性能。 相似文献
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提出了一种基于T-S模型的模糊预测控制策略。T-S模糊模型用来描述对象的非线性动态特性,通过当前的工况参数实时在线的修正每一时刻的阶跃响应模型参数,将模糊模型作为常规线性预测控制DMC方法的预测模型,从而把T-S模型对复杂的非线性系统的良好描述特性和预测控制的滚动优化算法相结合,来实现利用常规线性预测控制策略对非线性系统的有效控制,有效地解决了复杂工业过程的强非线性问题。pH中和过程的仿真结果表明其性能明显优于传统的PID控制器。 相似文献
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基于支持向量机回归的T-S模糊模型自组织算法及应用 总被引:2,自引:0,他引:2
结合模糊聚类算法和支持向量机回归算法提出了一种新的T-S模糊模型自组织算法. 该算法首先利用一种改进模糊聚类算法提取模糊规则和辨识前件参数,然后将T-S模糊模型后件变换为标准线性支持向量机回归模型,并利用支持向量机回归算法辨识后件参数. 仿真结果表明,相比现有的自组织算法,本文提出的T-S模糊模型自组织算法在规则数较少的情况下,仍然具有较高的辨识精度和较好的泛化能力. 最后,利用提出的T-S模糊模型自组织算法较好地建立了直拉硅单晶炉加热器和空气预热器的温度模型. 相似文献
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规则可生长与修剪的非线性系统T-S模糊模型辨识 总被引:1,自引:0,他引:1
通常离线提取 T-S 模糊模型的规则后, 规则数无法在模型使用中进行调整, 而这成为表达非线性系统复杂性的一个瓶颈. 针对这一问题, 本文引入一种神经网络的生长和修剪方法, 从实时数据中提取 T-S 模型的规则, 并定义其对应局部模型对输出的影响, 以此作为在线调整规则数的依据, 从而更准确地表达了非线性系统的复杂性和运行中的变化. 再加上基于竞争性 EKF(Extended Kalman filter) 的模型参数在线学习, T-S 模型的建模精度也得到了保证. 整个算法完全实现了 T-S 模糊模型的在线辨识, 使模型的结构和参数具有很好的自适应能力. 对 CSTR(Continuously stirred tank reactor) 系统的辨识, 表明了该算法在处理非线性系统辨识问题上的优越性能. 相似文献
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Chaos particle swarm optimization and T-S fuzzy modeling approaches to constrained predictive control 总被引:2,自引:0,他引:2
Predictive control of systems is very much related to the efficiency and cost of systems, as well as to the quality of systems outcomes. However, it is difficult to achieve optimal predictive control because most predictive controls for systems have characteristics of randomness, strong and complex constraints, large delay time, fuzziness, and nonlinearity. Conventional methods of solving constrained nonlinear optimization problems for predictive control are mainly based on quadratic programming, which is quite sensitive to initial values, easy to trap in local minimal points, and requires large computational effort. In recent years, T-S fuzzy modeling has been found to be an effective approach in performing predictive control. Intelligent optimization algorithms, such as chaos optimization algorithm (COA) and particle swarm optimization (PSO), have been shown to have faster convergence and higher iterative accuracy than those based on conventional optimization methods. In this paper, chaos particle swarm optimization (CPSO), which involves combining the strengths of COA and PSO, and T-S fuzzy modeling are proposed as approaches to perform constrained predictive control. Predictive control of temperature of continued hyperthermic celiac perfusion for medical treatment based on the proposed approaches was carried out. Simulation tests were conducted to evaluate the performance of temperature control based on T-S fuzzy modeling and CPSO. Test results indicate that the T-S fuzzy model based on CPSO outperforms models based on generalized predictive control, COA, and PSO. 相似文献
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基于T-S 模型和小世界优化算法的广义非线性预测控制 总被引:1,自引:0,他引:1
提出一种新型的基于T-S模糊模型和小世界优化算法的广义非线性预测控制策略.采用基于混沌遗传算法的T-S模糊模型描述复杂非线性系统的动态特性,构成模糊多步预报器.同时,针对现有基于二进制和十进制编码小世界优化算法运行时间长等缺点,提出一种新型的基于实数编码的小世界优化算法,函数测试和应用于非线性预测控制的滚动优化反映了其较强的寻优能力.最后,将其应用于基于实际数据的T-S模糊模型的广义非线性预测控制,满足了系统实时性和快速稳定性的要求. 相似文献
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In heating, ventilating and air-conditioning (HVAC) systems, there
exist severe nonlinearity, time-varying nature, disturbances and
uncertainties. A new predictive functional control based on
Takagi-Sugeno (T-S) fuzzy model was proposed to control HVAC
systems. The T-S fuzzy model of stabilized controlled process was
obtained using the least squares method, then on the basis of global
linear predictive model from T-S fuzzy model, the process was
controlled by the predictive functional controller. Especially the
feedback regulation part was developed to compensate uncertainties
of fuzzy predictive model. Finally simulation test results in HVAC
systems control applications showed that the proposed fuzzy model
predictive functional control improves tracking effect and
robustness. Compared with the conventional PID controller, this
control strategy has the advantages of less overshoot and shorter
setting time, etc. 相似文献
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Predictive functional control based on fuzzy T-S model for HVAC systems temperature control 总被引:1,自引:0,他引:1
In heating, ventilating and air-conditioning (HVAC) systems, there exist severe nonlinearity, time-varying nature, disturbances and uncertainties. A new predictive functional control based on Takagi-Sugeno (T-S) fuzzy model was proposed to control HVAC systems. The T-S fuzzy model of stabilized controlled process was obtained using the least squares method, then on the basis of global linear predictive model from T-S fuzzy model, the process was controlled by the predictive functional controller. Especially the feedback regulation part was developed to compensate uncertainties of fuzzy predictive model. Finally simulation test results in HVAC systems control applications showed that the proposed fuzzy model predictive functional control improves tracking effect and robustness. Compared with the conventional PID controller, this control strategy has the advantages of less overshoot and shorter setting time, etc. 相似文献
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A Takagi-Sugeno (T-S) fuzzy model is used to express non-linear dynamic systems with time-delay in this paper, and an on-line identification algorithm is presented regarding its parameters and structures. A multivariable fuzzy generalized predictive control approach is proposed based on the identified fuzzy model by means of the generalized predictive control principle. The closed-loop stability is analyzed in detail. A simulation study for the multivariable load system of a boiler-turbine unit shows that the approach is superior to convention load control systems. 相似文献