A fuzzy modeling method for complex systems is studied. The notation of general stochastic neural network (GSNN) is presented and a new modeling method is given based on the combination of the modified Takagi and Suge...A fuzzy modeling method for complex systems is studied. The notation of general stochastic neural network (GSNN) is presented and a new modeling method is given based on the combination of the modified Takagi and Sugeno's (MTS) fuzzy model and one-order GSNN. Using expectation-maximization(EM) algorithm, parameter estimation and model selection procedures are given. It avoids the shortcomings brought by other methods such as BP algorithm, when the number of parameters is large, BP algorithm is still difficult to apply directly without fine tuning and subjective tinkering. Finally, the simulated example demonstrates the effectiveness.展开更多
为了提升锂离子电池组均衡系统的性能,提出了一种基于模糊自适应模型预测控制(fuzzy adaptive model predictive control,FAMPC)的模块化均衡系统。首先,由改进的buck-boost电路和反激变压器组成双层均衡拓扑结构;其次,以不同电池剩余容...为了提升锂离子电池组均衡系统的性能,提出了一种基于模糊自适应模型预测控制(fuzzy adaptive model predictive control,FAMPC)的模块化均衡系统。首先,由改进的buck-boost电路和反激变压器组成双层均衡拓扑结构;其次,以不同电池剩余容量(state of charge,SOC)的状态作为模糊逻辑算法的输入,对均衡电流的约束条件进行调节;再次,基于FAMPC均衡控制方法,直接利用开关管的占空比作为系统输入;最后,在改变电池组状态并不使用额外电流控制机制的情况下进行仿真实验。结果表明,与传统的模糊控制方法相比,所提系统在正常条件下均衡速度提高了约24.51%,在电池低SOC的极端条件下均衡速度可以进一步提高至34.48%。所提系统将模糊算法提供的稳定性与模型预测控制算法的快速性相结合,保证了电池组更安全稳定的运行,可为电池组性能提升研究提供参考。展开更多
Input variables selection(IVS) is proved to be pivotal in nonlinear dynamic system modeling. In order to optimize the model of the nonlinear dynamic system, a fuzzy modeling method for determining the premise structur...Input variables selection(IVS) is proved to be pivotal in nonlinear dynamic system modeling. In order to optimize the model of the nonlinear dynamic system, a fuzzy modeling method for determining the premise structure by selecting important inputs of the system is studied. Firstly, a simplified two stage fuzzy curves method is proposed, which is employed to sort all possible inputs by their relevance with outputs, select the important input variables of the system and identify the structure.Secondly, in order to reduce the complexity of the model, the standard fuzzy c-means clustering algorithm and the recursive least squares algorithm are used to identify the premise parameters and conclusion parameters, respectively. Then, the effectiveness of IVS is verified by two well-known issues. Finally, the proposed identification method is applied to a realistic variable load pneumatic system. The simulation experiments indi cate that the IVS method in this paper has a positive influence on the approximation performance of the Takagi-Sugeno(T-S) fuzzy modeling.展开更多
In this paper, a modeling algorithm developed by transferring the adaptive fuzzy inference neural network into an on-line real time algorithm, combining the algorithm with conventional system identification method and...In this paper, a modeling algorithm developed by transferring the adaptive fuzzy inference neural network into an on-line real time algorithm, combining the algorithm with conventional system identification method and applying them to separate identification of nonlinear multi-variable systems is introduced and discussed.展开更多
文摘A fuzzy modeling method for complex systems is studied. The notation of general stochastic neural network (GSNN) is presented and a new modeling method is given based on the combination of the modified Takagi and Sugeno's (MTS) fuzzy model and one-order GSNN. Using expectation-maximization(EM) algorithm, parameter estimation and model selection procedures are given. It avoids the shortcomings brought by other methods such as BP algorithm, when the number of parameters is large, BP algorithm is still difficult to apply directly without fine tuning and subjective tinkering. Finally, the simulated example demonstrates the effectiveness.
文摘为了提升锂离子电池组均衡系统的性能,提出了一种基于模糊自适应模型预测控制(fuzzy adaptive model predictive control,FAMPC)的模块化均衡系统。首先,由改进的buck-boost电路和反激变压器组成双层均衡拓扑结构;其次,以不同电池剩余容量(state of charge,SOC)的状态作为模糊逻辑算法的输入,对均衡电流的约束条件进行调节;再次,基于FAMPC均衡控制方法,直接利用开关管的占空比作为系统输入;最后,在改变电池组状态并不使用额外电流控制机制的情况下进行仿真实验。结果表明,与传统的模糊控制方法相比,所提系统在正常条件下均衡速度提高了约24.51%,在电池低SOC的极端条件下均衡速度可以进一步提高至34.48%。所提系统将模糊算法提供的稳定性与模型预测控制算法的快速性相结合,保证了电池组更安全稳定的运行,可为电池组性能提升研究提供参考。
基金This work was supported by the Natural Science Foundation of Hebei Province(F2019203505).
文摘Input variables selection(IVS) is proved to be pivotal in nonlinear dynamic system modeling. In order to optimize the model of the nonlinear dynamic system, a fuzzy modeling method for determining the premise structure by selecting important inputs of the system is studied. Firstly, a simplified two stage fuzzy curves method is proposed, which is employed to sort all possible inputs by their relevance with outputs, select the important input variables of the system and identify the structure.Secondly, in order to reduce the complexity of the model, the standard fuzzy c-means clustering algorithm and the recursive least squares algorithm are used to identify the premise parameters and conclusion parameters, respectively. Then, the effectiveness of IVS is verified by two well-known issues. Finally, the proposed identification method is applied to a realistic variable load pneumatic system. The simulation experiments indi cate that the IVS method in this paper has a positive influence on the approximation performance of the Takagi-Sugeno(T-S) fuzzy modeling.
文摘In this paper, a modeling algorithm developed by transferring the adaptive fuzzy inference neural network into an on-line real time algorithm, combining the algorithm with conventional system identification method and applying them to separate identification of nonlinear multi-variable systems is introduced and discussed.
