基于模糊基函数网络在磨削过程建模中的应用

本文提出了一种新的基于自适应最小二乘(ALS)算法的模糊基函数网络(FBFN),并用磨削表面粗糙度模型来测试基于ALS的FBFN的性能,结果证明它在逼进复杂非线性磨削过程中有着很好的效果。     

Intelligent Adaptive Motion Control Using Fuzzy Basis Function Networks for Electric Unicycle

This paper presents two intelligent adaptive controllers, called self‐balancing and speed controllers, for self‐balancing and motion control, respectively, of an electric unicycle using fuzzy basis function networks (FBFN), which are employed to approximate model uncertainties and unknown friction between the wheel and the terrain surface. Both controllers are established based on the linearized model of the vehicle whose model uncertainties and parameter variations are caused by different riders and terrain. An adaptive backstepping controller together with online learning FBFN and sensing information of the rider's body inclination then is presented to achieve self‐balancing motion control. By adding an electronic throttle as the input device of speed commands, a decoupling sliding‐mode controller with online learning FBFN is proposed to accomplish self‐balancing and speed control. The performance and merit of the two proposed control methods are exemplified by conducting four simulations and three experiments on a laboratory‐built electric unicycle.     

基于模糊基函数网络和自适应最小二乘算法的外圆纵向磨削表面粗糙度的预测

建立了外圆纵向磨削表面粗糙度的模糊基函数网络（FBFN）预测模型，网络的训练采用自适应最小二乘算法（ALS）。ALS将最小二乘算法和遗传算法相结合，能够自主学习，不用人为干预，FBFN和粗糙度的分析模型相结合，只需少量实验数据便可完成网络的训练，自动产生模糊规则，确定隐含层的节点数。仿真和实验结果表明，FBFN网络结构非常适合粗糙度的预测和控制，采用ALS学习方法比BP算法、传统的遗传算法和正交二乘法等能产生更好的结果。     

Robust adaptive critic control of nonlinear systems using fuzzy basis function networks: An LMI approach

This paper proposes an adaptive critic tracking control design for a class of nonlinear systems using fuzzy basis function networks (FBFNs). The key component of the adaptive critic controller is the FBFN, which implements an associative learning network (ALN) to approximate unknown nonlinear system functions, and an adaptive critic network (ACN) to generate the internal reinforcement learning signal to tune the ALN. Another important component, the reinforcement learning signal generator, requires the solution of a linear matrix inequality (LMI), which should also be satisfied to ensure stability. Furthermore, the robust control technique can easily reject the effects of the approximation errors of the FBFN and external disturbances. Unlike traditional adaptive critic controllers that learn from trial-and-error interactions, the proposed on-line tuning algorithm for ALN and ACN is derived from Lyapunov theory, thereby significantly shortening the learning time. Simulation results of a cart-pole system demonstrate the effectiveness of the proposed FBFN-based adaptive critic controller.     

卫星姿控系统基于模糊基函数网络与自回归模型的故障预测

针对卫星姿态控制系统的故障预测问题,给出了模糊基函数网络(FBFN)与自回归模型(AR)相结合的故障预测方法,并提出了预测置信因子的概念,对故障预测的准确性进行评价.首先利用卫星正常运行时的姿态数据训练FBFN,将训练好的FBFN作为卫星姿控系统的标准输出模型;然后把卫星实时姿态数据与FBFN输出数据之间的差值作为残差...     

基于FBFN的车辆跟驰防碰撞控制器设计

对车速的非线性控制提出了一种基于模糊基函数网络的车辆跟驰防碰撞控制器。由雷达传感器测得后车与前车的相对距离与相对速度,将其并输入给控制器,建立模糊隶属函数及模糊规则,基于车辆特性,控制器输出预期的加减速率。提出的FBFN车辆跟驰防碰撞控制器可以提供安全、合理且舒适的驾驶体验。     

基于混合算法的短期负荷预测模糊建模

结合最小二乘（LS）辨识以及一种基于进化规划（EP）和粒子群优化（PSO）的混合进化算法EPPSO,针对对温度比较敏感的夏季负荷,提出一种3阶段短期负荷预测（STLF）算法。在第1阶段,应用LS设计模糊基函数网络（FBFN）完成STLF模糊空间划分;第2阶段,首先拓展FBFN成一阶Sugeno模糊模型,然后应用EPPSO调节其前件参数同时训练后件参数,最后将前述模型用于STLF得出的预测误差看做一个新的时间序列,并仅用气象因素对其进行辨识,可以用回归模型表示该辨识模型,进而应用LS进行辨识。文中提出的STLF模糊建模策略主要贡献于受气象因素影响较大的夏季负荷。仿真部分对浙江省电力公司的实际负荷进行了预测,与其他方法的比较结果证明该方法具有良好的预测性能。