新的基于mass-assignment的模糊CMAC神经网络及其学习收敛性

基于J.F.Baldwin等人提出的mass-assignment理论,提出了新的基于mass-assignment的模糊CMAC神经网络,接着研究了其学习规则.理论研究结果揭示出,此新模糊CMAC是一个全局逼近器,并且具有学习收敛性.故此新模糊CMAC有非常重要的应用潜力.     

模糊CMAC的硬件结构分析及其FPGA实现

提出了模糊CMAC的一种基于FPGA的硬件实现方法。与其它FPGA实现的神经网络相比，包含了可以用于在线学习的权学习算法。分析了模糊CMAC的模型结构及其相应的硬件模块；用VHDL实现基于上述模块的模糊CMAC；对该模糊CMAC进行硬件综合与测试。测试结果表明：该模糊CMAC的FPGA实现方法是可行的，硬件化后的网络具有速度快、精度高、占用器件资源少的特点，是在SOPC中实现模糊CMAC模块的一种有效方法。     

一种CMAC超闭球结构及其学习算法

提出了一种CMAC(Cerebellar Model Articulatlon Controller)输入空间超闭球量化 方法.基于超闭球上模糊基函数的信息存储与恢复策略,还给出了快速收敛的学习算法.通过 非线性动态系统建模仿真研究,结果表明CMAC具有很强的学习记忆和泛化能力.     

模糊小脑模型神经网络

提出输入层具有一定隶属度的模糊小脑模型神经网络(Fuzzy CMAC),它比小脑 模型CMAC(Cerebellar Model Articulation Controller)能更真实地描述客观世界.给出n维 Fuzzy CMAC算法,仿真结果表明Fuzzy CMAC比小脑模型CMAC具有如下优点:学习收敛 速度快得多,可以学习模糊规则.Fuzzy CMAC比CMAC优越,使CMAC成为Fuzzy CMAC 的特例.     

FUZZY CMAC NEURAL NETWORK ZHOU XUDONG

提出输入层具有一定隶属度的模糊小脑模型神经网络(Fuzzy CMAC),它比小脑模型CMAC(Cerebellar Model Articulation Controller)能更真实地描述客观世界.给出n维Fuzzy CMAC算法，仿真结果表明Fuzzy CMAC比小脑模型CMAC具有如下优点学习收敛速度快得多，可以学习模糊规则. Fuzzy CMAC比CMAC优越，使CMAC成为Fuzzy CMAC的特例.     

模糊CMAC的柔性空间机器人轨迹跟踪自学习控制

针对不确定自由漂浮柔性空间机器人系统,采用模糊CMAC神经网络自学习控制策略来解决轨迹跟踪控制问题.首先建立漂浮基空间机器人的动力学方程,然后利用具有快速学习能力的模糊CMAC神经网络来逼近非线性柔性臂的逆动力学模型.网络参数采用改进的有监督的Hebb学习规则进行自适应在线调整,并通过关联搜索进行自学习和自组织,其误差代价函数由PID控制器提供.仿真结果表明,这种模糊CMAC逆模PID控制器能够达到较高的控制精度,具有一定的工程应用价值.     

高超声速飞行器的模糊CMAC神经网络控制器设计

由于采用机体一体化设计,吸气式高超声速飞行器的气动特性难以准确获知,建立的数学模型是极不准确的;设计了一种模糊CMAC神经网络(FCMAC)控制器及其学习算法,在CMAC神经网络控制器中结合模糊逻辑理论,使得CMAC控制器具有自学习能力;仿真用高超声速飞行器的纵向模型对该控制器进行了验证,证明该控制方法能够有效地跟踪飞行器的高度和速度指令。     

一种模糊CMAC神经网络

提出了一种模糊CMAC(小脑模型关节控制器)神经网络,它由输入层、模糊化层、模糊相 联层、模糊后相联层与输出层等5层节点组成,具有与CMAC相似的单层连接权,可通过BP 算法学习推论参数或模糊规则.给出了网络的连接结构与学习算法,并将其应用于函数逼近 问题中仿真结果验证了该方法较之CMAC的优越性.     

一种多种群广义遗传CMAC的软测量模型

对CMAC的惯性系数和学习率进行了优化,提出了基于广义遗传优化的小脑模型神经网络(CMAC)算法,提高CMAC的计算速度和精度以满足复杂动态环境下的非线性实时控制的需要.结合溶出预脱硅系统工艺优化的需求,提出了基于广义遗传优化的CMAC的溶出赤泥A/S比系统软模型,用于准确实时地预测溶出赤泥A/S比.试验说明了该模型在...     

模糊CMAC及其在机器人轨迹跟踪控制中的应用

小脑模型关节控制器(CMAC)具有结构简单,学习快速的优点,但是它的空间划分方式不能在线进行调整,影响了其自适应能力的提高.本文将模糊理论引入CMAC,提出了一种能够反映人类小脑认知的模糊性和连续性的模糊小脑模型关节控制器(FCMAC).该控制器对CMAC的空间划分方式进行了模糊化处理,可通过BP学习算法对CMAC的空间划分方式进行在线调整,大大提高了CMAC的自适应能力.所提出的FCMAC被应用于机器人的轨迹跟踪控制系统以克服机器人系统中非线性和不确定性因素的影响.仿真实验结果表明,所提FCMAC与传统的CMAC相比性能上有了很大的改善.     

一种基于模糊CMAC神经网络的自学习控制器

通过分析模糊控制和基于广义基函数的ＣＭＡＣ神经网络,提出一种模糊ＣＭＡＣ（ＦＣＭＡＣ）神经网络。通过ＦＣＭＡＣ权系数的在线学习,实现修正模糊逻辑。给出一种基于ＦＣＭＡＣ的自学习控制器的结构及合适的学习算法,这种网络每次学习少量参数,算法简单。仿真结果表明所提出的控制器优于传统的ＰＩＤ控制器。     

Single-input CMAC control system

This paper attempts to propose a single-input cerebellar model articulation controller (CMAC) control system, which contains only one controller implemented by the CMAC. The single-input CMAC control system adopts two learning stages. An off-line learning stage is to enable the output behavior of the CMAC to approximate the control surface of a fuzzy PD-type controller. An on-line learning stage follows to improve the system stability by the modified learning rule. The linear interpolation scheme is also applied to the recall process at the on-line learning stage to ensure better accuracy of the CMAC output. Simulation results show that the single-input CMAC controller is superior to the fuzzy PD-type controller.     

A robust learning algorithm based on support vector regression and robust fuzzy cerebellar model articulation controller

For real-world applications, the obtained data are always subject to noise or outliers. The learning mechanism of cerebellar model articulation controller (CMAC), a neurological model, is to imitate the cerebellum of human being. CMAC has an attractive property of learning speed in which a small subset addressed by the input space determines output instantaneously. For fuzzy cerebellar model articulation controller (FCMAC), the concept of fuzzy is incorporated into CMAC to improve the accuracy problem. However, the distributions of errors into the addressed hypercubes may cause unacceptable learning performance for input data with noise or outliers. For robust fuzzy cerebellar model articulation controller (RFCMAC), the robust learning of M-estimator can be embedded into FCMAC to degrade noise or outliers. Meanwhile, support vector machine (SVR) is a machine learning theory based algorithm which has been applied successfully to a number of regression problems when noise or outliers exist. Unfortunately, the practical application of SVR is limited to defining a set of parameters for obtaining admirable performance by the user. In this paper, a robust learning algorithm based on support SVR and RFCMAC is proposed. The proposed algorithm has both the advantage of SVR, the ability to avoid corruption effects, and the advantage of RFCMAC, the ability to obtain attractive properties of learning performance and to increase accurate approximation. Additionally, particle swarm optimization (PSO) is applied to obtain the best parameters setting for SVR. From simulation results, it shows that the proposed algorithm outperforms other algorithms.     

Robust and fast learning for fuzzy cerebellar model articulation controllers.

In this paper, the online learning capability and the robust property for the learning algorithms of cerebellar model articulation controllers (CMAC) are discussed. Both the traditional CMAC and fuzzy CMAC are considered. In the study, we find a way of embeding the idea of M-estimators into the CMAC learning algorithms to provide the robust property against outliers existing in training data. An annealing schedule is also adopted for the learning constant to fulfill robust learning. In the study, we also extend our previous work of adopting the credit assignment idea into CMAC learning to provide fast learning for fuzzy CMAC. From demonstrated examples, it is clearly evident that the proposed algorithm indeed has faster and more robust learning. In our study, we then employ the proposed CMAC for an online learning control scheme used in the literature. In the implementation, we also propose to use a tuning parameter instead of a fixed constant to achieve both online learning and fine-tuning effects. The simulation results indeed show the effectiveness of the proposed approaches.     

Nonlinear Systems Identification via Two Types of Recurrent Fuzzy CMAC

Normal fuzzy CMAC neural network performs well for nonlinear systems identification because of its fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires huge memory and the dimension increases exponentially with the number of inputs. It is difficult to model dynamic systems with static fuzzy CMACs. In this paper, we use two types of recurrent techniques for fuzzy CMAC to overcome the above problems. The new CMAC neural networks are named recurrent fuzzy CMAC (RFCMAC) which add feedback connections in the inner layers (local feedback) or the output layer (global feedback). The corresponding learning algorithms have time-varying learning rates, the stabilities of the neural identifications are proven.     

Adaptive fault-tolerant control of non-linear systems: an improved CMAC-based fault learning approach

The conventional cerebellar model articulation controllers (CMAC) learning scheme equally distributes the correcting errors into all addressed hypercubes, regardless of the credibility of those hypercubes. This paper presents the adaptive fault-tolerant control scheme of non-linear systems using a fuzzy credit assignment CMAC neural network online fault learning approach. The credit assignment concept is introduced into fuzzy CMAC weight adjusting to use the learned times of addressed hypercubes as the credibility of CMAC. The correcting errors are proportional to the inversion of learned times of addressed hypercubes. With this fault learning model, the learning speed of fault can be improved. After the unknown fault is estimated, online, by using the fuzzy credit assignment CMAC, the effective control law reconfiguration strategy based on the sliding mode control technique is used to compensate for the effect of the fault. The proposed fault-tolerant controller adjusts its control signal by adding a corrective sliding mode control signal to confine the system performance within a boundary layer. The numerical simulations demonstrate the effectiveness of the proposed CMAC algorithm and fault-tolerant controller.