关于广义模糊CMAC学习收敛性的理论结果

提出了广义模糊CMAC(cerebellar model articu lation controller)神经网络,并导出了其学习的充分条件.最后,证明了广义模糊CMAC在 平方误差意义下的学习收敛性.研究结果为广义模糊CMAC的广泛应用提供了基础.     

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.     

具有微分输出的神经网络New-CMAC及其学习收敛性

基于传统的CMAC神经网络和局部加权回归技术,提出了与传统CMAC(cerebellar model articulation computer)有着同样存储空间量的改进的新CMAC网络New-CMAC,它具有传统的输出和具有其微分信息的输出,因而更适合于自动控制.接着,又提出了其新的学习算法,并研究了其学习收敛性.     

基于FPGA的2-D模糊CMAC网络的硬件实现

提出了二维模糊CMAC网络的一种基于FPGA的硬件实现方法.首先,分析了模糊CMAC网络的结构与算法,并以Matlab仿真为依据,得到模糊CMAC网络的FPGA实现所需的参数;在此基础上,对模糊CMAC网络进行硬件模块划分,基于VHDL实现了各硬件模块的功能描述,并对模块结构和权存储方式进行了优化;最后,在特定的FPGA器件上实现了模糊CMAC网络.测试结果表明:该模糊CMAC网络硬件实现具有速度快、精度高的特点,且占用较少的硬件资源,是SOPC中实现模糊CMAC网络模块的一种有效方法.     

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.     

基于变尺度编码CMAC的增强学习控制器及其应用

研究了一种基于变尺度编码CMAC神经网络的增强学习控制器设计方法,并应用于以自行车平衡为模型的非线性随机系统的学习控制中.该方法通过对Markov决策过程状态空间的变尺度重叠量化编码,实现基于CMAC的多分辨率值函数逼近,从而有效地提高了增强学习控制器对连续状态空间马氏决策问题的泛化性能.针对自行车学习控制的仿真研究表明,采用变尺度编码CMAC进行值函数逼近的增强学习控制器能够获得优于已有表格型方法和均匀编码CMAC方法的学习效率和泛化性能.     

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.     

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.     

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。     

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.     

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.     

A genetic algorithm based robust learning credit assignment cerebellar model articulation controller

In this paper, a novel approach of genetic algorithm based robust learning credit assignment cerebellar model articulation controller (GCA-CMAC) is proposed. The cerebellar model articulation controller (CMAC) is a neurological model, which has an attractive property of learning speed. However, the distributions of errors into the addressed hypercubes of CMAC are not proportional to their credibility and may cause unacceptable learning performance. The credit assignment CMAC (CA-CMAC) can solve this problem by using the creditability of hypercubes that the calculated errors are assigned proportional to the inverse of learning times. Afterward, the obtained learning times can be optimized by genetic algorithm (GA) to increase its accuracy. In this paper, the proposed algorithm is to combine credit assignment ideas and GA to provide accurate learning for CMAC. Moreover, we embed the robust learning approach into the GCA-CMAC and dynamically adjust the learning constant for training data with noise or outliers. From simulation results, it shows that the proposed algorithm outperforms other CMACs.     

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.     

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.     

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。     

一种基于CMAC的自学习控制器

现有的基于CMAC的自学习控制器能够有效地减小跟踪误差,但是在跟踪连续变 化信号如正弦波时,由于累积误差的影响会产生过学习现象,进而导致系统的不稳定.为此, 提出一种新的基于CMAC的自学习控制器,它以系统的动态误差作为CMAC的激励信号, 从而避免了累积误差的影响.仿真结果表明,该控制器不仅是有效的,而且具有很强的鲁棒 性.此外,它可以使用较高的学习速率,实时性强.