共查询到19条相似文献,搜索用时 125 毫秒
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神经网络是模拟人脑结构,它具有大规模并行及分布式信息处理能力,但是不能处理和描述模糊信息.模糊系统具有推理过程容易理解,但它很难实现自适应学习的功能.如果结合神经网络与模糊系统,可以取长补短.基于此,本文提出了一种新型动态模糊神经网络(Dynamic fuzzy neural network,D-FNN)学习算法.因为它具有结构和参数同时调整且学习速度快等优点,所以既可以在模糊逻辑系统中包含低级的神经网络学习和计算功能,也可以为神经网络提供高级的类似人的思维和推理的模糊逻辑系统.此外,本文还开发了生物医学工程应用算法程序,针对药物注射系统的直接逆控制案例进行了仿真,结果表明:D-FNN具有实时学习和控制能力强、参数估计和结构辨识同时进行等优点. 相似文献
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为了更好地辨识和控制非线性动态系统,在FNN基础上对其进行优化和改进,形成了动态模糊神经网络(DFNN)。给出了基于BP梯度算法的参数迭代学习算法,并应用于某非线性动态系统仿真试验中。仿真试验表明,该网络比单纯的FNN具有更强的辨识和控制能力,应用于非线性动态系统的控制中可以有效解决系统的非线性和不确定性,提高系统的跟踪性能,并且控制系统具有很强的鲁棒性。 相似文献
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吴晓红 《计算机与数字工程》2013,41(9)
论文提出了一种动态模糊神经网络(DFNN)控制算法用于欠驱动水面艇的航迹跟踪控制研究.动态模糊神经网络控制算法集合了模糊系统知识的方便表达和神经网络的强大学习功能.动态模糊神经网络无需任何先验知识和迭代学习,结构和参数同时调整,能充分逼近水面艇的逆动力学模型.同时,动态模糊神经网络能够在线调整权值,确保系统具有快速准确的跟踪性能,克服了由模型参数变化引起的不确定性的影响,具有一定的理论研究意义和实际应用价值.论文的研究通过在MATLAB/Simulink环境中进行仿真验证其有效性. 相似文献
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针对无人机非线性、强耦合等特点,提出了基于该自结构动态递归模糊神经网络的姿态控制系统,给出了基于Lyapunov函数的系统稳定性证明。对四层模糊神经网络进行了优化和改进,设计了自结构动态递归模糊神经网络,该网络可以根据系统状态在线更新权值、创建/删除节点、优化网络结构。仿真表明:该控制方法的突出优点是,在兼顾考虑了系统中的不确定性因素、非线性因素及外部干扰并存的情况下,保证系统的稳定性和跟踪性能;同时此网络结构比固定结构的模糊神经网络响应速度快,因此更具优越性。 相似文献
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Knowledge Incorporation into Neural Networks From Fuzzy Rules 总被引:1,自引:0,他引:1
The incorporation of prior knowledge into neural networks can improve neural network learning in several respects, for example, a faster learning speed and better generalization ability. However, neural network learning is data driven and there is no general way to exploit knowledge which is not in the form of data input-output pairs. In this paper, we propose two approaches for incorporating knowledge into neural networks from fuzzy rules. These fuzzy rules are generated based on expert knowledge or intuition. In the first approach, information from the derivative of the fuzzy system is used to regularize the neural network learning, whereas in the second approach the fuzzy rules are used as a catalyst. Simulation studies show that both approaches increase the learning speed significantly. 相似文献
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Neural networks that learn from fuzzy if-then rules 总被引:2,自引:0,他引:2
An architecture for neural networks that can handle fuzzy input vectors is proposed, and learning algorithms that utilize fuzzy if-then rules as well as numerical data in neural network learning for classification problems and for fuzzy control problems are derived. The learning algorithms can be viewed as an extension of the backpropagation algorithm to the case of fuzzy input vectors and fuzzy target outputs. Using the proposed methods, linguistic knowledge from human experts represented by fuzzy if-then rules and numerical data from measuring instruments can be integrated into a single information processing system (classification system or fuzzy control system). It is shown that the scheme works well for simple examples 相似文献
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《Advanced Robotics》2013,27(2):171-188
Several kinds of knee motion simulator systems have been developed for the accurate analysis of knee biomechanics. Knee motion simulators, however, are not recognized for their practical use because of difficulties in design and control. In this study, a wire-driven knee simulator which generates physiological knee motion has been developed. Physiological three-dimensional tibia motion against the femur can be generated by the simulator. Many clinical studies have been performed to analyze the length displacement pattern of the anterior cruciate ligament (ACL) and the posterior cruciate ligament (PCL). We assume that the physiological knee motion can be realized if the length displacement patterns of the ACL and PCL against the knee flexion angle are the same as the experimental data obtained from the literature. A fuzzy neural control policy, one of the most effective intelligent control policies, has been applied for control of the simulator. Applying the fuzzy neural control policy, human knowledge and experience can be reflected and adaptive/learning ability can be incorporated in the controller. On-line learning of the fuzzy neural controller is carried out to minimize a fuzzy controlled evaluation function using the back-propagation learning algorithm. The effectiveness of the proposed simulator has been evaluated by experiments using a model bone. 相似文献
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基于模糊RBF神经网络的函数逼近 总被引:4,自引:1,他引:3
提出了一种模糊RBF网络,将模糊逻辑的知识表达以及推理能力和RBF网络的快速学习和泛化能力结合起来,网络结构参数可按实际问题调整,仿真表明网络具有较快的学习速度和较高的函数逼近精度。 相似文献
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一种基于Rough Sets和模糊神经网络的规则获取的方法 总被引:3,自引:1,他引:2
该文提出了一种基于RoughSets思想获取初始规则,并通过模糊神经网络优化,最后再进行简化获取模糊规则,及模糊系统参数学习的方法。并通过实例进行了自动列车运行系统仿真。文中还基于上述实例,将这种基于模糊神经网络的学习与控制方法与标准的BP网络和基本的模糊系统方法进行了比较,并总结了这种方法的特点。结论表明,该文所提出的模糊规则生成和模糊系统学习方法是行之有效的。 相似文献
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An ART-based fuzzy adaptive learning control network 总被引:4,自引:0,他引:4
Cheng-Jian Lin Chin-Teng Lin 《Fuzzy Systems, IEEE Transactions on》1997,5(4):477-496
This paper addresses the structure and an associated online learning algorithm of a feedforward multilayer neural net for realizing the basic elements and functions of a fuzzy controller. The proposed fuzzy adaptive learning control network (FALCON) can be contrasted with traditional fuzzy control systems in network structure and learning ability. An online structure/parameter learning algorithm, FALCON-ART, is proposed for constructing FALCON dynamically. It combines backpropagation for parameter learning and fuzzy ART for structure learning. FALCON-ART partitions the input state space and output control space using irregular fuzzy hyperboxes according to the data distribution. In many existing fuzzy or neural fuzzy control systems, the input and output spaces are always partitioned into “grids”. As the number of variables increases, the number of partitioned grids grows combinatorially. To avoid this problem in some complex systems, FALCON-ART partitions the I/O spaces flexibly based on data distribution. It can create and train FALCON in a highly autonomous way. In its initial form, there is no membership function, fuzzy partition, and fuzzy logic rule. They are created and begin to grow as the first training pattern arrives. Thus, the users need not give it any a priori knowledge or initial information. FALCON-ART can online partition the I/O spaces, tune membership functions, find proper fuzzy logic rules, and annihilate redundant rules dynamically upon receiving online data 相似文献
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Jia-Lin Chen Jyh-Yeong Chang 《Fuzzy Systems, IEEE Transactions on》2000,8(6):730-745
This paper presents a novel learning algorithm of fuzzy perceptron neural networks (FPNNs) for classifiers that utilize expert knowledge represented by fuzzy IF-THEN rules as well as numerical data as inputs. The conventional linear perceptron network is extended to a second-order one, which is much more flexible for defining a discriminant function. In order to handle fuzzy numbers in neural networks, level sets of fuzzy input vectors are incorporated into perceptron neural learning. At different levels of the input fuzzy numbers, updating the weight vector depends on the minimum of the output of the fuzzy perceptron neural network and the corresponding nonfuzzy target output that indicates the correct class of the fuzzy input vector. This minimum is computed efficiently by employing the modified vertex method. Moreover, the fuzzy pocket algorithm is introduced into our fuzzy perceptron learning scheme to solve the nonseparable problems. Simulation results demonstrate the effectiveness of the proposed FPNN model 相似文献
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This paper presents a new method for learning a fuzzy logic controller automatically. A reinforcement learning technique is applied to a multilayer neural network model of a fuzzy logic controller. The proposed self-learning fuzzy logic control that uses the genetic algorithm through reinforcement learning architecture, called a genetic reinforcement fuzzy logic controller, can also learn fuzzy logic control rules even when only weak information such as a binary target of “success” or “failure” signal is available. In this paper, the adaptive heuristic critic algorithm of Barto et al. (1987) is extended to include a priori control knowledge of human operators. It is shown that the system can solve more concretely a fairly difficult control learning problem. Also demonstrated is the feasibility of the method when applied to a cart-pole balancing problem via digital simulations 相似文献