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提出了一种基于去模糊优化的模糊神经网络控制器及模糊神经网络的遗传学习算法.利用遗传算法优化包含控制器性能的指标来离线寻找最优的模糊神经网络控制器结构和参数,经过遗传算法训练的模糊神经网络控制器被接入模糊神经网络智能控制系统中.仿真结果表明,利用此方法实现的控制,系统的控制精度高,超调量小,鲁棒性能很强,获得了良好的控制效果. 相似文献
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描述了一种基于实数延时模糊神经网络的有记忆效应的功率放大器模型.该模糊神经系统即自适应模糊神经推理系统,采用模糊c类均值聚类方法来减少模型的规则数目和简化模型结构.在训练过程中,采用最小二乘和反向传播相结合的高效算法提取模型参数.在测试平台上用三载波WCDMA宽带信号对射频功率放大器进行测试,并借助矢量信号分析仪采样功率放大器输入和输出数据,成功地对模型进行了训练和验证.通过和实数延时神经网络模型(RVTDNN)比较,该模型的收敛速度远快于这些前馈结构的神经网络模型.比较和分析时域和频域结果表明模型有很好的性能,其归一化均方误差达-38dB. 相似文献
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提出了一种新的自组织模糊神经网络算法,该算法能够基于输入数据自动进行神经网络结构辨识和参数辨识。首先采用一种自组织聚类方法得到神经网络的结构和网络参数初值,然后采用监督学习来优化网络参数。以某污水处理厂的运行数据为对象,应用该自组织模糊神经网络建立了活性污泥污水处理系统出水水质预测模型。仿真结果表明,该模型能够对污水处理系统出水水质进行较好的预测。 相似文献
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基于模糊神经网络的传感器可信度实时获取 总被引:1,自引:0,他引:1
针对传感器在复杂环境中所测信息不完全准确的问题,提出了一种基于专家规则的零阶Sugeno模糊模型神经网络来获取传感器可信度的方法.神经网络经训练样本训练后,可以根据传感器状态和环境信息实时地得到传感器可信度.将该模型学习算法中的最小二乘识别器加以改进,并引入了遗忘因子,可以使该网络实现在线学习,不断更新网络参数.仿真结果表明该模糊神经网络可以有效地获得传感器可信度,且越小则网络在线学习能力越强. 相似文献
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基于模糊神经网络智能预测模型的设计与实现 总被引:1,自引:0,他引:1
针对智能决策支持系统中经常遇到的预测类问题,根据人工神经网络和模糊逻辑系统的各自特点,设计一种模糊神经网络模型,将模糊系统用类似于神经网络的结构表示,再用相应的学习算法训练模糊系统实现模糊推理.并对此模型进行预测验证和编程实现. 相似文献
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本文提出了一种称为近似逻辑的逻辑系统,该系统不仅具有模糊的逻辑值,其逻辑运算符也为模糊的.近似逻辑很适合描述神经网络.基于这种逻辑我们定义了一种神经网络模型.该模型可以学习和存储知识.基于这种神经网络模型,开发了一个多专家意见综合系统. 相似文献
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本文设计了基于ARM9的温湿度智能网络监控系统,在深入研究学习无线通信网络、嵌入式系统开发、模糊控制算法等知识的基础上,完成了温湿度数据采集终端、通信网络、温湿度模糊控制算法、ARM9监控中心及PC机监控软件等的设计与开发工作。 相似文献
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Temperature control with a neural fuzzy inference network 总被引:7,自引:0,他引:7
Chin-Teng Lin Chia-Feng Juang Chung-Ping Li 《IEEE transactions on systems, man and cybernetics. Part C, Applications and reviews》1999,29(3):440-451
Although multilayered backpropagation neural networks (BPNNs) have demonstrated high potential in adaptive control, their long training time usually discourages their applications in industry. Moreover, when they are trained online to adapt to plant variations, the over-tuned phenomenon usually occurs. To overcome the weakness of the BPNN, we propose a neural fuzzy inference network (NFIN) suitable for adaptive control of practical plant systems in general and for adaptive temperature control of a water bath system in particular. The NFIN is inherently a modified Takagi-Sugeno-Kang (TSK)-type fuzzy rule based model possessing a neural network's learning ability. In contrast to the general adaptive neural fuzzy networks, where the rules should be decided in advance before parameter learning is performed, there are no rules initially in the NFIN. The rules in the NFIN are created and adapted as online learning proceeds via simultaneous structure and parameter identification. The NFIN has been applied to a practical water bath temperature control system. As compared to the BPNN under the same training procedure, the simulated results show that not only can the NFIN greatly reduce the training time and avoid the over-tuned phenomenon, but the NFIN also has perfect regulation ability. The performance of the NFIN is compared to that of the traditional PID controller and fuzzy logic controller (FLC) on the water bath temperature control system. The three control schemes are compared, with respect to set point regulation, ramp-point tracking, and the influence of unknown impulse noise and large parameter variation in the temperature control system. The proposed NFIN scheme has the best control performance 相似文献
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J. W. Jung V. Q. Leu D. Q. Dang T. D. Do F. Mwasilu H. H. Choi 《International Journal of Electronics》2013,100(8):1267-1288
This paper presents a supervisory fuzzy neural network control (SFNNC) method for a three-phase inverter of uninterruptible power supplies (UPSs). The proposed voltage controller is comprised of a fuzzy neural network control (FNNC) term and a supervisory control term. The FNNC term is deliberately employed to estimate the uncertain terms, and the supervisory control term is designed based on the sliding mode technique to stabilise the system dynamic errors. To improve the learning capability, the FNNC term incorporates an online parameter training methodology, using the gradient descent method and Lyapunov stability theory. Besides, a linear load current observer that estimates the load currents is used to exclude the load current sensors. The proposed SFNN controller and the observer are robust to the filter inductance variations, and their stability analyses are described in detail. The experimental results obtained on a prototype UPS test bed with a TMS320F28335 DSP are presented to validate the feasibility of the proposed scheme. Verification results demonstrate that the proposed control strategy can achieve smaller steady-state error and lower total harmonic distortion when subjected to nonlinear or unbalanced loads compared to the conventional sliding mode control method. 相似文献
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Nonlinear System Control Using Adaptive Neural Fuzzy Networks Based on a Modified Differential Evolution 总被引:1,自引:0,他引:1
《IEEE transactions on systems, man and cybernetics. Part C, Applications and reviews》2009,39(4):459-473
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Juang C.-F. Chen J.-S. 《IEEE transactions on systems, man and cybernetics. Part C, Applications and reviews》2007,37(3):410-417
Temperature control by a Takagi-Sugeno-Kang (TSK)-type recurrent fuzzy network (TRFN) designed by modeling plant inverse is proposed in this paper. TRFN is a recurrent fuzzy network developed from a series of TSK-type fuzzy if--then rules, and is characterized by structure and parameter learning. In parameter learning, two types of learning algorithms, the Kalman filter and the gradient descent learning algorithms, are applied to consequent parameters depending on the learning situation. The TRFN has the following advantages when applied to temperature control problems: 1) high learning ability, which considerably reduces the controller training time; 2) no a priori knowledge of the plant order is required, which eases the design process; 3) good and robust control performance; 4) online learning ability, i.e., the TRFN can adapt itself to unpredictable plant changes. The TRFN-based direct inverse control configuration is applied to a real water bath temperature control plant, where various control conditions are experimented. The same experiments are also performed by proportional-integral (PI), fuzzy, and neural network controllers. From comparisons, the aforementioned advantages of a TRFN have been verified 相似文献
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采用自组织模糊神经网络充分融合模糊控制和神经网络控制的优点,应用于电力变换器的潮流控制中,通过仿真对比,实验表明它具有自组织和在线学习能力以及设计上不依赖被控制对象数学模型的特点,具有很好的追踪控制能力和鲁棒性,达到了较好控制效果. 相似文献
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研究了模糊控制与神经网络两者相结合构成的模糊神经网络控制策略,实现了模糊神经网络控制算法在PLC中的软件编程,它不依赖于被控对象精确的数学模型,将模糊神经网络控制器应用于温度控制系统中,获得了良好的控制效果。 相似文献
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In this paper, a hybrid intelligent method including fuzzy inference and neural network is presented for real-time self-reaction of a mobile robot in unknown environments. A neural network with fuzzy inference (fuzzy neural network, FNN) presented can effectively improve the learning speed of the neural network. The method can be used to control a mobile robot based on the present motion situations of the robot in real-time; these situations include the distances in different directions between the obstacles and the robot provided by ultrasonic sensors, the target orientation sensed by a simple optical range-finder and the movement direction of the robot. Simulation results showed that the above method can quickly map the fuzzy relationship between the inputs and the output of the control system of the mobile robot. 相似文献
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This paper proposes a neural fuzzy approach for connection admission control (CAC) with QoS guarantee in multimedia high-speed networks. Fuzzy logic systems have been successfully applied to deal with traffic-control-related problems and have provided a robust mathematical framework for dealing with real-world imprecision. However, there is no clear and general technique to map domain knowledge on traffic control onto the parameters of a fuzzy logic system. Neural networks have learning and adaptive capabilities that can be used to construct intelligent computational algorithms for traffic control. However, the knowledge embodied in conventional methods is difficult to incorporate into the design of neural networks. The proposed neural fuzzy connection admission control (NFCAC) scheme is an integrated method that combines the linguistic control capabilities of a fuzzy logic controller and the learning abilities of a neural network. It is an intelligent implementation so that it can provide a robust framework to mimic experts' knowledge embodied in existing traffic control techniques and can construct efficient computational algorithms for traffic control. We properly choose input variables and design the rule structure for the NFCAC controller so that it can have robust operation even under dynamic environments. Simulation results show that compared with a conventional effective-bandwidth-based CAC, a fuzzy-logic-based CAC, and a neural-net-based CAC, the proposed NFCAC can achieve superior system utilization, high learning speed, and simple design procedure, while keeping the QoS contract 相似文献
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永磁同步电机具有非线性、强耦合的特性,常规的矢量控制方法难以对其进行精确控制。此外,电机系统易受负载扰动影响,从而产生转速和电磁转矩波动。针对转速环参数固定会导致系统响应速度慢、超调量大的问题,文中提出了一种模糊径向基神经网络PID控制策略,用以替代矢量控制系统中转速环PID控制。将神经网络和模糊控制相结合,基于增量式PID控制方式,利用梯度下降优化算法动态调整转速环中的PID参数。系统模型仿真结果表明,模糊神经网络PID控制的电机系统超调量较小,相较于常规PID控制,新模型在低速和高速运行的启动时间分别缩短了66.7%和75.9%,动态响应更快,具有更好的鲁棒性和抗干扰能力。利用DSP搭建了实验平台,实验结果也证明了该控制方法的有效性。 相似文献