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A general purpose implementation of the tabu search metaheuristic, called Universal Tabu Search, is used to optimally design
a locally recurrent neural network architecture. The design of a neural network is a tedious and time consuming trial and
error operation that leads to structures whose optimality is not guaranteed. In this paper, the problem of choosing the number
of hidden neurons and the number of taps and delays in the FIR and IIR network synapses is formalised as an optimisation problem,
whose cost function to be minimised is the network error calculated on a validation data set. The performance of the proposed
approach has been tested on the problem of modelling the dynamics of a non-isothermal, continuously stirred tank reactor,
in two different operating conditions: when a first order exothermic reaction is occurring; and when two consecutive first
order reactions lead to a chaotic behaviour. Comparisons with alternative neural approaches are reported, showing the usefulness
of the proposed method. 相似文献
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The use of a new Recurrent Neural Network (RNN) for controlling a robot manipulator is presented in this paper. The RNN is
a modification of Elman network. In order to solve load uncertainties, a fast-load adaptive identification is also employed
in a control system. The weight parameters of the network are updated using the standard Back-Propagation (BP) learning algorithm.
The proposed control system is consisted of a NN controller, fast-load adaptation and PID-Robust controller. A general feedforward
neural network (FNN) and a Diagonal Recurrent Network (DRN) are utilised for comparison with the proposed RNN. A two-link
planar robot manipulator is used to evaluate and compare performance of the proposed NN and the control scheme. The convergence
and accuracy of the proposed control scheme is proved. 相似文献
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This paper presents a pattern discrimination method for electromyogram (EMG) signals for application in the field of prosthetic control. The method uses a novel recurrent neural network based on the hidden Markov model. This network includes recurrent connections, which enable modeling time series, such as EMG signals. Weight coefficients in the network can be learned using a well-known back-propagation through time algorithm. Pattern discrimination experiments were conducted to demonstrate the feasibility and performance of the proposed method. We were able to successfully discriminate forearm motions using the EMG signals, and achieved considerably high discrimination performance compared with other discrimination methods. 相似文献
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一类动态递归神经网络的智能控制器 总被引:2,自引:0,他引:2
提出一种改进型动态递归神经网络的自适应控制方法,研究了动态递归网络的学习算法,分析了学习算法的收敛性,并推导了保证算法收敛的有效学习率范围,在此基础上提出了模糊推理自适应学习率方法。计算机仿真实验表明,本文控制方法对于未知、非线性被控对象的控制是有效的。 相似文献
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A fuzzy‐recurrent neural network (FRNN) has been constructed by adding some feedback connections to a feedforward fuzzy neural network (FNN). The FRNN expands the modeling ability of a FNN in order to deal with temporal problems. A basic concept of the FRNN is first to use process or expert knowledge, including appropriate fuzzy logic rules and membership functions, to construct an initial structure and to then use parameter‐learning algorithms to fine‐tune the membership functions and other parameters. Its recurrent property makes it suitable for dealing with temporal problems, such as on‐line fault diagnosis. In addition, it also provides human‐understandable meaning to the normal feedforward multilayer neural network, in which the internal units are always opaque to users. In a word, the trained FRNN has good interpreting ability and one‐step‐ahead predicting ability. To demonstrate the performance of the FRNN in diagnosis, a comparison is made with a conventional feedforward network. The efficiency of the FRNN is verified by the results. 相似文献
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基于神经网络的新型复合速度控制器的设计 总被引:2,自引:1,他引:1
针对传统PID控制和神经网络直接逆动态控制各自的特点,提出了将两者相结合构造一种新型复合神经网络速度控制器的方法:基于感应电机间接磁场定向矢量控制系统.对该复合速度控制器进行仿真研究。仿真结果表明,在电机参数变化和负载扰动的情况下,该控制器的应用使感应电机间接磁场定向矢量控制系统具有很好的鲁棒性和抗扰性能: 相似文献
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对一种递归神经网络算法的修正 总被引:1,自引:0,他引:1
本文指出了Chao-chee Ku等人提出的对角递归神经网络算法中存在的不足,并给出了修正算法,数学分析及仿真结果表明,本文所做的修正是合理的。 相似文献
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基于动态递归网络的PID自适应控制器的设计与应用 总被引:1,自引:1,他引:0
本文分析了改进的ELMAN网络的结构,并讨论了神经网络的学习算法,针对BP算法的缺陷,提出了用遗传算法修正网络权值的学习算法。本文不仅将采用遗传算法进行训练的改进ELMAN网络应用于汽车磷化加热系统的建模,而且针对该系统的特点提出了一种带预测模型的神经网络PID自适应控制器,并最后将该控制器应用于磷化温度控制,取得了良好的控制效果。 相似文献
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回归神经网络中样本特征记忆的反馈控制方法研究 总被引:1,自引:0,他引:1
分析了具有遗忘特性及信息锁存能力的状态回归神经网络的计算方法。针对多输入多输出时序样本,提出了更能反映网络短时记忆能力以及时序样本数据物理特性的同时刻反馈控制和计算方法。实验结果显示,该文提出的方法对时序样本的学习和记忆不但具有更高的准确性,而且不增加计算的复杂性。 相似文献
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Fuzzy and Recurrent Neural Network Motion Control among Dynamic Obstacles for Robot Manipulators 总被引:1,自引:0,他引:1
An integration of fuzzy controller and modified Elman neural networks (NN) approximation-based computed-torque controller is proposed for motion control of autonomous manipulators in dynamic and partially known environments containing moving obstacles. The fuzzy controller is based on artificial potential fields using analytic harmonic functions, a navigation technique common used in robot control. The NN controller can deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics of the robot arm. The NN weights are tuned on-line, with no off-line learning phase required. The stability of the closed-loop system is guaranteed by the Lyapunov theory. The purpose of the controller, which is designed as a neuro-fuzzy controller, is to generate the commands for the servo-systems of the robot so it may choose its way to its goal autonomously, while reacting in real-time to unexpected events. The proposed scheme has been successfully tested. The controller also demonstrates remarkable performance in adaptation to changes in manipulator dynamics. Sensor-based motion control is an essential feature for dealing with model uncertainties and unexpected obstacles in real-time world systems. 相似文献
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自适应B样条模糊神经网络控制器的设计 总被引:2,自引:0,他引:2
B样条具有最小局部支撑和易于实现的优点。文章利用多变量B样条网络在运算表达式上与模糊神经网络结构之间的对等关系,并通过对其权值的训练,设计出自适应B样条模糊神经网络控制器。应用于具有严重非线性摩擦力影响的速度跟踪系统的仿真实验表明,所设计的控制器完全等价于模糊神经网络控制器,同时在计算量和实现上具有明显的优势。 相似文献
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介绍一种基于RBF的模糊神经网络设计与仿真分析的实现方法。该方法利用MATLAB中的神经网络工具箱图形用户界面GUI结合模糊控制规则表给定的输入/输出样本数据设计、构建RBF模糊神经控制器,并在Simulink中建立系统仿真模型。通过对阶跃输入信号作用下系统动态性能的仿真分析,结果表明基于RBF的模糊神经控制器有良好的控制性能。 相似文献
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众所周知,线谱对(LSP-LinearSpectrumPair)系数是一种线性预测系数,它表征的是语音谱包络。在时域中它的谱插值性能良好,但是它的插值间隔一般都限制在20~30毫秒之间。为了解决这个问题,本文介绍一种使用递归神经神经网络(RNN-RecurrentNeuralNetworks)来对线谱对系数进行插值的算法。实验结果表明,使用递归神经网络可以使插值的间隔增加到100毫秒而不明显降低合成语音的质量。 相似文献
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