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排序方式: 共有186条查询结果,搜索用时 125 毫秒
1.
工程优化问题中神经网络与进化算法的比较   总被引:2,自引:2,他引:5       下载免费PDF全文
目前工程优化问题不仅种类繁多,而且各自采用的模型与方法迥异。从方法论的高度,将现有工程优化问题分为黑箱优化与白箱优化,然后推出各自的优化模型。对于黑箱优化问题,阐述了前向神经网络在系统逼近上的优势,以及进化算法与BP算法在求解神经网络权值上的优劣;对于白箱优化问题,阐述了进化算法与反馈神经网络的优缺点和目前流行的进化算法及其通用改进策略。通过分析,可以对目前的优化问题,以及神经网络与进化算法在其中的作用,有更加全面的认识。  相似文献
2.
Multiple models switching control based on recurrent neural networks   总被引:2,自引:2,他引:0  
This paper presents a novel approach in designing adaptive controller to improve the transient performance for a class of nonlinear discrete-time systems under different operating modes. The proposed scheme consists of generalized minimum variance (GMV) controllers and a compensating controller. GMV controllers are based on the known nominal linear multiple models, while the compensating controller is based upon a recurrent neural network. The adaptation law of network weight is derived from Lyapunov stability theory. A suitable switching control strategy is applied to choose the best controller by the performance indices at every sampling instant. Simulations are discussed in order to illustrate the merits of the proposed method.  相似文献
3.
This paper proposes an adaptive recurrent neural network control (ARNNC) system with structure adaptation algorithm for the uncertain nonlinear systems. The developed ARNNC system is composed of a neural controller and a robust controller. The neural controller which uses a self-structuring recurrent neural network (SRNN) is the principal controller, and the robust controller is designed to achieve L 2 tracking performance with desired attenuation level. The SRNN approximator is used to online estimate an ideal tracking controller with the online structuring and parameter learning algorithms. The structure learning possesses the ability of both adding and pruning hidden neurons, and the parameter learning adjusts the interconnection weights of neural network to achieve favorable approximation performance. And, by the L 2 control design technique, the worst effect of approximation error on the tracking error can be attenuated to be less or equal to a specified level. Finally, the proposed ARNNC system with structure adaptation algorithm is applied to control two nonlinear dynamic systems. Simulation results prove that the proposed ARNNC system with structure adaptation algorithm can achieve favorable tracking performance even unknown the control system dynamics function.  相似文献
4.
非最小相位非线性系统的简单递归神经网络控制   总被引:1,自引:1,他引:6  
从简单递归神经网络的统一结构出发设计了简单递归神经网络控制器,在引入了控制加权的目标函数下优化神经网络权值学习,因此是通常意义的神经网络控制的推广。证明了整个系统的稳定性,并通过仿真验证了控制器的有效性。  相似文献
5.
非线性系统的回归网络辨识   总被引:1,自引:1,他引:1  
针对未知非线性系统的辨识问题,本文提出了一种新型的回归网络模型,证明了该网络模型在一定条件下能够逼近非线性系统的输入输出关系,提出了训练网络前向连接和反向连接权值的动态反向传播算法,伪真结果验证该方法的有效性。  相似文献
6.
To avoid the need to pre-process noisy data, two special denoising layers based on wavelet multiresolution analysis have been integrated into layered neural networks. A gradient-based learning algorithm has been developed that uses the same cost function to set both the neural network weights and the free parameters of the denoising layers. The denoising layers, when integrated into feedforward and recurrent neural networks, were validated on three time series prediction problems: the logistic map, a rubber hardness time series, and annual average sunspot numbers. Use of the denoising layers improved the prediction accuracy in both cases.  相似文献
7.
In this research, we work with data of futures contracts on foreign exchange rates for British pound (BP), Canadian dollar (CD), and Japanese yen (JY) that are traded at the Chicago Mercantile Exchange (CME) against US dollars. We model relationships between exchange rates in these currencies using linear models, feed forward artificial neural networks (ANN), and three versions of recurrent neural networks (RNN1, RNN2 and RNN3) for predicting exchange rates in these currencies against the US dollar. Our results on forecast evaluations based on AGS test the tests of forecast equivalence between any two competing models among the entire models employed for each of the series show that ANN and the three versions of RNN models offer superior forecasts for predicting BP, CD and JY exchange rates although the forecast evaluations based on MGN test are in sharp contrast. On the other hand forecast based on SIGN test shows that ANN and all the versions of RNN models offer superior forecasts for BP and CD in exception of JY exchange rates. The results for forecast evaluation for all the models for each of the series based on summary measures of forecast evaluations show that RNN3 model appears to offer the most accurate predictions of BP and RNN1 for JP exchange rates. However, none of the RNN models appear to be statistically superior to the benchmark (i.e., linear model) for predicting CD exchange rates.   相似文献
8.
Multiresolution-based bilinear recurrent neural network   总被引:1,自引:1,他引:0  
A multiresolution-based bilinear recurrent neural network (MBLRNN) is proposed in this paper. The proposed MBLRNN is based on the BLRNN that has robust abilities in modeling and predicting time series. The learning process is further improved by using a multiresolution-based learning algorithm for training the BLRNN so as to make it more robust for the prediction of time series data. The proposed MBLRNN is applied to the problems of network traffic prediction and electric load forecasting. Experiments and results on both practical problems show that the proposed MBLRNN outperforms both the traditional multilayer perceptron type neural network (MLPNN) and the BLRNN in the prediction accuracy.
Dong-Chul ParkEmail: Email:
  相似文献
9.
This paper considers the delay-dependent stability problem of recurrent neural networks with interval time-varying delays. An appropriate Lyapunov–Krasovskii functional is constructed and the combination method of Wirtinger inequality and reciprocally convex optimization technique is employed. Combing a new activation function segmentation method of the boundary condition and the orthogonal complement lemma, three further improved delay-dependent stability criteria are established. Finally, two numerical examples show the effectiveness of our proposed method by comparison with the recent existing works.  相似文献
10.
针对消除扩频系统中的窄带干扰问题,文章提出了一种基于扩展卡尔曼滤波(EKF)的递归神经网络预测器(RNNP)。扩展卡尔曼滤波被用于反馈修改递归神经网络的权值系数,从而准确地估计干扰信号,具有收敛速度快、预测精度高和适用于非线性处理的优点。仿真结果表明:基于EKF学习算法的RNNP相对于自适应线性最小均方差(LMS)干扰预测器、自适应近似条件均值(ACM)干扰预测器和基于实时递推学习(RTRL)算法的RNNP在预测误差的均方误差、收敛速度、信噪比改善量方面上有不同程度的改进。  相似文献
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