共查询到19条相似文献，搜索用时 281 毫秒
1.
2.
神经网络和传统线性模型结合为处理混沌时间序列提供了新的途径。将Elman神经网络和单整自回归移动平均模型结合起来,同时分析我国进出口贸易量时间序列中的线性和非线性两部分,得到更准确的预测精度。实证表明,复合模型吸收两类方法的优点,较单一模型能够更有效地预测我国进出口数据。 相似文献
3.
基于神经网络的房屋销售面积预测研究 总被引：1，自引：0，他引：1
房屋销售面积的预测直接影响房地产开发商的投资额,而多种不稳定因素又给预测带来了困难。传统的基于统计方法的预测模型比较成熟,但缺点是适用性差、实时性不强,不能反映预测过程中的不确定性与非线性,其预测值和实际值常有较大的误差。人工神经网络能很好地处理非线性问题。在预测模型中引入神经网络和传统的线性回归方法,共同处理模型中的线性及非线性因素,以达到降低误差的目的。实验结果表明,该模型能达到有效的预测结果, 相似文献
4.
5.
6.
研究Schur收敛性条件的扰动特征泛函凸组合模型的收敛性和稳定性,是实现对特征灵敏的前馈网络系统连续性和非线性控制的关键理论依据。传统分析方法采用的模糊免疫时滞环节进行完全跟踪补偿,构造李雅普诺夫泛函线性矩阵不等式,进行非线性凸组合模型构建,但模型因扰动特征泛函收敛效果不好。构建了基于Schur收敛性条件的扰动特征泛函凸组合模型,求解平均扰动特征泛函的平均互信息量,设定扰动特征连接权值下的系统函数,通过实时自适应学习算法对被控对象进行亏损特征分解,得到Schur收敛性条件,对凸组合模型的收敛性和渐进稳定性进行证明。最后进行数值算例分析,得出构建的凸组合模型收敛性和渐进稳定性较好,计算精度精确,寻优过程可靠。 相似文献
7.
《科技通报》2015,(12)
在进行无线通信数据射频调制过程中,因振荡数据的非线性特性产生谐波振荡,很难提高无线通信传输数据的调制解调能力。传统方法采用神经网络模糊控制的分布估计谐波平衡算法,非线性滚动预测控制品质上表现不佳,谐波平衡和稳定性控制效果不好。提出一种改进的基于神经网络谐波平衡的非线性通信系统的稳定性控制模型,构建非线性通信系统模型,提取通信系统中的信号和信道特征,进行信道模型设计,采用神经网络控制方法,实现控制算法改进。仿真结果表明,采用该算法能有效提高非线性通信系统的稳定性,降低误码率,克服旁瓣中的相干分量干扰,接收端的冲激响应自相关累加输出稳定性较好,克服因振荡数据的非线性特性产生谐波振荡导致的通信误差,改善通信质量。 相似文献
8.
9.
MR阻尼器的力学模型都是以电压为已知量,来求阻尼器的出力.而在结构控制中,通常是由控制算法先求出需要的控制力,由此控制力反推出相应的电压,从而控制阻尼器的输入电压来使其产生需要的力.由于MR阻尼器是一种强非线性半主动控制装置,由阻尼器的阻尼力反推其输入电压是一个复杂而困难的问题.本文利用神经网络强大的学习、非线性拟合等... 相似文献
10.
研究了在一些试验点有重复观测时自变量非随机的半线性EV模型 .分别给出了线性参数和非线性部分的估计 ,并在适当的条件下证明了它们是强相合的 . 相似文献
11.
人工神经网络在经济控制中的应用 总被引：3，自引：0，他引：3
本文运用二次函数最优控制理论建立经济系统优化控制模型,以一个宏观经济模型为例,用神经网络建立的模型和控制器对所关心的经济变量进行控制,给出了仿真结果。 相似文献
12.
为克服现有的实际短波功放模型不能很好的处理其本身固有的非线性的缺憾,本文基于实测短波功放系统的I/O数据样本,通过采用记忆多项式模型,结合RLS自适应滤波器,为短波功放构建了一种合适精确的非线性模型,并对短波宽带功放非线性特性进行了分析。实测数据建模结果表明该方法能较好地模拟短波功放的记忆效应和非线性效应。 相似文献
13.
Faezeh Farivar Mahdi Aliyari Shoorehdeli Mohammad Teshnehlab 《Journal of The Franklin Institute》2012,349(7):2243-2267
Despite active research and significant progress in the last three decades on control of human eye movements, it remains challenging issue due to its applications in prosthetic eyes and robotics. Till now, no considerable investigation of this subject is presented in the interdisciplinary sciences. The goal of this paper is to present a distinguished survey of existing literature on the intelligent control of the human eye movements system applied in a huggable pet-type robot as a biomechatronic system.In this study, the basic knowledge of human eye movements control is explained to show how the neural networks in the brainstem control the human eye movements. The geometry and model of human eye movements system are investigated and this system is considered as a nonlinear control system. The specified model may only be an academic exercise. It can have scientific importance in understanding of the human movement system in general. Also, it can be useful for robotics.Intelligent methods such as artificial neural networks and fuzzy neural networks are proposed to control the human eye movements and numerical simulations are presented. It is discussed that the intelligent controls applied to control of human eye movements system are emulated from the neural controls in biological system. 相似文献
14.
The Hammerstein–Wiener model is a nonlinear system with three blocks where a dynamic linear block is sandwiched between two static nonlinear blocks. For parameter learning of the Hammerstein–Wiener model, the synchronous parameter learning methods are proposed to learn the model parameters by constructing hybrid model of the three series block, such as over parameterization method, subspace method and maximum likelihood method. It should be pointed out that the aforementioned methods appeared the product term of model parameters in the process of parameter learning, and parameter separation method is further adopted to separate hybrid parameters, which increases the complexity of parameter learning. To address this issue, a novel three-stage parameter learning method of the neuro-fuzzy based Hammerstein–Wiener model corrupted by process noise using combined signals is developed in this paper. The combined signals are designed to completely separate the parameter learning issues of the static input nonlinear block, the linear dynamic block and the static output nonlinear block, which effectively simplifies the process of parameter learning of the Hammerstein–Wiener model. Parameter learning of the Hammerstein–Wiener model are summarized into the following three aspects: The first one is to learn the output static nonlinear block parameters using two sets of separable signals with different sizes. The second one is to estimate the linear dynamic block parameters by means of the correlation analysis method, the unmeasurable intermediate variable information problem is effectively handled. The final one is to determine the parameters of the static input nonlinear block and the moving average noise model using recursive extended least square scheme. The simulation results are presented to illustrate that the proposed learning approach yields high learning accuracy and good robustness for the Hammerstein–Wiener model corrupted by process noise. 相似文献
15.
Decentralized adaptive neural backstepping control scheme is developed for uncertain high-order stochastic nonlinear systems with unknown interconnected nonlinearity and output constraints. For the control of high-order nonlinear interconnected systems, it is assumed that nonlinear system functions are unknown. It is for the first time to control stochastic nonlinear high-order systems with output constraints. Firstly, by constructing barrier Lyapunov functions, output constraints are handled. Secondly, at each recursive step, only one adaptive parameter is updated to overcome over-parameterization problems, and RBF neural networks are used to identify unknown nonlinear functions so that the difficulties caused by completely unknown system functions and stochastic disturbances are tackled. Finally, based on the Lyapunov stability method, the decentralized adaptive control scheme via neural networks approximator is proposed, ultimately reducing the number of learning parameters. It is shown that the designed controller can guarantee all the signals of the resulting closed-loop system to be semi-globally uniformly ultimately bounded (SGUUB), and the tracking errors for each subsystem are driven to a small neighborhood of zero. The simulation studies are performed to verify the effectiveness of the proposed control strategy. 相似文献
16.
Fernando Ornelas Tellez Alexander G. Loukianov Eduardo Jose Bayro Corrochano 《Journal of The Franklin Institute》2010,347(6):1015-1034
This paper presents a discrete-time decentralized neural identification and control for large-scale uncertain nonlinear systems, which is developed using recurrent high order neural networks (RHONN); the neural network learning algorithm uses an extended Kalman filter (EKF). The discrete-time control law proposed is based on block control and sliding mode techniques. The control algorithm is first simulated, and then implemented in real time for a two degree of freedom (DOF) planar robot. 相似文献
17.
Jorge D. Rios Alma Y. Alanis Carlos Lopez-Franco Nancy Arana-Daniel 《Journal of The Franklin Institute》2018,355(1):218-249
This work presents a neural identifier-control scheme for uncertain nonlinear discrete-time systems with unknown time-delays. This scheme is based on a neural identifier to get a model of the system and a discrete-time block control technique based on sliding modes to generate the control law. The neural identifier is based on a Recurrent High Order Neural Network (RHONN) trained with an Extended Kalman Filter (EKF) based algorithm. Applicability is shown using real-time test results for linear induction motors. Also, a Lyapunov analysis is added in order to prove the semi-globally uniformly ultimately boundedness (SGUUB) of the proposed neural identifier-control scheme. 相似文献
18.
L. Piroddi 《Journal of The Franklin Institute》2012,349(3):826-844
Nonlinear control with feedforward neural networks is usually designed by means of model based control strategies, which make explicit use of (direct or inverse) models of the controlled system. In this framework, a typical control problem consists in reducing the effects of the inevitable errors introduced by neural network approximation. In a non-adaptive setting, modeling errors can be compensated by hybrid control schemes, where the approximate neural controller is complemented with an integral type regulator connected in parallel. However, in this way, the model based control paradigm is partially lost and stability properties of the control system may be degraded. In this paper a stability analysis of such hybrid schemes is performed, which shows that control system stability can be achieved provided each of the two control blocks obeys a specific condition. Furthermore, a modified hybrid scheme is proposed to enhance the cooperation between the two control blocks: a nonlinear static filter is employed to modulate the integral action so that it becomes significant only when the neural controller has approached the equilibrium. Stability analysis is extended to this case. The hybrid scheme where the two control blocks are connected hierarchically in cascade is finally discussed. 相似文献
19.
细胞信号转导网络的结构复杂,规模庞大,建立的数学模型维数高,变量多,具有高度非线性。在复杂系统分析设计中,模型简化始终是主要的研究问题之一。提出一种基于混合推理方法的模型简化策略,利用代谢控制分析、敏感性分析、主元分析和通量分析相结合,降低系统模型维数,减少生化反应个数,简化系统结构。以NF-κB信号转导网络作为研究对象,原模型由24个常微分方程和64个参数组成,简化模型则包括17个常微分方程,1个代数方程和52个参数。仿真结果表明,简化模型能够准确地预测系统的动态特性,为模型分析和参数辨识提供方便,验证了模型简化策略的有效性。 相似文献