基于神经网络的一类非线性系统自适应反步控制

针对传统自适应控制需要满足匹配条件、激发信号存在以及逼近误差有界等条件,提出一种新的基于神经网络的一类非线性系统自适应反步控制器设计方案.使用三层神经网络逼近系统的非线性特性,通过网络权系数自适应调整来不断的在线估计未知的逼近误差上界,采用有σ修正项的自适应律以放松持续激励条件.给出了基于Lyapunov意义上的闭环系统稳定性分析,证明跟踪误差收敛于原点的一个ε领域内.仿真结果表明了所提反步控制器的正确性.     

Adaptive robot control using neural networks

This paper studies the trajectory tracking problem to control the nonlinear dynamic model of a robot using neural networks. These controllers are based on learning from input-output measurements and not on parametric-model-based dynamics. Multilayer recurrent networks are used to estimate the dynamics of the system and the inverse dynamic model. The training is achieved using the backpropagation method. The minimization of the quadratic error is computed by a variable step gradient method. Another multilayer recurrent neural network is added to estimate the joint accelerations. The control process is applied to a two degree-of-freedom (DOF) SCARA robot using a DSP-based controller. Experimental results show the effectiveness of this approach. The tracking trajectory errors are very small and torques expected at manipulator joints are free of chattering.<>     

Adaptive control of blood pressure

Stochastic adaptive controllers have been developed for automatic control of blood pressure during infusions of cardiostimulatory or vasoactive drugs. An adaptive algorithm based upon a minimum variance control law is presented. A more advanced algorithm obtained by augmenting the performance measure to include the rate of charge of the control signal is also presented. An autoregressive-moving-average (ARMA) model, representing the dynamics of the system, and a recursive least-squares parameter estimation technique are used for both algorithms. A series of experiments was performed in dogs, utilizing an electronically activated drug infuser. Stable control was achieved, even when the circulatory state of the animal underwent major changes, using either algorithm. On the basis of theoretical considerations and experimental results, we expect that these adaptive controllers will significantly improve the performance of drug infusion systems in clinical applications.     

Adaptive decision feedback equalization for digital satellitechannels using multilayer neural networks

This paper introduces an adaptive derision feedback equalization using the multilayer perceptron structure of an M-ary PSK signal through a TDMA satellite radio channel. The transmission is disturbed not only by intersymbol interference (ISI) and additive white Gaussian noise, but also by the nonlinearity of transmitter amplifiers. The conventional decision feedback equalizer (DFE) is not well-suited to detect the transmitted sequence, whereas the neural-based DFE is able to take into account the nonlinearities and therefore to detect the signal much better. Nevertheless, the applications of the traditional multilayer neural networks have been limited to real-valued signals. To overcome this difficulty, a neural-based DFE is proposed to deal with the complex PSK signal over the complex-valued nonlinear MPSK satellite channel without performing time-consuming complex-valued back-propagation training algorithms, while maintaining almost the same computational complexity as the original real-valued training algorithm. Moreover, a modified back-propagation algorithm with better convergence properties is derived on the basis of delta-bar-delta rule. Simulation results for the equalization of QPSK satellite channels show that the neural-based DFE provides a superior bit error rate performance relative to the conventional mean square DFE, especially in poor signal-to-noise ratio conditions     

Functionality of multilayer Boolean neural networks

The authors present a method for calculating the functionality of a multilayer neural network (MLN) using logical nodes. The method is demonstrated with reference to six network topologies, and a cost/functionality ratio is proposed as an assessment metric for MLNs.<>     

Image compression using multilayer neural networks

A new neural-network data compression method is presented. The work extends the use of two-layer neural networks to multilayer networks. The results show the performance superiority of multilayer neural networks compared with that of the two-layer one, especially at high compression ratios. To overcome the long training time required for multilayered networks, a previously developed training algorithm has been used. A modified feedback error is proposed to reduce further the training time and to enhance the image quality. Also, a redistribution of the grey levels in the training phase is proposed to make the minimisation of the mean-square error more related to the human-vision system     

基于神经网络的图像亮度和对比度自适应增强

提出一种高频增强与神经网络相结合的图像亮度和对比度自适应增强方法。利用均值滤波获取原始图像的低频分量,由原始图像与低频分量的差值获取图像的高频分量。同时引入神经网络方法,建立图像的灰度均值、标准偏差与亮度和对比度两个增强系数的非线性映射关系,根据图像本身的均值与标准偏差自动获取增强系数,从而实现图像的亮度和对比度的自适应增强。该方法计算量小,实时性强,对亮度和对比度都较低的图像增强效果较好,可用于图像动态检测系统。为了验证算法的可行性,将所提出的方法应用到货车故障动态图像检测系统(TFDS)所采集的动态图像处理中,获得了好的效果。     

Visual control of robotic manipulator based on neural networks

A control scheme for a robotic manipulator system that uses visual information to position and orient the end-effector is described. The control system directly integrates visual data into the servoing process without subdividing the process into determination of the position and orientation of the workplace and inverse kinematic calculation. The feature of the control scheme is the use of neural networks for the determination of the change in joint angles required in order to achieve the desired position and orientation. The proposed system is able to control the robot so that it can approach the desired position and orientation from arbitrary initial ones. Simulations for a robotic manipulator with six degrees of freedom are described. The validity and the effectiveness of the proposed control scheme are verified by computer simulations     

Storage capacity of multilayer Boolean neural networks

A method for determining the statistical storage capacity of a multilayer Boolean neural network is presented. Theoretical values are compared with those obtained by computer simulation of a number of small networks.<>     

基于BP神经网络和SNR的自适应音频水印算法

提出一种基于BP神经网络和信噪比(SNR)的自适应音频水印算法。通过修改音频分抽样后子音频帧小波变换近似分量均值的方法嵌入水印。水印嵌入强度由期望的SNR值确定,可避免反复实验或者由经验数据确定水印强度。水印的提取以子音频抽样帧小波变换近似分量均值为BP神经网络的输入,以神经网络的输出作为提取的水印。经过模板训练的BP神经网络能有效的恢复嵌入到音频中的水印数据,实现了水印的盲检测。实验结果表明,本文提出的水印算法具有较好的鲁棒性和不可听性。     

一种基于神经网络的超稳定自适应控制算法

提出了一种用于控制复杂非线性系统的超稳定自适应控制算法。使用波波夫超稳定性原理设计控制器。用神经网络在线辨识系统的建模误差及不确定性因素，辨识结果作为补偿信号以实现系统的鲁棒控制。对一双输入双输出非线性系统的仿真结果表明，所提出的超稳定自适应控制算法具有较好的性能。     

Adaptive feedback linearization control of antilock braking systems using neural networks

Antilock braking systems are designed to control the wheel slip, such that the braking force is maximized and steerability is maintained during braking. However, the control of antilock braking systems is a challenging problem due to nonlinear braking dynamics and the uncertain and time-varying nature of the parameters. This paper presents an adaptive neural network-based hybrid controller for antilock braking systems. The hybrid controller is based on the well-known feedback linearization, combined with two feedforward neural networks that are proposed so as to learn the nonlinearities of the antilock braking system associated with feedback linearization controller. The adaptation law is derived based on the structure of the controller, using steepest descent gradient approach and backpropagation algorithm to adjust the networks weights. The weight adaptation is online and the stability of the proposed controller in the sense of Lyapunov is studied. Simulations are conducted to show the effectiveness of the proposed controller under various road conditions and parameter uncertainties.     

Robustness of representations in multilayer feedforward neural networks

Recent research has shown that multilayer feedforward networks with sigmoidal activation functions are universal approximators, and that this holds for more general activations as well. The mathematical underpinning for these results has been various: Kolmogorov's resolution of Hilbert's thirteenth problem; the Stone-Weierstrass theorem; approximation of Fourier and Radon integral representations; and convergence of probability measures. This paper

基于Hopfield神经网络的多用户OFDM系统自适应资源分配

提出一种用Hopfield神经网络对多用户OFDM通信系统进行子载波分配和比特自适应加载的方法,利用Hopfield神经网络的并行处理、收敛快、易收敛到最优解的特点,在系统性能一定和满足各个用户业务要求的条件下,对资源进行自适应分配,以使多用户OFDM系统的发射功率最小。将比特加载矩阵拆成三个矩阵来表示,简化了运算。仿真结果表明采用Hopfield神经网络方法可有效地解决通信中资源分配最优化问题,并且总体效果也优于采用遗传算法得到的结果。     

基于CMAC神经网络的舰载雷达转台的复合控制

舰载雷达伺服系统采用单一的PID控制算法往往难以取得较高的控制品质和控制精度，本文阐述了采用CMAC神经网络和传统PID相结合的复合控制方法，介绍了CMAC神经网络的原理及其结构模型，给出了其学习算法，并对其在某雷达转台上的实际应用进行了详细的论述。     

Shape from focus using multilayer feedforward neural networks

The conventional shape-from-focus (SFF) methods have inaccuracies because of piecewise constant approximation of the focused image surface (FIS). We propose a scheme for SFF based on representation of three-dimensional (3-D) FIS in terms of neural network weights. The neural networks are trained to learn the shape of the FIS that maximizes the focus measure.     

The use of multilayer neural networks in material synthesis

This paper demonstrates the incorporation of a multilayer neural network in semiconductor thin film deposition processes. As a first step toward neural network-based process control, we present results from neural network pattern classification and beam analysis of reflection high energy electron diffraction RHEED images of GaAs/AlGaAs crystal surfaces during molecular beam epitaxy growth. For beam analysis, we used the neural network to detect and measure the intensity of the RHEED beam spots during the growth process and, through Fourier transformation, determined the thin film deposition rate. The neural network RHEED pattern classification and intensity analysis capability allows, powerful in situ real time monitoring of epitaxial thin film deposition processes. Our results show that a three layer network with sixteen hidden neurons and three output neurons had the highest correct classification rate with a success rate of 100% during testing and training on 13 examples     

Loss estimation and control mechanism in bufferless optical packet-switched networks based on multilayer perceptron

Artificial neural networks (ANNs) are well-known estimators for the output of broad range of complex systems and functions. In this paper, a common ANN architecture called multilayer perceptron (MLP) is used as a fast optical packet loss rate (OPLR) estimator for bufferless optical packet-switched (OPS) networks. Considering average loads at the ingress switches of an OPS network, the proposed estimator estimates total OPLR as well as ingress OPLRs (the OPLR of optical packets sent from individual ingress switches). Moreover, a traffic policing algorithm called OPLRC is proposed to control ingress OPLRs in bufferless slotted OPS networks with asymmetric loads. OPLRC is a centralized greedy algorithm which uses estimated ingress OPLRs of a trained MLP to tag some optical packets at the ingress switches as eligible for drop at the core switches in case of contention. This will control ingress OPLRs of un-tagged optical packets within the specified limits while giving some chance for tagged optical packets to reach their destinations. Eventually, the accuracy of the proposed estimator along with the performance of the proposed algorithm is evaluated by extensive simulations. In terms of the algorithm, the results show that OPLRC is capable of controlling ingress OPLRs of un-tagged optical packets with an acceptable accuracy.     

认知中继网络中基于频谱感知的自适应功率控制策略

针对采用联合overlay和underlay频谱共享模式的认知中继网络,基于频谱感知结果和干扰信道条件,提出了自适应功率控制策略,从理论上分析了次用户的中断概率。通过Monte-Carlo仿真,与单一overlay或underlay频谱共享模式和固定功率控制方案下次用户的中断性能进行了比较。仿真结果表明,采用自适应功率控制方案,在相同中断概率条件下,次用户对主用户的干扰概率降低了70%~80%;在相同干扰概率条件下,次用户的中断概率降低了1~2个数量级,频谱共享性能得到了有效提高。     

Adaptive load distribution approach based on congestion control scheme in ad-hoc networks

The ‘load distribution’ proposition in mobile ad-hoc networks (MANETs) is accomplishing great stimulation. This is because of the phenomenal facets it possesses including advanced network resilience, reliability and performance. Though there are other leading network layer routing protocols, but they radically utilise single-path communication paradigm, which is why they fail in achieving efficient load distribution in a network. Via this paper, we propose an efficient cross-layer adaptive load distribution approach to capitalise network’s channel utilisation and to rapidly adapt to dynamic wireless channel characteristic changes. The proposed method modifies the load balanced congestion adaptive routing (LBCAR) protocol and is developed using dynamic load distribution technique, by pioneering (i) novel parameters, which report for the availability of route pertaining to minimum traffic load and better link lifetime and also adapt according to varying available network resources; (ii) an absolute dynamic method to lessen the redundant route oscillations which further reduces the routing instabilities. The simulation results demonstrate the usefulness of the proposed method and yields better results in comparison to LBCAR and standard instead of dynamic ource outing, it is dynamic source routing (DSR) protocol.