Variable neural networks for adaptive control of nonlinear systems

This paper is concerned with the adaptive control of continuous-time nonlinear dynamical systems using neural networks. A novel neural network architecture, referred to as a variable neural network, is proposed and shown to be useful in approximating the unknown nonlinearities of dynamical systems. In the variable neural networks, the number of basis functions can be either increased or decreased with time, according to specified design strategies, so that the network will not overfit or underfit the data set. Based on the Gaussian radial basis function (GRBF) variable neural network, an adaptive control scheme is presented. The location of the centers and the determination of the widths of the GRBFs in the variable neural network are analyzed to make a compromise between orthogonality and smoothness. The weight-adaptive laws developed using the Lyapunov synthesis approach guarantee the stability of the overall control scheme, even in the presence of modeling error(s). The tracking errors converge to the required accuracy through the adaptive control algorithm derived by combining the variable neural network and Lyapunov synthesis techniques. The operation of an adaptive control scheme using the variable neural network is demonstrated using two simulated examples     

Identification and control of continuous-time nonlinear systems via dynamic neural networks

In this paper, we present an algorithm for the online identification and adaptive control of a class of continuous-time nonlinear systems via dynamic neural networks. The plant considered is an unknown multi-input/multi-output continuous-time higher order nonlinear system. The control scheme includes two parts: a dynamic neural network is employed to perform system identification and a controller based on the proposed dynamic neural network is developed to track a reference trajectory. Stability analysis for the identification and the tracking errors is performed by means of Lyapunov stability criterion. Finally, we illustrate the effectiveness of these methods by computer simulations of the Duffing chaotic system and one-link rigid robot manipulator. The simulation results demonstrate that the model-based dynamic neural network control scheme is appropriate for control of unknown continuous-time nonlinear systems with output disturbance noise.     

ECG analysis using nonlinear PCA neural networks for ischemiadetection

The detection of ischemic cardiac beats from a patient's electrocardiogram (EGG) signal is based on the characteristics of a specific part of the beat called the ST segment. The correct classification of the beats relies heavily on the efficient and accurate extraction of the ST segment features. An algorithm is developed for this feature extraction based on nonlinear principal component analysis (NLPCA). NLPCA is a method for nonlinear feature extraction that is usually implemented by a multilayer neural network. It has been observed to have better performance, compared with linear principal component analysis (PCA), in complex problems where the relationships between the variables are not linear. In this paper, the NLPCA techniques are used to classify each segment into one of two classes: normal and abnormal (ST+, ST-, or artifact). During the algorithm training phase, only normal patterns are used, and for classification purposes, we use only two nonlinear features for each ST segment. The distribution of these features is modeled using a radial basis function network (RBFN). Test results using the European ST-T database show that using only two nonlinear components and a training set of 1000 normal samples from each file produce a correct classification rate of approximately 80% for the normal beats and higher than 90% for the ischemic beats     

An approach to inverse nonlinear control using neural networks

In this paper, we present a strategy for controlling a class of nonlinear dynamical systems using techniques based on neural networks. The proposed strategy essentially exploits the property of neural networks in being able to approximate arbitrary nonlinear maps when suitable learning strategies are applied. For the closed-loop control, such a network is used in conjunction with a technique of inverse nonlinear control to form what we call an inverse nonlinear controller using neural networks, abbreviated as the INC/NN controller. Properties of the controller are discussed, and it is shown that the proposed INC/NN controller allows the closed-loop error dynamics to be specified directly through a set of controller gains. Extensions of the basic INC/NN controller to incorporate integral control action, to higher order systems, and to a class of nonlinear multi-input multi-output dynamical systems are also indicated. Finally, results of some real-time experiments in applying the INC/NN controller to a position control system which has inherent nonlinearities are presented.     

Experimental identification of universal motor dynamics using neural networks

In this paper a feedforward multilayer Levenberg–Marquardt (LM) neural network-training algorithm is implemented experimentally to identify the weak non-linear dynamics of a universal direct current motor. A flat-spectrum multi-frequency signal is used as the excitation signal. The effects of Gaussian white noise on the identification performance are evaluated quantitatively. The simulation and experimental results confirm that neural network identification is affected by noise, but it is capable to learn, reasonably well, the dynamic pattern of the motor in the presence of noise.     

Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks and genetic algorithms

The use of genetic algorithm (GA) to simplify the structures of artificial neural network-based modulation format identification is proposed in next-generation dynamic and heterogeneous fiber-optic networks. Simulation results show that with 80 asynchronous amplitude histogram bins, by virtue of GA, the identification error rate decreases from 4.24 to 1.04 %.     

A comparative study of neural network structures in identification of nonlinear systems

This paper investigates the identification of nonlinear systems by neural networks. As the identification methods, Feedforward Neural Networks (FNN), Radial Basis Function Neural Networks (RBFNN), Runge–Kutta Neural Networks (RKNN) and Adaptive Neuro Fuzzy Inference Systems (ANFIS) based identification mechanisms are studied and their performances are comparatively evaluated on a three degrees of freedom anthropomorphic robotic manipulator.     

Specific emitter identification for satellite communication using probabilistic neural networks

Electromagnetic signal emitted by satellite communication (satcom) transmitters are used to identify specific individual uplink satcom terminals sharing the common transponder in real environment, which is known as specific emitter identification (SEI) that allows for early indications and warning (I&W) of the targets carrying satcom furnishment and furthermore the real time electromagnetic situation awareness in military operations. In this paper, the authors are the first to propose the identification of specific transmitters of satcom by using probabilistic neural networks (PNN) to reach the goal of target recognition. We have been devoted to the examination by exploring the feasibility of utilizing the Hilbert transform to signal preprocessing, applying the discrete wavelet transform to feature extraction, and employing the PNN to perform the classification of stationary signals. There are a total of 1000 sampling time series with binary phase shift keying (BPSK) modulation originated by five types of satcom transmitters in the test. The established PNNs classifier implements the data testing and finally yields satisfactory accuracy at 8 dB(±1 dB) carrier to noise ratio, which indicates the feasibility of our method, and even the keen insight of its application in military.     

Optimal nonlinear adaptive prediction and modeling of MPEG video inATM networks using pipelined recurrent neural networks

This paper investigates the application of a pipelined recurrent neural network (PRNN) to the adaptive traffic prediction of MPEG video signal via dynamic ATM networks. The traffic signal of each picture type (I, P, and B) of MPEG video is characterized by a general nonlinear autoregressive moving average (NARMA) process. Moreover, a minimum mean-squared error predictor based on the NARMA model is developed to provide the best prediction for the video traffic signal. However, the explicit functional expression of the best mean-squared error predictor is actually unknown. To tackle this difficulty, a PRNN that consists of a number of simpler small-scale recurrent neural network (RNN) modules with less computational complexity is conducted to introduce the best nonlinear approximation capability into the minimum mean-squared error predictor model in order to accurately predict the future behavior of MPEG video traffic in a relatively short time period based on adaptive learning for each module from previous measurement data, in order to provide faster and more accurate control action to avoid the effects of excessive load situation. Since those modules of PRNN can be performed simultaneously in a pipelined parallelism fashion, this would lead to a significant improvement in the total computational efficiency of PRNN. In order to further improve the convergence performance of the adaptive algorithm for PRNN, a learning-rate annealing schedule is proposed to accelerate the adaptive learning process. Another advantage of the PRNN-based predictor is its generalization from learning that is useful for learning a dynamic environment for MPEG video traffic prediction in ATM networks where observations may be incomplete, delayed, or partially available. The PRNN-based predictor presented in this paper is shown to be promising and practically feasible in obtaining the best adaptive prediction of real-time MPEG video traffic     

Automatic identification of voice band telephony coding schemes using neural networks

A new method for identifying the type of coding present on a telecommunications channel is described. The method employs a combination of least mean squares adaptive filter techniques and neural network methods. The method is explained, and simulation results presented.<>     

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.     

Stability in contractive nonlinear neural networks

We consider models of the form mu chi = -x + p + WF(x) where x = x(t) is a vector whose entries represent the electrical activities in the units of a neural network. W is a matrix of synaptic weights, F is a nonlinear function, and p is a vector (constant or slowly varying over time) of inputs to the units. If the map WF(x) is a contraction, then the system has a unique equilibrium which is globally asymptotically stable; consequently the network acts as a stable encoder in that its steady-state response to an input is independent of the initial state of the network. We consider some relatively mild restrictions on W and F(x), involving the eigenvalues of W and the derivative of F, that are sufficient to ensure that WF(x) is a contraction. We show that in the linear case with spatially-homogeneous synaptic weight, the eigenvalues of W are simply related to the Fourier transform of the connection pattern. This relation makes it possible, given cortical activity patterns as measured by autoradiographic labeling, to construct a pattern of synaptic weights which produces steady state patterns showing similar frequency characteristics. Finally, we consider the relationships, in the spatial and frequency domains, between the equilibrium of the model and that of the linear approximation mu chi = -x + p + Wx; this latter equilibrium can be computed easily from p in the homogeneous case using discrete Fourier transforms.     

Cellular neural networks: a paradigm for nonlinear spatio-temporalprocessing

The paradigm of Cellular Neural Networks (CNNs)is going to achieve a complete maturity. In fact, from a methodological point of view, important results on their digitally programmable analog dynamics have been gained, completed with thousands of application routines. This has encouraged the spreading of a great number of applications in the most different disciplines. Moreover, their structure, tailor made for VLSI realization, has led to the production of some chip prototypes that, embedded in a computational infrastructure, have produced the first analogic cellular computers. This completes the framework and makes it possible to realize complex spatio-temporal and filtering tasks on a time scale of microseconds. In this paper some sketches on the main aspects of CNNs, from the formal to the hardware prototype point of view, are presented together with some appealing applications to illustrate complex image, visual and spatio-temporal dynamics processing     

Information theory, complexity and neural networks

Some of the main results in the mathematical evaluation of neural networks as information processing systems are discussed. The basic operation of feedback and feed-forward neural networks is described. Their memory capacity and computing power are considered. The concept of learning by example as it applies to neural networks is examined     

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.     

Digital systems for artificial neural networks

The current art of digital electronic implementation of neural networks is reviewed. Most of this work has taken place as digital simulations on general-purpose serial or parallel digital computers. Specialized neural network emulation systems have also been developed for more efficient learning and use. Dedicated digital VLSI integrated circuits offer the highest near-term future potential for this technology     

Some aspects of the theory of nonlinear networks

In the development of network theory over the years, the primary focus of attention has been in the area of linear systems. Several reasons for this emphasis can easily be cited, but perhaps the foremost reason is that it has long been thought that, except in certain very special cases, little progress toward a rigorous definitive theory could be expected once the hypothesis of linearity is discarded. The recent success in the use of numerical methods for computing solutions of the equations for specific nonlinear networks (the importance of which is not to be minimized) has, furthermore, resulted in a certain complacency on the part of many engineers who occasionally need to solve network problems. One senses their outlook as being, basically, that whenever a particular nonlinear problem arises, one need only then run, data in hand, to the computer. Somewhat ironically, however, the development of computer-aided network analysis techniques has also been a prime impetus for many of the recent theoretical investigations in the field of nonlinear networks, and although much remains to be done, a rather comprehensive body of knowledge in this area has begun to take form. A number of related recent contributions to the theory of non-linear networks are reviewed here. As distinct from the computational aspects of the network analysis problem, we discuss work whose primary purpose is to yield an understanding of the nature of the equations that describe the behavior of nonlinear networks, and to identify and relate certain properties of the network elements, and the manner of their interconnection, to properties of the equations and their solutions. In addition, we do frequently touch on the problem of computation since, as has already been implied, it is indeed one of the purposes of the work discussed here to provide more of a theoretical foundation on which to base the numerical analyses.     

Artificial neural networks for nonlinear time-domain filtering ofspeech

A multilayer perceptron (MLP) is applied as a time domain nonlinear filter to two classes of degraded speech, namely Gaussian white noise and nonlinear system degradation introduced by a low bit-rate CELP coder. The goal of the study is to examine the influence of the inherent nonlinearity within the MLP, and this is achieved by varying the levels of nonlinearity within the structure. Direct comparisons of MLPs and linear filters show that with CELP degradation the SNR improvements achieved by the MLP is measurably better than with an equivalent linear structure (3 dB cf 1.5 dB) but when the degradation is additive noise the two structures perform equally well. The study highlights the importance of scaling to achieve optimum performance, and of matching the enhancer to the degradation     

Digitally programmable nonlinear function generator for neural networks

The design of a new digitally programmable analogue circuit well suited for the implementation of several sets of nonlinear functions by approximating them by using a linear combination of sigmoidal terms is presented. The proposed circuit, allowing the building of several collections of nonlinear functions, would be useful in modelling artificial neural networks, fuzzy as well as partial differential equations based circuits