IDENTIFICATION OF NONLINEAR TIME VARYING SYSTEM USING FEEDFORWARD NEURAL NETWORKS

As it is well known,it is difficult to identify a nonlinear time varying system using traditional identification approaches,especially under unknown nonlinear function.Neural networks have recently emerged as a successful tool in the area of identification and control of time invariant nonlinear systems.However,it is still difficult to apply them to complicated time varying system identification.In this paper we present a learning algorithm for identification of the nonlinear time varying system using feedforward neural networks.The main idea of this approach is that we regard the weights of the network as a state of a time varying system,then use a Kalman filter to estimate the state.Thus the network implements nonlinear and time varying mapping.We derived both the global and local learning algorithms.Simulation results demonstrate the effectiveness of this approach.

Nonlinear Time-Varying Systems Identification Using Basis Sequence Expansions Combined with Neural Networks

A new method for identifying nonlinear time varying systems with unknown structure is presented. The method extends the application area of basis sequence identification. The essential idea is to utilize the learning and nonlinear approximating ability of neural networks to model the non linearity of the system, characterize time varying dynamics of the system by the time varying parametric vector of the network, then the parametric vector of the network is approximated by a weighted sum of known basis sequences. Because of black box modeling ability of neural networks, the presented method can identify nonlinear time varying systems with unknown structure. In order to improve the real time capability of the algorithm, the neural network is trained by a simple fast learning algorithm based on local least squares presented by the authors. The effectiveness and the performance of the method are demonstrated by some simulation results.

Identification and Control of Dynamical Systems Using Modified Neural Networks

Aim To study the identification and control of nonlinear systems using neural networks. Methods A new type of neural network in which the dynamical error feedback is used to modify the inputs of the network was employed to reduce the inherent network approximation error. Results A new identification model constructed by the proposed network and stable filters was derived for continuous time nonlinear systems, and a stable adaptive control scheme based on the proposed networks was developed. Conclusion Theory and simulation results show that the modified neural network is feasible to control a class of nonlinear systems.

An Improved SPSA Algorithm for System Identification Using Fuzzy Rules for Training Neural Networks

Simultaneous perturbation stochastic approximation （SPSA） belongs to the class of gradient-free optimization methods that extract gradient information from successive objective function evaluation. This paper describes an improved SPSA algorithm, which entails fuzzy adaptive gain sequences, gradient smoothing, and a step rejection procedure to enhance convergence and stability. The proposed fuzzy adaptive simultaneous perturbation approximation （FASPA） algorithm is particularly well suited to problems involving a large number of parameters such as those encountered in nonlinear system identification using neural networks （NNs）. Accordingly, a multilayer perceptron （MLP） network with popular training algorithms was used to predicate the system response. We found that an MLP trained by FASPSA had the desired accuracy that was comparable to results obtained by traditional system identification algorithms. Simulation results for typical nonlinear systems demonstrate that the proposed NN architecture trained with FASPSA yields improved system identification as measured by reduced time of convergence and a smaller identification error.

Modelling missile motion system using neural networks

The models of noallnear systems are idendried by recmeive pedctive ermrs(RPE) methed based on thelayered neural networks. To improve the identification precision, gain callcient and arentUm factor are itheucedinto the algorithm for the data are dids by noses and vny suddnly. this lerthm is applied to the twcmedeiling of rolling and pitchng angles of ndssiles. Simulation results shoW tha the proposed algurithm is sultable forthe modelling of nodrinear systems.

Identification of Hammerstein Model Using Hybrid Neural Networks

The identification problem of Hammerstein model with extension to the multi input multi output (MIMO) case is studied. The proposed identification method uses a hybrid neural network (HNN) which consists of a multi layer feed forward neural network (MFNN) in cascade with a linear neural network (LNN). A unified back propagation (BP) algorithm is proposed to estimate the weights and the biases of the MFNN and the LNN simultaneously. Numerical examples are provided to show the efficiency of the proposed method.

Nonlinear Identification and Control of Laser Welding Based on RBF Neural Networks

A laser beam is a heat source with a high energy density;this technol-ogy has been rapidly developed and applied in thefield of welding owing to its potential advantages,and supplements traditional welding techniques.An in-depth analysis of its operating process could establish a good foundation for its application in China.It is widely understood that the welding process is a highly nonlinear and multi-variable coupling process;it comprises a significant number of complex processes with random uncertain factors.Because of their nonlinear mapping and self-learning characteristics,artificial neural networks(ANNs)have certain advantages in comparison to traditional methods in thefield of welding.Laser welding is a nonlinear dynamic process;these processes still pose a major challenge in thefield of control.Therefore,establishing a stable model is a pre-requisite for achieving accurate control.In this study,the identification and con-trol of radial basis function neural networks in laser welding processes and self-tuning PID control methods are proposed to improve weld quality.Using a MATLAB simulation,it is shown that the proposed method can obtain a good description of the level of nonlinear dynamic control,and that the algorithm iden-tification accuracy is high,practical,and effective.Using this method,the weld width quickly reaches the expected value and the system remains stable,with good robustness.Further,it ensures the stability and dynamic performance of the welding process and improves weld quality.

An Improved Differential Evolution Trained Neural Network Scheme for Nonlinear System Identification

This paper presents an improved nonlinear system identification scheme using di？erential evolution （DE）, neural network （NN） and Levenberg Marquardt algorithm （LM）. With a view to achieve better convergence of NN weights optimization during the training, the DE and LM are used in a combined framework to train the NN. We present the convergence analysis of the DE and demonstrate the efficacy of the proposed improved system identification algorithm by exploiting the combined DE and LM training of the NN and suitably implementing it together with other system identification methods, namely NN and DE＋NN on a number of examples including a practical case study. The identification results obtained through a series of simulation studies of these methods on different nonlinear systems demonstrate that the proposed DE and LM trained NN approach to nonlinear system identification can yield better identification results in terms of time of convergence and less identification error.

Nonlinear Systems Identification via an Input-Output Model Based on a Feedforward Neural Network

This paper develops a feedforward neural network based input output model for a general unknown nonlinear dynamic system identification when only the inputs and outputs are accessible observations. In the developed model, the size of the input space is directly related to the system order. By monitoring the identification error characteristic curve, we are able to determine the system order and subsequently an appropriate network structure for systems identification. Simulation results are promising and show that generic nonlinear systems can be identified, different cases of the same system can also be discriminated by our model.

Identification of Artificial Neural Network Models for Three-Dimensional Simulation of a Vibration-Acoustic Dynamic System

Industrial noise can be successfully mitigated with the combined use of passive and Active Noise Control (ANC) strategies. In a noisy area, a practical solution for noise attenuation may include both the use of baffles and ANC. When the operator is required to stay in movement in a delimited spatial area, conventional ANC is usually not able to adequately cancel the noise over the whole area. New control strategies need to be devised to achieve acceptable spatial coverage. A three-dimensional actuator model is proposed in this paper. Active Noise Control (ANC) usually requires a feedback noise measurement for the proper response of the loop controller. In some situations, especially where the real-time tridimensional positioning of a feedback transducer is unfeasible, the availability of a 3D precise noise level estimator is indispensable. In our previous works [1,2], using a vibrating signal of the primary source of noise as an input reference for spatial noise level prediction proved to be a very good choice. Another interesting aspect observed in those previous works was the need for a variable-structure linear model, which is equivalent to a sort of a nonlinear model, with unknown analytical equivalence until now. To overcome this in this paper we propose a model structure based on an Artificial Neural Network (ANN) as a nonlinear black-box model to capture the dynamic nonlinear behaveior of the investigated process. This can be used in a future closed loop noise cancelling strategy. We devise an ANN architecture and a corresponding training methodology to cope with the problem, and a MISO (Multi-Input Single-Output) model structure is used in the identification of the system dynamics. A metric is established to compare the obtained results with other works elsewhere. The results show that the obtained model is consistent and it adequately describes the main dynamics of the studied phenomenon, showing that the MISO approach using an ANN is appropriate for the simulation of the investigated process. A clear conclusion is reached highlighting the promising results obtained using this kind of modeling for ANC.

STUDY ON PREDICTION METHODS FOR DYNAMIC SYSTEMS OF NONLINEAR CHAOTIC TIME SERIES＊

The prediction methods for nonlinear dynamic systems which are decided by chaotic time series are mainly studied as well as structures of nonlinear self-related chaotic models and their dimensions. By combining neural networks and wavelet theories, the structures of wavelet transform neural networks were studied and also a wavelet neural networks learning method was given. Based on wavelet networks, a new method for parameter identification was suggested, which can be used selectively to extract different scales of frequency and time in time series in order to realize prediction of tendencies or details of original time series. Through pre-treatment and comparison of results before and after the treatment, several useful conclusions are reached: High accurate identification can be guaranteed by applying wavelet networks to identify parameters of self-related chaotic models and more valid prediction of the chaotic time series including noise can be achieved accordingly.

A multi-input and multi-output design on automotive engine management system

Lookup table is widely used in automotive industry for the design of engine control units(ECU).Together with a proportional-integral controller,a feed-forward and feedback control scheme is often adopted for automotive engine management system(EMS).Usually,an ECU has a structure of multi-input and single-output(MISO).Therefore,if there are multiple objectives proposed in EMS,there would be corresponding numbers of ECUs that need to be designed.In this situation,huge efforts and time were spent on calibration.In this work,a multi-input and multi-out(MIMO) approach based on model predictive control(MPC) was presented for the automatic cruise system of automotive engine.The results show that the tracking of engine speed command and the regulation of air/fuel ratio(AFR) can be achieved simultaneously under the new scheme.The mean absolute error(MAE) for engine speed control is 0.037,and the MAE for air fuel ratio is 0.069.

Intelligent risk identification of gas drilling based on nonlinear classification network

During the transient process of gas drilling conditions,the monitoring data often has obvious nonlinear fluctuation features,which leads to large classification errors and time delays in the commonly used intelligent classification models.Combined with the structural features of data samples obtained from monitoring while drilling,this paper uses convolution algorithm to extract the correlation features of multiple monitoring while drilling parameters changing with time,and applies RBF network with nonlinear classification ability to classify the features.In the training process,the loss function component based on distance mean square error is used to effectively adjust the best clustering center in RBF.Many field applications show that,the recognition accuracy of the above nonlinear classification network model for gas production,water production and drill sticking is 97.32%,95.25%and 93.78%.Compared with the traditional convolutional neural network(CNN)model,the network structure not only improves the classification accuracy of conditions in the transition stage of conditions,but also greatly advances the time points of risk identification,especially for the three common risk identification points of gas production,water production and drill sticking,which are advanced by 56,16 and 8 s.It has won valuable time for the site to take correct risk disposal measures in time,and fully demonstrated the applicability of nonlinear classification neural network in oil and gas field exploration and development.

Generalized Nonlinear Irreducible Auto-Correlation and Its Applications in Nonlinear Prediction Models Identification

There is still an obstacle to prevent neural network from wider and more effective applications, i.e., the lack of effective theories of models identification. Based on information theory and its generalization, this paper introduces a universal method to achieve nonlinear models identification. Two key quantities, which are called nonlinear irreducible auto-correlation (NIAC) and generalized nonlinear irreducible auto-correlation (GNIAC), are defined and discussed. NIAC and GNIAC correspond with intrinstic irreducible auto-(dependency) (IAD) and generalized irreducible auto-(dependency) (GIAD) of time series respectively. By investigating the evolving trend of NIAC and GNIAC, the optimal auto-regressive order of nonlinear auto-regressive models could be determined naturally. Subsequently, an efficient algorithm computing NIAC and GNIAC is discussed. Experiments on simulating data sets and typical nonlinear prediction models indicate remarkable correlation between optimal auto-regressive order and the highest order that NIAC-GNIAC have a remarkable non-zero value, therefore demonstrate the validity of the proposal in this paper.

非线性MIMO系统线性化解耦的一种新方法(Ⅰ)——连续时间系统

给出用神经网络（ Ｎ Ｎ）α阶积分逆系统实现连续非线性 Ｍ Ｉ Ｍ Ｏ 系统线性化解耦的方法。 Ｎ Ｎα阶积分逆系统由一个静态神经网络加若干积分器构成，将其串联在原系统之前，原系统则解耦成若干个相互无关的 Ｓ Ｉ Ｓ Ｏ 伪线性积分系统。理论分析与仿真结果表明，对于精确模型未知的较一般的非线性 Ｍ Ｉ Ｍ Ｏ 系统，所给出的方法均能实现有效的线性化解耦，且结构简单，易于工程实现。

模糊CMAC神经网络用于MIMO非线性系统的反馈线性化

针对一类多输入多输出(MIMO)连续时间非线性系统,应用模糊CMAC神经网络,给出一种状态反馈控制器,用于使状态反馈可线性化的未知的非线性动态系统获得要求的跟踪性能.在很弱的假设条件下,应用李雅普诺夫稳定性理论严格地证明了闭环系统内的所有信号为一致最终有界(UUB).

非线性MIMO系统线性化解耦的一种新方法(Ⅱ)——离散时间系统

研究如何构造神经网络（ＮＮ） α阶时延逆系统，并将其用于ＭＩＭＯ 强耦合非线性离散时间系统的线性化解耦。ＮＮα阶时延逆系统由单个静态神经网络和若干时延因子组成，将其与原系统复合可形成具有最小阶时延的伪线性解耦系统。该方法对被控系统数学模型的先验知识要求很少，不但能有效地实现对原系统的线性化解耦，且结构简单。

一类不确定非线性MIMO系统的神经网络输出反馈跟踪控制

针对一类具有外部干扰的不确定仿射非线性MIMO系统提出了一种神经网络输出反馈跟踪控制方法.在仅输出可测的情况下,控制律和神经网络权值更新律中仅用到输出误差,无需设计状态观测器或加入低通滤波器使得估计误差动态满足严格正实条件.为抑制外部干扰和子系统间的交叉耦合及神经网络逼近误差,在控制律中加入鲁棒控制项.基于Lyapunov稳定性定理证明了系统的稳定性及信号的有界性.仿真例子证实了所提方法的可行性.

基于CMAC神经网络的一类MIMO非线性系统的自适应反馈线性化

在已知系统标称模型的基础上，将ＣＭＡＣ神经网络用于一类状态反馈可线性化的ＭＩＭＯ连续时间不确定性非线性系统的鲁棒自适应反馈线性化，使系统获得要求的跟踪性能。在很弱的假设条件下，应用李雅普诺夫稳定性理论证明了闭环系统内的所有信号为一致最终有界。仿真算例验证了该方法的正确性与有效性。