Yong Fang

A recurrent neural-network-based real-time learning control strategy applying to nonlinear systems with unknown dynamics

In this paper, the authors present a real-time learning control scheme for unknown nonlinear dynamical systems using recurrent neural networks (RNNs). Two RNNs, based on the same network architecture, are utilized in the learning control system. One is used to approximate the nonlinear system, and the other is used to mimic the desired system response output. The learning rule is achieved by combining the two RNNs to form the neural network control system. A generalized real-time iterative learning algorithm is developed and used to train the RNNs. The algorithm is derived by means of two-dimensional (2-D) system theory that is different from the conventional algorithms that employ the steepest optimization to minimize a cost function. This paper shows that an RNN using the real-time iterative learning algorithm can approximate any trajectory tracking to a very high degree of accuracy. The proposed learning control scheme is applied to numerical problems, and simulation results are included. The results are very promising, and this paper suggests that the 2-D system theory-based RNN learning algorithm provides a new dimension in real-time neural control systems.

Orthogonal wavelet neural networks applying to identification of Wiener model

In this paper, an orthogonal wavelet-based neural network (OWNN) is proposed. In the proposed OWNN both the orthogonal scaling functions and the corresponding mother wavelets are combined as the nonlinear activation function. The OWNN is applied to identify a Wiener-type cascade dynamical model. A linear autoregressive moving average (ARMA) model is used as the dynamic subsystems and the OWNN is employed as the nonlinear static subsystem. A Wiener model identification algorithm is formed by combining the proposed OWNN with the conventional least squares method.

Technical Communique: Iterative Learning Control of Linear Discrete-Time Multivariable Systems

The authors present an iterative learning control rule for linear discrete-time multivariable systems. The control rule assures a zero output error for the whole desired trajectory after only one learning iteration. The robustness of the control rule is studied and two numerical examples are used to validate the new control rule.

Power Minimization for OFDM Transmission with Subcarrier-Pair Based Opportunistic DF Relaying

This paper develops a sum-power minimized resource allocation (RA) algorithm subject to a sum-rate constraint for cooperative orthogonal frequency division modulation (OFDM) transmission with subcarrier-pair based opportunistic decode-and-forward (DF) relaying. The improved DF protocol first proposed in is used with optimized subcarrier pairing. Instrumental to the RA algorithm design is appropriate definition of variables to represent source/relay power allocation, subcarrier pairing and transmission-mode selection elegantly, so that after continuous relaxation, the dual method and the Hungarian algorithm can be used to find an (at least approximately) optimum RA with polynomial complexity. Moreover, the bisection method is used to speed up the search of the optimum Lagrange multiplier for the dual method. Numerical results are shown to illustrate the power-reduction benefit of the improved DF protocol with optimized subcarrier pairing.

Blind equalization of a noisy channel by linear neural network

In this paper, a new neural approach is introduced for the problem of blind equalization in digital communications. Necessary and sufficient conditions for blind equalization are proposed, which can be implemented by a two-layer linear neural network. In the hidden layer, the received signals are whitened, while the network outputs provide directly an estimation of the source symbols. We consider a stochastic approximate learning algorithm for each layer according to the property of the correlation matrices of the transmitted symbols. The proposed class of networks yield good results in simulation examples for the blind equalization of a three-ray multipath channel.

A real-time learning control approach for nonlinear continuous-time system using recurrent neural networks

In this paper, a real-time iterative learning control (ILC) approach for a nonlinear continuous-time system using recurrent neural networks (RNNs) with time-varying weights is presented. Two RNNs are utilized in the ILC system. One is used to approximate the nonlinear system and another is used to mimic the desired system response. The ILC rule is obtained by combining the two RNNs to form a neural network control system. Also, a kind of iterative RNNs training algorithm is developed based on the two-dimensional (2-D) system theory. An RNN using the proposed 2-D training algorithm is able to approximate any trajectory to a very high degree of accuracy. Simulation results show that the proposed ILC approach is very efficient. The newly developed 2-D RNNs training algorithms provides a new dimension to the application of RNNs in a nonlinear continuous-time system.

Linear neural network based blind equalization

Abstract This letter considers the problem of blind equalization in digital communications by using linear neural network. Unlike most adaptive blind equalization methods which are based on matrix decomposition or the Hankel property of matrix, we give a stochastic approximate learning algorithm for the neural network according to the property of the correlation matrices of the transmitted symbols. The network outputs provide an estimation of the source symbols, while the weight matrix of network estimates the inverse of the channel matrix. Simulation results demonstrate the performance and validity of the proposed approach for blind equalization.

A Novel ICI Self-Cancellation Scheme for OFDM Systems

Orthogonal frequency division multiplexing (OFDM) is a promising technique for fourth-generation (4G) broadband wireless communication systems. However, it suffers from inter-carrier interference (ICI) due to its sensitivity to the frequency offset caused by Doppler frequency drift. In this paper, we investigate the self-cancellation techniques to mitigate ICI for OFDM systems and a novel ICI self-cancellation scheme is proposed to mitigate the ICI effects. The performances of ICI self-cancellation schemes are analyzed in terms of the carrier-to-interference ratio (CIR) and bit error rate (BER). Simulation results demonstrate that the proposed ICI self-cancellation scheme outperforms other existing self-cancellation methods and robustness to large frequency offset.

Synthesis of the sliding-mode neural network controller for unknown nonlinear discrete-time systems

This paper develops a sliding-mode neural network controller for a class of unknown nonlinear discrete-time systems using a recurrent neural network (RNN). The control scheme is based on a linearized expression of the nonlinear system using a linear neural network (LNN). The control law is proposed according to the discrete L yapunov theory. With a modified real-time recurrent learning algorithm, the RNN as an estimator is used to estimate the unknown part in the control law in on-line fashion. The stability of the control system is guaranteed owing to the on-line learning ability of the RNN algorithm. The proposed control scheme is applied to numerical problems and simulation results that it is very effective.

Convergence Analysis of Wireless Remote Iterative Learning Control Systems with Dropout Compensation

The wireless remote iterative learning control (ILC) system with random data dropouts is considered. The data dropout is viewed as a binary switching sequence which obeys the Bernoulli distribution. In order to eliminate the effect of data dropouts on the convergence property of output error, the signal at the same time with the lost one but in the last iteration is used to compensate the data dropout at the actuator. With the dropout compensation, the convergence property of output error is analyzed by studying the element values of system transition matrix. Finally, some simulation results are given to illustrate the validity of the proposed method.