A genetic‐based neuro‐fuzzy controller for blind equalization of time‐varying channels

This paper presents a neuro‐fuzzy network (NFN) where all its parameters can be tuned simultaneously using genetic algorithms (GAs). The approach combines the merits of fuzzy logic theory, neural networks and GAs. The proposed NFN does not require a priori knowledge about the system and eliminates the need for complicated design steps such as manual tuning of input–output membership functions, and selection of fuzzy rule base. Although, only conventional GAs have been used, convergence results are very encouraging. A well‐known numerical example derived from literature is used to evaluate and compare the performance of the network with other equalizing approaches. Simulation results show that the proposed neuro‐fuzzy controller, all parameters of which have been tuned simultaneously using GAs, offers advantages over existing equalizers and has improved performance. From the perspective of application and implementation, this paper is very interesting as it provides a new method for performing blind equalization. The main contribution of this paper is the use of learning algorithms to train a feed‐forward neural network for M‐ary QAM and PSK signals. This paper also provides a platform for researchers of the area for further development. Copyright © 2008 John Wiley & Sons, Ltd.     

运用PLC技术改造385m~2凯利式叶滤机控制系统

根据生产实践 ,结合PLC的技术特点和 385m2 凯利式叶滤机的电气联锁特性 ,阐述了应用PLC技术 ,可进一步提高 385m2 凯利式叶滤机的运转率和自动化水平。     

An Area Optimization Method for Digital Filter Design

In this paper, we propose an efficient design method for area optimization in a digital filter. The conventional methods to reduce the number of adders in a filter have the problem of a long critical path delay caused by the deep logic depth of the filter due to adder sharing. Furthermore, there is such a disadvantage that they use the transposed direct form (TDF) filter which needs more registers than those of the direct form (DF) filter. In this paper, we present a hybrid structure of a TDF and DF based on the flattened coefficients method so that it can reduce the number of flip‐flops and full‐adders without additional critical path delay. We also propose a resource sharing method and sharing‐pattern searching algorithm to reduce the number of adders without deepening the logic depth. Simulation results show that the proposed structure can save the number of adders and registers by 22 and 26%, respectively, compared to the best one used in the past.     

Sediment Concentration and Its Prediction by Perceptron Kalman Filtering Procedure

Predictions of the discharge and the associated sediment concentration are very useful ingredients in any water resources reservoir design, planning, maintenance, and operation. Although there are many empirical relationships between the discharge and sediment concentration amounts, they need estimation of model parameters. Generally, parameter estimations are achieved through the regression method (RM), which has several restrictive assumptions. Such models are locally valid and their structures and parameter values are questionable from region to others. This paper proposes a new approach for sediment concentration prediction provided that there are measurements of discharge and sediment concentration. The basis of the methodology is a dynamic transitional model between successive time instances based on two variables, namely, discharge and sediment concentration measurements. The transition matrix elements are estimated from the measurements through a special form of the artificial neural networks as perceptrons. The sediment concentration predictions from discharge measurements are achieved through a perceptron Kalman filtering (PKF) technique. In the meantime, this technique also provides temporal predictions. A certain portion of the measurement sequence is employed for the model parameter estimations through training and the remaining part is used for the model verification. Detailed comparisons between RM and PKF approaches are presented and, finally, it is shown that the latter model works dynamically by simulating the observation scatter diagram in the best possible manner with smaller prediction errors. The application of the methodology is performed for the discharge and sediment concentration measurements obtained from the Mississippi River basin at St. Louis, Missouri. It is found that the PKF methodology has smaller average relative, root-mean-square, and absolute errors than RM. Furthermore, graphical representation, such as the scatter and frequency diagrams, indicated that the PKF approach has superiority over the RM.     

如何提高电子产品的可靠性

主要介绍了电子产品在研制、生产、试验中的可靠性工作。     

视觉诱发脑电波的时变最优滤波估计

本文提出了一个自修正的非平稳信号模型,并设计了时变最优滤波器于视觉诱发脑电波 的估计.估计出的信号与原始信号的各波峰潜伏期严格保持一致,而波峰幅值略有衰减.给 出的仿真与实体试验结果都证明了本方法的有效性.     

氩缓冲气体对铯原子共振滤光器特性的影响

铯原子共振滤光器(Cs-ARF)是目前实现蓝绿激光水下通讯的一种关键元件。本文详细研究了不同气压氩缓冲气体对Cs-ARF特性的影响,明显改善了Cs-ARF的响应速率和辐射转换效率。还研究了氮气及不同器件结构对Cs-ARF特性的影响。     

设计IIR数字滤波器的遗传优化算法

提出了ＩＩＲ数字滤波器设计的遗传优化法。这是一种模拟自然遗传和达尔文进化理论的随机并行优化算法。首先，详细描述了遗传算法并给出了计算步骤，然后将遗传算法用于ＩＩＲ数字滤波器的优化设计，最后给出了模拟计算结果。     

一种快速跟踪机动目标的并行算法

针对机动目标的跟踪问题,采用一种完全解耦的卡尔曼滤波跟踪算法,即速度滤波器和加速度滤波器并行独立运算,加速度滤波器的输出用于校正速度滤波器的结果。根据探测到的目标机动性情况,加速度滤波器可实时切换。降低了滤波维数,从而使计算量和存贮量减少,提高实时性。仿真结果表明,该算法具有很好的跟踪性能。     

Conditions for stability of the extended Kalman filter and their application to the frequency tracking problem

The error dynamics of the extended Kalman filter (EKF), employed as an observer for a general nonlinear, stochastic discrete time system, are analyzed. Sufficient conditions for the boundedness of the errors of the EKF are determined. An expression for the bound on the errors is given in terms of the size of the nonlinearities of the system and the error covariance matrices used in the design of the EKF. The results are applied to the design of a stable EKF frequency tracker for a signal with time-varying frequency.This research was supported by the Co-operative Research Centre for Robust and Adaptive Systems ((CR)² ASys). The authors wish to acknowledge the funding of the activities of (CR)² ASys by the Australian Commonwealth Government under the Co-operative Research Centre Program.