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。     

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.     

Bayesian and Dempster-Shafer fusion

The Kalman Filter is traditionally viewed as a prediction-correction filtering algorithm. In this work we show that it can be viewed as a Bayesian fusion algorithm and derive it using Bayesian arguments. We begin with an outline of Bayes theory, using it to discuss well-known quantities such as priors, likelihood and posteriors, and we provide the basic Bayesian fusion equation. We derive the Kalman Filter from this equation using a novel method to evaluate the Chapman-Kolmogorov prediction integral. We then use the theory to fuse data from multiple sensors. Vying with this approach is the Dempster-Shafer theory, which deals with measures of “belief”, and is based on the nonclassical idea of “mass” as opposed to probability. Although these two measures look very similar, there are some differences. We point them out through outlining the ideas of the Dempster-Shafer theory and presenting the basic Dempster-Shafer fusion equation. Finally we compare the two methods, and discuss the relative merits and demerits using an illustrative example.     

Accurate estimation of environment parameters from ultrasonic data

This paper describes a robust and accurate ultrasonic sensing system for a mobile robot. The system continuously updates a local map of the environment in which obstacles are represented by straight lines or points in a robot centered coordinate frame. The presented algorithms use a Kalman filter for the reduction of the noise in the ultrasonic data and use a systematical error correction (‘bundle correction’) to reduce the uncertainty in obstacle direction. Experiments are carried out in simulation and with a real mobile robot system. Results show that the accuracy with which line parameters can be estimated is in the order of 1 degree for the orientation and about 2 cm for the position. The effect of the bundle correction is significant and maximal when the robot approaches walls under a small angle.     

Multi‐layered flat‐surface micro‐optical components directly molded on an LCD panel

Abstract— We have developed a process to fabricate optical components, such as a lens, prism, or diffuser, directly on to a glass substrate. Processes include precision mastering by diamond cutting and multi‐layer photopolymer (2P) molding to realize flat surfaces and the integration of multiple components with an alignment within a few micrometers.     

基于OTA的多功能电压模滤波器及其仿真

本文采用信号流图法设计了一种基于ＯＴＡ的三输入一输出多功能电压模滤波器新电路，该电路由五个ＯＴＡ和两个接地电容构成，合理选择输入电压可实现低通、高通、带通、带阻及全通滤波功能。文章还对电路进行了ＭＯＳ管级的计算机仿真，其结果表明所提出的电路方案正确有效。     

A novel robust current control approach using adaptive line enhancer for three‐phase current‐source active power filter

An effective system control method is presented for applying a three‐phase current‐source PWM converter with a deadbeat controller to active power filters (APFs). In the shunt‐type configuration, the APF is controlled such that the current drawn by the APF from the utility is equal to the current harmonics and reactive current required for the load. To attain the time‐optimal response of the APF supply current, a two‐dimensional deadbeat control scheme is applied to APF current control. Furthermore, in order to cancel both the delay in the two‐dimensional deadbeat control scheme and the delay in DSP control strategy, an Adaptive Line Enhancer (ALE) is introduced in order to predict the desired value three sampling periods ahead. ALE has another function of bringing robustness to the deadbeat control system. Due to the ALE, settling time is made short in a transient state. On the other hand, total harmonic distortion (THD) of source currents can be minimized compared to the case where ideal identification of the controlled system can be made. The experimental results obtained from the DSP‐based APF are also reported. The compensating ability of this APF is very high in accuracy and responsiveness although the modulation frequency is rather low. © 2004 Wiley Periodicals, Inc. Electr Eng Jpn, 150(1): 50–61, 2005; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20014     

用于频率合成器的低损耗声表面波滤波器

简述了产生声表面波滤波器插入损耗的主要机理和获得低损耗的原则；介绍了根据此原则对１００～５００ＭＨｚ５种滤波器的研制过程和结果。实验表明，采用镜像阻抗连接换能器结构可达到３～４ｄＢ的低损耗，并成功地应用于频率合成器中。