On-line emission and economic load dispatch using adaptive Hopfield neural network

This paper presents an adaptive Hopfield neural network (AHNN) based methodology, where the slope of the activation function is adjusted, for finding approximate Pareto solutions for the multi-objective optimization problem of emission and economic load dispatch (EELD). We have placed emphasis on finding solutions quickly, rather than the global Pareto solutions, so that our algorithm can be employed in large on-line power systems where variations in load are quite frequent. To enable faster convergence, adaptive learning rates have been developed by using energy function and applied to the slope adjustment method. The proposed algorithm has been tested on selected IEEE bus benchmark systems. The convergence of AHNN is found to be nearly 50% faster than the non-adaptive version.     

Proactive resource allocation for asynchronous real-time distributed systems in the presence of processor failures

We present two proactive resource allocation algorithms, RBA^*-FT and OBA-FT, for fault-tolerant asynchronous real-time distributed systems. The algorithms consider an application model where task timeliness is specified by Jensen's benefit functions and the anticipated application workload during future time intervals is described by adaptation functions. In addition, we assume that reliability functions of processors are available a priori. Given these models, our objective is to maximize aggregate task benefit and minimize aggregate missed deadline ratio in the presence of processor failures. Since determining the optimal solution is computationally intractable, the algorithms heuristically compute sub-optimal resource allocations, but in polynomial time. Experimental results reveal that RBA^*-FT and OBA-FT outperform their non-fault-tolerant counterparts in the presence of processor failures. Furthermore, RBA^*-FT performs better than OBA-FT, although OBA-FT incurs better worst-case and amortized computational costs. Finally, we observe that both algorithms robustly withstand errors in the estimation of anticipated failures.     

基于非锐化掩模的实时自适应图像增强算法

针对传统的非锐化掩模算法的局限性,依据图像中各像素点及以其为中心的若干相邻像素点的均方差值,提出了自适应图像增强算法的原理,并分析其硬件实现方法,然后给出了该算法与其它算法应用于图像增强的对比结果,最后在FPGA（field programmable gate array）实验板上进行验证.实验结果表明,此算法有效的增强图像的细节区域,防止图像边缘区域出现过冲现象,抑制图像平坦区域的噪声放大.因此,该算法取得了良好的视觉效果,硬件实现简单,适合于实时条件下图像的增强.     

利用非线性自适应技术检测微弱的瞬时信号

本文根据局部流形理论,提出了一类非线性自适应预测滤波模型。仿真结果显示,本文提出的自适应预测滤波模型具有很强的非线性近似能力,可以检测淹没在超混沌或杂波环境中的瞬时信号。     

支持下一代无线宽带应用自适应QoS模型的研究

作为真正完整的下一代网络 ( NGN,Next Generation Network)解决方案 ,NGN需要在固定通信领域和移动通信领域都能够支持综合多媒体特性。NGN中的新一代移动通信网承载在开放式、层次化结构的分组交换网络之上 ,能够为用户提供端到端的 Qo S解决方案。本文分析了下一代移动通信系统中多媒体宽带应用的 Qo S问题 ,从无线通信系统的多层次结构出发 ,建立了业务流的 Qo S指标评判体系 ,为下一代网络中的无线宽带应用提供了一种自适应Qo S控制模型     

Robustness of VS-MRAC with respect to unmodelled dynamics and external disturbances

A variable structure model reference adaptive controller (VS-MRAC) using only input and output measurements was recently proposed and shown to be globally asymptotically stable with superior transient behaviour and disturbance rejection properties. In this paper a singular perturbation approach is used in order to establish the robustness of the controller in the presence of unmodelled dynamics (parasitics) and disturbances. We develop a new Lyapunov-based technique to analyse the overall system and show that for sufficiently small parasitics the system remains stable and the output error is small in some sense.     

自适应有源噪声控制的滤波-LMAD算法

本文研究了稳定非高斯环境下的自适应有源噪声控制问题，提出了滤波－ＬＭＡＤ（ｌｅａｓｔｍｅａｎａｂｓｏｌｕｔｅｄｅｖｉａｔｉｏｎ）算法及归一化滤波－ＬＭＡＤ算法，仿真结果表明，与常用的滤波－ＬＭＳ算法和归－化滤波－ＬＭＳ算法相比，本文提出了算法收敛速度更快。     

Adaptive generation of surfaces in volume data

A widespread approach to generating polygonal approximations of iso-surfaces or contour surfaces in volume data is the socalled marching-cubes algorithm. This algorithm, however, has the disadvantage that the number of polygonal chains generated is considerable. The splitting-box algorithm presented here reduces the number of polygonal chains by adapting their size to the shape of the surface. The resulting polygonal chains offer a wide spectrum for representing the contour surface. An exact representation is achieved by a new type of generic patches calculated from the polygonal chains. Approximations of different quality may be obtained by combining the algorithm generating the patches with simple triangulations.     

Neural Network Feedback Control: Work at UTA’s Automation and Robotics Research Institute

This is an outline of research in neural networks for feedback control done since the mid 1990s at the Automation and Robotics Research Institute (ARRI) of The University of Texas at Arlington (UTA). It shows how the developments of Intelligent Control Systems based on neural networks have followed three main generations. This statement provides a short, broad-brush perspective on the development of intelligent neural feedback controllers.