Compact self‐constructing recurrent fuzzy neural network with decision feedback for quadrature amplitude modulation signaling systems

This paper proposes a novel adaptive decision feedback equalizer (DFE) based on compact self‐constructing recurrent fuzzy neural network (CSRFNN) for quadrature amplitude modulation systems. Without the prior knowledge of channel characteristics, a novel training scheme containing both compact self‐constructing learning (CSL) and real‐time recurrent learning algorithms is derived for the CSRFNN. The proposed CSL algorithm adopts two evaluation criteria to intelligently decide the number of fuzzy rules that are necessary. The real‐time recurrent learning is performed simultaneously with the CSL at each time instant to adjust DFE parameters. The proposed DFE is compared with several neural network‐based DFEs on a nonlinear complex‐valued channel. The results show that the CSRFNN DFE is superior to classical neural network DFEs in terms of symbol‐error rate, convergence speed, and time cost. Copyright © 2011 John Wiley & Sons, Ltd.     

Adaptive fuzzy output‐feedback tracking control for switched stochastic pure‐feedback nonlinear systems

This paper considers the problem of adaptive fuzzy output‐feedback tracking control for a class of switched stochastic nonlinear systems in pure‐feedback form. Unknown nonlinear functions and unmeasurable states are taken into account. Fuzzy logic systems are used to approximate the unknown nonlinear functions, and a fuzzy observer is designed to estimate the immeasurable states. Based on these methods, an adaptive fuzzy output‐feedback control scheme is developed by combining the backstepping recursive design technique and the common Lyapunov function approach. It is shown that all the signals in the closed‐loop system are semiglobally uniformly ultimately bounded in mean square in the sense of probability, and the observer errors and tracking errors can be regulated to a small neighborhood of the origin by choosing appropriate parameters. Finally, a simulation result is provided to show the effectiveness of the proposed control method.     

An improved decision feedback equalization using a priori information

This paper addresses the exhaustive computational complexity of the maximum‐a‐posteriori equalizer and the inefficiency of the conventional decision feedback equalizer (DFE) algorithm in iterative equalization, especially when the higher‐level modulation is used with severely distorted Inter Symbol Interference channels. The new method proposed here improves the bit error rate (BER) performance by computing the extra metric r_n+1 using the feedback symbols from previous iteration and combining it with a priori information of the symbols. After each iteration, the hard‐detected symbols are saved in the memory as a priori data for next iteration. We verified the proposed algorithm for Binary Phase Shift Keying and 8‐phase shift keying modulation. The promising simulation results show that the BER performance given by the proposed low complexity DFE algorithm improved dramatically throughout the iterations when the conventional DFE has only insignificant improvement in the process of iterative equalization. Copyright © 2009 John Wiley & Sons, Ltd.     

Fuzzy adaptive optimal control for nonlinear switched systems with actuator hysteresis

This article considers the issue of fuzzy adaptive dynamic programming control of nonlinear switched systems with arbitrary switchings and unknown uncertain functions and actuator hysteresis nonlinearities. The whole control approach is made of switching feedforward controller and optimal switching feedback controller. To get over the hardness of arbitrary switching structure and the issue of “explosion of complexity”, the common Lyapunov function theory and dynamic surface control method are utilized in the recursive design technique. By using fuzzy logic systems to model unknown inner dynamics and unknown cost functions, a novel fuzzy adaptive optimal switching control strategy is developed. Meanwhile, uniformly ultimately boundedness of all weights in the controlled systems are proved by the proposed control method, and the tracking performance is guaranteed in an optimal manner. Subsequently, a numerical simulation study is used to test the effectiveness of the presented control strategy.     

Fuzzy financial decision-making: load management programs case study

This paper derives two fuzzy financial profitability models, namely, a least cost solution model and a cost-benefit analysis model, to evaluate the fuzzy financial profitability of load management alternatives. A straightforward vertex parameters' fuzzy mathematics operation using the function principle is derived as an alternative to the traditional extension principle and is applied to evaluate a number of different financial decision indexes. Compared to the extension principle, the function principle is simple to implement and is conceptually straightforward. The geometric moments of the fuzzy financial indexes are established in order to determine their relative ranking as part of a decision-making process. The performances of the proposed fuzzy financial models are verified by considering their application to two practical load management programs in Taiwan, namely, a cooling energy storage air conditioner and a cogeneration project. These investigations not only confirm that the results of the fuzzy financial models are consistent with those of the conventional crisp models but also demonstrate that the proposed models are more flexible, intelligent, and computationally efficient compared to the extension principle fuzzy mathematics approach. The developed models represent readily implemented possibility analysis tools for use in the arena of uncertain financial decision-making.     

模糊粗糙集理论在变压器故障诊断中的应用

提出一种改进的三比值变压器故障诊断方法。以模糊粗糙集为数学基础建立信息决策系统,采用数据挖掘技术解决这一建立过程中的若干问题。考虑到信息源的连续取值对模糊粗糙推理的影响,利用模糊集方法处理连续取值型属性。利用数据库知识发现技术挖掘数据库中隐含的聚类信息,设置属性的模糊取值并确定隶属函数。并在此基础上基于包含度对模糊规则进行约简和剔除。设计了适用于模糊粗糙规则提取的数据挖掘算法,从数据库中提取规则,按属性集建立多表决策库的拓扑结构。诊断结果表明,该决策库故障正判率较高,模糊判断规则适应现场条件。     

A 6‐Gbps/lane receiver for a clock‐forwarded link in 65‐nm CMOS process

For a 6‐Gbps/lane clock‐forwarded link, a wireline receiver has been developed. The phases of the sampling clocks are aligned to the center of the input data eye by a clock and data recovery (CDR) circuit. In the CDR circuit, the sampling clock phases are rotated by a phase rotating phase locked loop (PLL). A three‐tap decision feedback equalizer (DFE) compensates for the loss of cable together with a continuous‐time linear equalizer (CTLE) to ensure sufficient eye opening for the CDR circuit to find the optimum sampling phase. The DFE coefficients are adaptively calculated based on the data and edge samples. Implemented in a 65‐nm CMOS process, the three‐lane 6‐Gbps/lane receiver for a clock‐forwarded link occupies 0.63 mm² including pads and consumes 288 mA from a 1.2‐V supply. Copyright © 2015 John Wiley & Sons, Ltd.     

一种基于模糊途径的发电系统可靠性评估

介绍了用模糊数学计算发电系统可靠性的一种新方法。该方法用隶属函数表现发电机组故障率和修复率的不确定性和不精确性,将系统负荷数据“模糊化”,同时在机组追加算法的基础上进行模糊修正,通过负荷的模糊聚类简化计算,提高了计算效率。该算法以概率分布特征表述可靠性指标,符合概率统计特征。     

基于模糊控制的改进频移式孤岛检测方法

针对孤岛检测中传统正反馈主动频移法的初始截断系数和反馈系数只能取固定值,导致的检测盲区大、检测时间长以及总谐波失真过高等问题,同时通过对主动频移法中施加的扰动量与公共耦合点电压频率关系的分析,提出一种改进的带正反馈主动频移式孤岛检测方法。利用模糊控制算法对反馈系数进行模糊化处理,并对反馈量做出基于三次幂函数的变换,建立起新的截断系数。与传统正反馈主动频移法相比,改进后的方法减小了检测盲区,缩短了检测时间,并且降低了总的谐波畸变率,提高了电能质量。最后利用MATLAB/Simulink平台对该方法进行了仿真验证。     

热工过程模糊决策预测控制的应用研究

预测控制是一种适于大滞后热工对象的控制方法，但传统的受限预测控制由于控制目标单一、优化过程复杂等原因未能很好应用。该文在模糊集合理论的基础上，提出了一类新型的基于模糊决策方法的受限预测控制方法。与传统受限预测控制相比，这种新型的模糊预测控制方法采用了性能全面、确切的模糊指标函数来替代传统预测控制中的线性二次型指标函数，并将控制系统中的多种约束条件自然融合到模糊指标函数中，从而有效地简化了受限预测控制的整个优化过程。在此基础上，论文进一步证明了传统的受限预测控制算法在功能上只是该文预测控制方法的一个特例，因此，该文有效地扩展了传统受限预测控制方法的功能。论文最后通过仿真计算证实了所提新型预测控制方法对非线性和大滞后的热工过程具有优良的控制性能。     

改进的模糊层次分析法在配电网规划中的应用

针对配电网规划综合决策中普遍存在的信息不完整和一致性难以满足的问题,改进了基于模糊区间层次分析法的配电网规划辅助决策方法,提出了计及残缺判断的模糊区间层次分析法(FAHP)。在提出的配电网规划整体框架的基础上,结合某个实例介绍了采用该方法进行方案确定的具体实现步骤:先通过FAHP得到主观权值,再对该值进行修正形成可变的综合权重,最后给出方案排序,并设计了相应的计算机递归算法,提高了排序的计算效率。实例计算表明,该方法更符合配电网规划的柔性要求,是一种实用和可行的配电网规划综合决策方法。     

火电厂多代理控制系统中反馈控制代理

提出了火电厂多代理控制系统中反馈控制代理(FBCAs),能够在环境中灵活自主地行动以达到设计目标。FBCAs采用基于遗传算法的模糊控制,仿真结果表明,基于遗传算法的模糊控制是FBCAs进行决策的有效工具。     

A small‐gain approach for adaptive output‐feedback NN control of switched pure‐feedback nonlinear systems

This paper investigates the problem of adaptive output‐feedback neural network (NN) control for a class of switched pure‐feedback uncertain nonlinear systems. A switched observer is first constructed to estimate the unmeasurable states. Next, with the help of an NN to approximate the unknown nonlinear terms, a switched small‐gain technique‐based adaptive output‐feedback NN control scheme is developed by exploiting the backstepping recursive design scheme, input‐to‐state stability analysis, the common Lyapunov function method, and the average dwell time (ADT) method. In the recursive design, the difficulty of constructing an overall Lyapunov function for the switched closed‐loop system is dealt with by decomposing the switched closed‐loop system into two interconnected switched systems and constructing two Lyapunov functions for two interconnected switched systems, respectively. The proposed controllers for individual subsystems guarantee that all signals in the closed‐loop system are semiglobally, uniformly, and ultimately bounded under a class of switching signals with ADT, and finally, two examples illustrate the effectiveness of theoretical results, which include a switched RLC circuit system.     

Fuzzy adaptive control of a class of nonlinear systems with unmodeled dynamics

This paper investigates the tracking control problem for a class of pure‐feedback systems with unmodeled dynamics. The useful properties of the fuzzy basis functions and membership are explored to be used for stability analysis, and an alternative Lyapunov function depending on both control input and system state is utilized. Then, an adaptive fuzzy controller is designed to ensure that the tracking error is within a small adjustable neighborhood of the origin, where some conventional assumptions imposed on the unmodeled dynamics have been relaxed. Finally, simulation results are given to validate the theoretical results.     

Fuzzy approximation-based adaptive control of nonstrict feedback stochastic nonlinear systems with time-varying state constraints

This article develops an approximation-based fuzzy control scheme for nonstrict feedback stochastic nonlinear systems (NFSNS) with time-varying state constraints. The difficulty in constructing controller is how to conquer the algebraic loop problem caused by nonstrict feedback structure, as well as prevent the state constraints from violating. To dispose the time-varying state constraints, time-varying barrier Lyapunov function is incorporated into the backstepping design framework. The lumped uncertainties of NFSNS are approximated by the fuzzy logic systems. By virtue of fuzzy basis function, the algebraic loop problem is effectively handled. Theoretical analysis shows that the predefined state constraints are not violated and all signals of the closed-loop systems are bounded. Finally, simulation results substantiate the validity of the devised method.     

具有非线性输入的鲁棒自适应模糊滑模控制

针对一类具有死区非线性输入的SISO非线性系统,基于滑模控制原理,提出了一种稳定自适应模糊控制器设计方案.该方案通过使用积分型Lyapunov函数避免了反馈线性化方法中可能出现的控制器奇异性问题,运用两阶段法构造两个Lyapunov函数,确定出用于建模的有界闭区域,再证明跟踪误差收敛到零.通过理论分析,证明了闭环控制系统全局一致终结有界;仿真结果表明了该方法的有效性.     

注入式混合型有源滤波器的电流跟踪控制

以注入式混合型有源滤波器为例,通过对其建立数学模型,提出了一种递推PI控制作为该混合型有源滤波器的电流跟踪控制算法。为有效解决递推PI控制在稳态性能、动态性能方面存在的矛盾,采用了模糊自调整机构。模糊自调整机构根据实际发出的补偿谐波电流与期望发出的补偿谐波电流之间的误差的大小以及变化趋势等特征,通过模糊推理作出相应决策,在线调整递推PI控制算法的比例系数、积分系数,从而使得系统同时具有较好的稳态、动态性能和自适应能力。相关的稳态、动态实验结果均证明了该电流跟踪控制算法的可行性和有效性。     

基于频域的β-GPC稳定性分析与设计及在中频电源控制中的应用

推导了β-GPC(Generalized Predictive Control,即广义预测控制)的闭环反馈结构和闭环传递函数的递推公式.采用频域响应法分析β-GPC控制下闭环系统的频率特性.通过计算系统开环和闭环极点,分析闭环系统频率特性,设计β-GPC的参数,应用于中频电源控制系统中取得了满意的稳定性和暂态特性.     

汽轮发电机组故障诊断中的模糊量化处理

从３个方面分析了汽轮发电机组故障诊断中存在的模糊性问题,从模糊集的概念出发,提出了隶属函数构造的３点原则．对振动征兆、参数征兆和趋势型征兆构造了隶属度函数,分析处理了它们在故障诊断中的模糊量化问题．     

Output feedback control of discrete-time nonlinear systems with unknown time-delay based on Takagi-Sugeno fuzzy models

In this paper, a new discrete-time fuzzy output feedback control method for nonlinear systems with unknown time-delay is proposed. The method follows on from the analysis and design method for a fuzzy controller and observer proposed by Ma et al. (IEEE T Fuzzy Syst (1998) 6(1):41–51), and the extension of this to nonlinear systems with known time-delay by Cao et al. (IEEE T Fuzzy Syst (2000) 8(2):200–211) For the case of unknown time-delay, we derive the sufficient condition for the asymptotic stability of the equilibrium point by applying Lyapunov-Krasovskii theorem and convert this condition into the famous LMI problem.