煤矿监控图像增强算法的分析与实现

针对煤矿井下粉尘多、光照差的恶劣环境使得矿井监控图像偏暗、对比度低、视觉效果差的特点,提出了一种基于小波变换和模糊理论的图像增强算法。该算法选择小波变换为工具分解图像,应用新的模糊隶属度和增强算子对高频信息进行模糊处理,利用直方图均衡化对低频信息进行处理,最后对图像进行重构。处理结果较好地增强了图像细节信息,从整体上改善了图像效果。     

基于D-FNN的混沌铁磁谐振系统非线性补偿控制方法

针对电网混沌铁磁谐振系统产生的混沌现象,提出基于动态模糊神经网络的混沌铁磁谐振系统非线性补偿控制方法。该方法采用动态模糊神经网络来逼近系统的非线性部分,消除了过电压的混沌现象,将系统稳定到目标位置,实现了对系统的非线性补偿控制。Matlab仿真结果表明,基于动态模糊神经网络的非线性补偿控制方法控制结果正确,响应快速。     

遗传算法模糊PID测控系统在环控试验台中的应用

飞机环控试验台须模拟流量0～14000kg/h、压力0～2.5MPa和常温～500℃的空气环境;项目要求测控范围广、精度±1%且不超调;空气状态具有非线性、时变等特点,且控制参数之间存在复杂耦合;针对以上难点,设计了分布式测控系统,提出了改进的智能PID控制方案;通过遗传算法分段整定PID参数,离线建立PID数据库,使系统能够根据控制目标值选择最优PID初值;在此基础上,结合模糊推理在线调整PID参数,使系统具有了自适应性,能在具体工况和干扰下达到很好的控制效果;实际应用中完全满足了指标要求,解决了传统PID的控制难点,对类似的复杂系统有一定借鉴意义。     

基于模糊神经网络的电弧炉控制系统及仿真

电极调节系统是电弧炉炼钢过程不可缺少的基本装备。目前常用的电极调节器大多是基于单相意识,导致电极调节中电极往往误动作,大大影响了电弧炉的运行效益。在模糊控制和神经网络的基础上,探索研究一种基于三相意识的电弧炉控制新方法,采用模糊神经网络调节器控制对电弧炉电极进行调节,控制弧流弧压稳定在一定范围之内,使电弧炉冶炼达到有功功率最大化,进一步提高电弧炉的综合运行效益,降低能耗、减轻对电网危害。并用matlab对模糊神经网络进行仿真。结果表明,本网络具有较快的训练速度和较高的泛化能力,满足电弧炉控制的要求,其控制性能优于常规电弧炉控制系统。     

Asymptotic and robust stability of T‐S fuzzy genetic regulatory networks with time‐varying delays

In this paper, we propose and investigate a new general model of fuzzy genetic regulatory networks described by the Takagi–Sugeno (T‐S) fuzzy model with time‐varying delays. By using a Lyapunov functional approach and linear matrix inequality (LMI) techniques, the stability criteria for the delayed fuzzy genetic regulatory networks are expressed as a set of LMIs, which can be solved numerically by LMI toolbox in Matlab. Two fuzzy genetic network example are given to verify the effectiveness and applicability of the proposed approach. Copyright © 2011 John Wiley & Sons, Ltd.     

Adaptive fuzzy dynamic surface control for a class of perturbed nonlinear time-varying delay systems with unknown dead-zone

In this paper,adaptive dynamic surface control(DSC) is developed for a class of nonlinear systems with unknown discrete and distributed time-varying delays and unknown dead-zone.Fuzzy logic systems are used to approximate the unknown nonlinear functions.Then,by combining the backstepping technique and the appropriate Lyapunov-Krasovskii functionals with the dynamic surface control approach,the adaptive fuzzy tracking controller is designed.Our development is able to eliminate the problem of "explosion of complexity" inherent in the existing backstepping-based methods.The main advantages of our approach include:1) for the n-th-order nonlinear systems,only one parameter needs to be adjusted online in the controller design procedure,which reduces the computation burden greatly.Moreover,the input of the dead-zone with only one adjusted parameter is much simpler than the ones in the existing results;2) the proposed control scheme does not need to know the time delays and their upper bounds.It is proven that the proposed design method is able to guarantee that all the signals in the closed-loop system are bounded and the tracking error is smaller than a prescribed error bound,Finally,simulation results demonstrate the effectiveness of the proposed approach.     

Direct adaptive fuzzy control with membership function tuning

In this paper, a direct self‐structured adaptive fuzzy control is introduced for the class of nonlinear systems with unknown dynamic models. Control is accomplished by an adaptive fuzzy system with a fixed number of rules and adaptive membership functions. The reference signal and state errors are used to tune the membership functions and update them instantaneously. The Lyapunov synthesis method is also used to guarantee the stability of the closed loop system. The proposed control scheme is applied to an inverted pendulum and a magnetic levitation system, and its effectiveness is shown via simulation. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Entropy/cross entropy-based group decision making under intuitionistic fuzzy environment

We study the group decision making problem under intuitionistic fuzzy environment. Based on entropy and cross entropy, we give two methods to determine the optimal weights of attributes, and develop two pairs of entropy and cross entropy measures for intuitionistic fuzzy values. Then, we discuss the properties of these measures and the relations between them and the existing ones. Furthermore, we introduce three new aggregation operators, which treat the membership and non-membership information fairly, to aggregate intuitionistic fuzzy information. Finally, several practical examples are presented to illustrate the developed methods.     

A new ECG beat clustering method based on kernelized fuzzy c-means and hybrid ant colony optimization for continuous domains

The kernelized fuzzy c-means algorithm uses kernel methods to improve the clustering performance of the well known fuzzy c-means algorithm by mapping a given dataset into a higher dimensional space non-linearly. Thus, the newly obtained dataset is more likely to be linearly seprable. However, to further improve the clustering performance, an optimization method is required to overcome the drawbacks of the traditional algorithms such as, sensitivity to initialization, trapping into local minima and lack of prior knowledge for optimum paramaters of the kernel functions. In this paper, to overcome these drawbacks, a new clustering method based on kernelized fuzzy c-means algorithm and a recently proposed ant based optimization algorithm, hybrid ant colony optimization for continuous domains, is proposed. The proposed method is applied to a dataset which is obtained from MIT–BIH arrhythmia database. The dataset consists of six types of ECG beats including, Normal Beat (N), Premature Ventricular Contraction (PVC), Fusion of Ventricular and Normal Beat (F), Artrial Premature Beat (A), Right Bundle Branch Block Beat (R) and Fusion of Paced and Normal Beat (f). Four time domain features are extracted for each beat type and training and test sets are formed. After several experiments it is observed that the proposed method outperforms the traditional fuzzy c-means and kernelized fuzzy c-means algorithms.     

A new time invariant fuzzy time series forecasting method based on particle swarm optimization

In the analysis of time invariant fuzzy time series, fuzzy logic group relationships tables have been generally preferred for determination of fuzzy logic relationships. The reason of this is that it is not need to perform complex matrix operations when these tables are used. On the other hand, when fuzzy logic group relationships tables are exploited, membership values of fuzzy sets are ignored. Thus, in defiance of fuzzy set theory, fuzzy sets’ elements with the highest membership value are only considered. This situation causes information loss and decrease in the explanation power of the model. To deal with these problems, a novel time invariant fuzzy time series forecasting approach is proposed in this study. In the proposed method, membership values in the fuzzy relationship matrix are computed by using particle swarm optimization technique. The method suggested in this study is the first method proposed in the literature in which particle swarm optimization algorithm is used to determine fuzzy relations. In addition, in order to increase forecasting accuracy and make the proposed approach more systematic, the fuzzy c-means clustering method is used for fuzzification of time series in the proposed method. The proposed method is applied to well-known time series to show the forecasting performance of the method. These time series are also analyzed by using some other forecasting methods available in the literature. Then, the results obtained from the proposed method are compared to those produced by the other methods. It is observed that the proposed method gives the most accurate forecasts.