DETECTOR: A knowledge-based system for injection molding diagnostics

A knowledge-based system (KBS) for diagnosis of multiple defects in injection molding is presented. The general scheme for knowledge representation based on fuzzy set theory has been shown useful in representing inexact and incomplete information for developing the KBS. An optimality criterion is created for selecting a simple and best cover to explain the given problem. An efficient search algorithm for finding such cover is also discussed.     

多机器人网络中资源公平分配的研究

就如何实现多机器人网络中各独立任务资源的公平分配及其服务质量最优化提出了一种新方法。该方法通过对增益控制参数的模糊控制,增强了系统的鲁棒性,并尽可能快速平稳地实现各项独立任务所需资源的公平分配。     

液压支架工作状态模糊识别系统研究

针对目前综采工作面液压支架压力监测系统只能监测综采工作面压力,不能对液压支架工作状态进行识别的问题,设计了一种液压支架工作状态模糊识别系统。该系统可对现有的综采工作面液压支架压力监测系统监测到的压力数据进行模糊识别,根据模糊识别输出值即可判断液压支架的5种工作状态,即降架、移架、升架、增阻、卸压保持;当液压支架处于增阻工作状态且增阻时间过长时,该系统可及时通知液压支架操作工增大液压支架压力,辅助实现顶煤破碎。测试结果验证了该系统的可行性。     

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.     

Some investigations on fuzzy P + fuzzy I + fuzzy D controller for non-stationary process

The paper addresses the adaptive behaviour of parallel fuzzy proportional plus fuzzy integral plus fuzzy derivative (FP+FI+FD) controller. The parallel FP+FI+FD controller is actually a non-linear adaptive controller whose gain changes continuously with output of the process under control. Two non-stationary processes, whose characteristics change with time, are considered for simulation study. Simulation is performed using software LabVIEW TM . The set-point tracking response of parallel FP+FI+FD is compared with conventional parallel proportional plus integral plus derivative (PID) controller, tuned with the Ziegler-Nichols (Z-N) tuning technique. Simulation results show that conventional PID controller fails to track the set-point and becomes unstable as the process changes its characteristic with time. But the parallel FP+FI+FD controller shows considerably much better set-point tracking response and does not deviate from steady state. Also, a huge spike is observed in the output of PID controller as the reference set-point and process parameters are changed, while the FP+FI+FD controller gives spike free control signal.