Fuzzy inferring mathematical morphology and optical implementation

Fuzzification is introduced into gray-scale mathematical morphology by using two-input one-output fuzzy rule-based inference systems. The fuzzy inferring dilation or erosion is defined from the approximate reasoning of the two consequences of a dilation or an erosion and an extended rank-order operation. The fuzzy inference systems with numbers of rules and fuzzy membership functions are further reduced to a simple fuzzy system formulated by only an exponential two-input one-output function. Such a one-function fuzzy inference system is able to approach complex fuzzy inference systems by using two specified parameters within it-a proportion to characterize the fuzzy degree and an exponent to depict the nonlinearity in the inferring. The proposed fuzzy inferring morphological operators tend to keep the object details comparable to the structuring element and to smooth the conventional morphological operations. Based on digital area coding of a gray-scale image, incoherently optical correlation for neighboring connection, and optical thresholding for rank-order operations, a fuzzy inference system can be realized optically in parallel. (C) 1996 Society of Photo-Optical Instrumentation Engineers.

基因芯片PCR温度模糊自整定PID控制算法的研究

在核酸扩增反应仪中,基因芯片核酸扩增反应过程要求实现温度高精度快速跟踪控制,常规温控方案和算法难以实现。将模糊推理系统与常规PID控制方式相结合,采用模糊自整定PID控制算法实现了温度快速跟踪控制。实验结果表明:模糊自整定PID控制算法比常规PID算法具有更强的鲁棒性,能够克服控制对象热惯性参数时变性的影响,降低了输出温度最大超调量,提高了稳态精度。

具有不完全微分的Fuzzy-GA PID控制器及其在智能仿生人工腿中的应用

以模糊推理和遗传算法为基础，提出了一种新的具有不完全微分的最优PID控制器的设计方法，该控制器由离线和在线两部分组成，在离线部分，以系统响应的超调量、上升时间以及调整时间为性能指标，利用遗传算法搜索出一组最优的PID参数Kp^*、Ti^*和Td^*，作为在线部分调整的初始值，在在线部分，一个专用的PID参数优化程序以离线部分获得Kp^*、Ti^*和Td^*为基础，根据系统当前的误差e和误差变化率e^.，通过一个模糊推理系统在线调整系统瞬态响应的PID参数，以确保系统的响应具有最优的动态和稳态性能．该控制器已被用来控制由作者设计的智能仿生人工腿中的执行电机．计算机仿真结果表明，该控制器具有良好的控制性能和鲁棒性能。

Optimal Fuzzy PID Controller with Incomplete Derivation and Its Simulation Research on Application of Intelligent Artificial Legs

提出了一种新的最优模糊PID控制器，它由两部分组成，即在线模糊推理机构和带有不完全微分的常规PID控制器，在模糊推理机构中，引入了三个可调节因子xp,xi和xd，其作用是进一步修改和优化模糊推理的结果，以使控制器对一个给定对象具有最优的控制效果，可调节因子的最优值采用ITAE准则及Nelder和Mead提出的柔性多面体最优搜索算法加以确定，这种PID控制器被用来控制由作者设计的智能人工腿中的一个直流电机，仿真结果表明该控制器的设计是非常有效的，它可被用于控制各种不同的对象和过程。

最优Fuzzy—GA PID控制器及其应用

提出了一种基于模糊推理与遗传算法的最优PID控制器的设计方法，该控制器由离线和在线2部分组成，在离线部分，以系统响应的超调量、上升时间及调速时间为性能指标，利用遗传算法搜索出一组最优的PID参数Kp^*，Ti^*及Td^*，为在线部分调节的初始值，在在线部分，采用一个专用的PID参数优化程序，以离线部分获得的Kp^*,Ti^*及Td^*为基础，根据系统当前的误差e和误差变化率·↑e，通过模糊推理在线调整系统瞬态响应的PID参数，以确保系统的响应具有最优的动态和稳态性能，计算机仿真结果表明，与传统的PID控制器相比，这种最优PID控制器具有良好的控制性能和鲁棒性能，可用于控制不同的对象和过程。

基于模糊推理水下作业系统运动控制研究

水下作业系统是运动学冗余系统,本文将模糊推理方法融入基于任务优先运动学控制算法,对系统载体与机械手进行协调运动分配,同时对系统多个任务进行优化。通过带有3自由度水下机械手的水下作业系统进行算例仿真研究,说明运动控制算法的有效性。

水泥回转窑热工控制模型研究及其仿真

水泥回转窑是建材工业发展的方向，我国是水泥生产大国，而国内回转窑与发达国家相差甚大，尤其在热工控制方面。由于水泥回转窑具有时变、分布参数和非线性特性，是一个典型的复杂过程，因而水泥回转窑控制系统是一个很有意义且困难的研究方向，本论文在借鉴国内外同类研究的基础上，提出了模糊专家系统控制模型，进行了深入地研究，并且对该模型进行了计算机仿真，希望通过这项研究，提高我国在水泥回转窑先进智能技术的控制水平。主要研究内容有：对水泥回转窑的热工过程进行了详细分析，对其不同控制方法进行全面的综述，对水泥回转窑实现控制的人工智能方法进行了全面的综述，并介绍了国内外的研究现状；研究了对水泥回转窑控制的模糊控制模型、专家系统设计方法，以及利用模糊控制与专家系统相结合的方法对水泥回转窑进行安全而有效控制的方法：研究了专家系统的实时性问题，提出了静态排列专家系统的推理时间模型、优化排列专家系统的时间估计模型与排列准则；利用计算机仿真方法，实现对水泥回转窑这种复杂而昂贵系统控制进行实验研究，以较低的代价实现对其分析。本论文的主研究成果如下：1. 详细研究了水泥回转窑的技术发展与结构演化过程，分析了水泥回转窑的热工过程以及影响水泥生产的各种因素，总结了影响水泥生产质量的主要因素与次要因素，确定了控制水泥回转窑的主要并且可测量的过程参数。2. 用推理全成方法研究模糊控制模型，实现从模糊的角度研究水泥回转窑的控制：从专家 系统角度研究水泥回转窑的控制问题，并提取了有关的专家系统控制规则；在模糊控制与专家系统的基础上，将水泥回转窑的模糊控制与专家系统相结合，利用层次化的控制器结构，底层为模糊控制器，顶层为专家系统，实现了水泥回转窑的安全与有效控制。3. 从定量的角度研究了专家系统的推理时间问题，给出了三种相应的时间估计模型，这不仅可以分析水泥回转窑系统中的专家系统的实时性，而且也可以分析一般专家系统的推理时间和问题。4. 本文提出的计算机仿真工具，为三组数据分别进行计算机仿真，以此研究水泥回转窑控制策略的性能以及对其动态过程进行分析，为水泥回转窑这样的复杂且昂贵的控制系统研究提供有效的手段。

一种基于Agent的自适应的分布式入侵检测系统

针对传统分布式入侵检测系统组件之间依赖程度大、系统不够健壮且入侵检测系统自身结构固定不能适应入侵的变化的问题,提出了一种基于Agent的自适应的分布式入侵检测系统(简称AAA-DIDS)·AAADIDS采用Agent概念重新构造系统的组件,改进了分布式入侵检测系统由于高层节点单一无冗余而产生的可靠性差的缺陷,从构造上克服了分布式入侵检测系统的脆弱性·同时,AAADIDS系统采用智能技术构建了自适应的入侵检测系统模型,增加了系统应对入侵行为变化的智能性·AAA-DIDS系统相对于传统的分布式入侵检测系统有效地提高了系统自身的可靠性和针对外界变化的适应能力·

An application of the analytic hierarchy process and fuzzy logic inference in a decision support system for forage selection

Forage selection plays a prominent role in the process of returning cultivated lands back into grasslands. The conventional method of selecting forage species can only provide attempts for problem-solving without considering the relationships among the decision factors globally. Therefore, this study is dedicated to developing a decision support system to help farmers correctly select suitable forage species for the target sites. After collecting data through a field study, we developed this decision support system. It consists of three steps: (1) the analytic hierarchy process (AHP), (2) weights determination, and (3) decision making. In the first step, six factors influencing forage growth were selected by reviewing the related references and by interviewing experts. Then a fuzzy matrix was devised to determine the weight of each factor in the second step. Finally, a gradual alternative decision support system was created to help farmers choose suitable forage species for their lands in the third step. The results showed that the AHP and fuzzy logic are useful for forage selection decision making, and the proposed system can provide accurate results in a certain area (Gansu Province) of China.

Numerical simulation of an adaptive optics system with laser propagation in the atmosphere

A comprehensive model of laser propagation in the atmosphere with a complete adaptive optics (AO) system for phase compensation is presented, and a corresponding computer program is compiled. A direct wave-front gradient control method is used to reconstruct the wave-front phase. With the long-exposure Strehl ratio as the evaluation parameter, a numerical simulation of an AO system in a stationary state with the atmospheric propagation of a laser beam was conducted. It was found that for certain conditions the phase screen that describes turbulence in the atmosphere might not be isotropic. Numerical experiments show that the computational results in imaging of lenses by means of the fast Fourier transform (FFT) method agree well with those computed by means of an integration method. However, the computer time required for the FFT method is 1 order of magnitude less than that of the integration method. Phase tailoring of the calculated phase is presented as a means to solve the problem that variance of the calculated residual phase does not correspond to the correction effectiveness of an AO system. It is found for the first time to our knowledge that for a constant delay time of an AO system, when the lateral wind speed exceeds a threshold, the compensation effectiveness of an AO system is better than that of complete phase conjugation. This finding indicates that the better compensation capability of an AO system does not mean better correction effectiveness. (C) 2000 Optical Society of America.

Effects of Noise and Detection Error in a Dynamic Adaptive Optics System

It is well known that noise and detection error can affect the performances of an adaptive optics (AO) system. Effects of noise and detection error on the phase compensation effectiveness in a dynamic AO system are investigated by means of a pure numerical simulation in this paper. A theoretical model for numerically simulating effects of noise and detection error in a static AO system and a corresponding computer program were presented in a previous article. A numerical simulation of effects of noise and detection error is combined with our previous numeral simulation of a dynamic AO system in this paper and a corresponding computer program has been compiled. Effects of detection error, readout noise and photon noise are included and investigated by a numerical simulation for finding the preferred working conditions and the best performances in a practical dynamic AO system. An approximate model is presented as well. Under many practical conditions such approximate model is a good alternative to the more accurate one. A simple algorithm which can be used for reducing the effect of noise is presented as well. When signal to noise ratio is very low, such method can be used to improve the performances of a dynamic AO system.