智能控制在气动比例位置系统中的应用

本文对由比例阀和无杆汽缸组成的气动位置系统在线性化的基础上进行了数学建模，对模型引入白噪声干扰，它通过噪声模型作用与对象输出端，并利用Hammerstein模型模拟了对象的非线性本质，通过计算机仿真研究了基于遗传算法寻优的神经模糊网络控制的应用效果。与实际结果进行比较，仿真效果良好。     

Application of neuro-fuzzy techniques in oil pipeline ultrasonic nondestructive testing

This paper presents a novel approach to the problem of nondestructive pipeline testing using ultrasonic imaging. The identification of the flaw type and its dimensions are the most important problems in the pipeline inspection. Unlike typical methods, a decision based neural network is used for the detection of flaws. We train a generalized regression neural network to determine the dimensions of the corrosions and generate the whole image of both the internal and external walls of the oil pipeline. As an improvement to the detection algorithm, we introduce fuzzy decision-based neural network algorithms for the detection and classification of the corrosions. The simulation and experimental systems results show that these new methods outperform the existing methods.     

基于模型参考模糊自适应的电液位置伺服系统鲁棒控制研究

针对电液位置伺服系统存在的参数变化及外力扰动，引入模型参考模糊自适应机构，构成模型参考模糊自适应控制，即MRFAC，仿真实验表明，与传统PID相比，该方法可以有效抑制参数扰动及外力干扰，具有很强的鲁棒性。     

模糊控制在半固态浆料制备装置中的应用

介绍了低转速输送管半固态浆料制备装置的原理和组成，采用模糊控制技术改进了该装置的温度控制系统，并详细介绍了该控制系统的总体设计及软硬件方案。试验证明，模糊控制技术的应用提高了该装置的温度控制效果，实现了良好稳定的铝金属半固态浆料微观组织控制作用。     

Power of a Unit‐Root Test and the Initial Condition

Abstract. It is now well known that how the initial observation is generated can have a significant effect on the power of a unit‐root test. In this article, we show that by taking a simple data‐dependent weighted average of the initial condition‐robust test of Elliott and Müller [Journal of Econometrics (2006), forthcoming] and the standard augmented Dickey–Fuller test, we are able to produce a new unit‐root test that can improve power, both asymptotically and in finite samples, over a wide range of possibilities governing the generation of the initial observation.     

仿人智能比例控制器与PID FUZZY控制算法的仿真分析

对于具有二阶惯性加纯滞后的不同对象，分别运用常规ＰＩＤ控制、Ｆｕｚｚｙ控制以及仿人智能控制算法进行仿真研究，并对各种控制算法的控制效果进行分析比较。结果表明：仿人智能控制的调节品质明显优于常规ＰＩＤ及Ｆｕｚｚｙ控制，具有响应速度快、超调小（甚至无超调）、过渡过程时间短、控制精度高、系统适应性强等优点。     

水泥生产除尘效果模糊综合评价研究

文中阐述了基于层次分析的水泥生产除尘效果模糊综合评价方法。利用层次分析将复杂的决策系统分解成若干个简单的决策系统，将模糊数学应用于判别水泥生产除尘效果的优劣，使得难以量化的质量评价问题转化成定量的结果。     

Integrating Fuzzy C-Means and TOPSIS for performance evaluation: An application and comparative analysis

In this paper we introduce a multi-method multiple criteria approach for evaluating the performance of organizations. Performance analysis may include both strategic and operational performance, as well as financial and other less tangible factors. This paper introduces the use of Fuzzy C-Means and TOPSIS for organizational performance evaluation purposes. Using real company data and balanced scorecard accounting and performance dimensions the methodology is applied and evaluated. The predictive abilities of the technique from an organizational performance evaluation approach are evaluated using this data. One of the results from the illustrative application is that economic performance evaluation is not the best predictor of overall viability of some organizations, especially e-commerce based organizations.     

An intelligent negotiator agent design for bilateral contracts of electrical energy

In this paper, an intelligent agent (using the Fuzzy SARSA learning approach) is proposed to negotiate for bilateral contracts (BC) of electrical energy in Block Forward Markets (BFM or similar market environments). In the BFM energy markets, the buyers (or loads) and the sellers (or generators) submit their bids and offers on a daily basis. The loads and generators could employ intelligent software agents to trade energy in BC markets on their behalves. Since each agent attempts to choose the best bid/offer in the market, conflict of interests might happen. In this work, the trading of energy in BC markets is modeled and solved using Game Theory and Reinforcement Learning (RL) approaches. The Stackelberg equation concept is used for the match making among load and generator agents. Then to overcome the negotiation limited time problems (it is assumed that a limited time is given to each generator–load pairs to negotiate and make an agreement), a Fuzzy SARSA Learning (FSL) method is used. The fuzzy feature of FSL helps the agent cope with continuous characteristics of the environment and also prevents it from the curse of dimensionality. The performance of the FSL (compared to other well-known traditional negotiation techniques, such as time-dependent and imitative techniques) is illustrated through simulation studies. The case study simulation results show that the FSL based agent could achieve more profits compared to the agents using other reviewed techniques in the BC energy market.     

A demerit-fuzzy rating system,monitoring scheme and classification for manufacturing processes

For monitoring online manufacturing processes, the proportion of weights imposed on each type of product’s defects (nonconformities or demerits) has a profoundly effective impact on control charts’ performance. Apparently, the demerit-chart approach is superior than the widely-used c-chart scheme, because it allows us to place relative precise weights (real numbers) on defects according to their distinctly inferior degrees affecting the product quality so that the abnormal variations of processes can be literally exposed. However, in many applications, the seriousness of defects is evaluated partially or entirely by the inspectors’ perceptive judgement or knowledge, so with the precise-weight assignment, the demerit rating mechanism is considered to be somewhat constrained and subjective which inevitably leads to the targeted manufacturing process with limited and possibly biased information for online surveillance. To cope with the drawback, a demerit-fuzzy rating system and monitoring scheme is proposed in this paper. We first incorporate fuzzy weights (fuzzy numbers) to properly reflect the severity measures of defects which are categorized linguistically. Then, based on properties of fuzzy set theory and proposed approaches for fuzzy-number ranking, we develop the demerit-fuzzy charting scheme which is capable of discriminating process conditions into multi-intermittent statuses between in-control and out-of-control. This approach improves the traditional process control techniques with the binary-classification restraint for the process conditions. Finally, the proposed demerit-fuzzy rating system, monitoring scheme, and classification is elucidated by an application in garment industry to monitor textile-stitching nonconformities conditions.