CHARACTERIZATION OF THE FLUE GAS OF A COAL-FIRED POWER PLANT BY ULTRAVIOLET-VISIBLE SPECTROSCOPY AND A TAGAKI-SUGENO MODEL

Quantitative analysis of flue gas of thermal power plant is of great significance for environmental protection. This article reports the characterization of the flue gas of a coal-fired power plant by ultraviolet-visible spectroscopy and a Tagaki-Sugeno model consisting of a series of fuzzy rules. The wavelength signals are expressed by linguistic terms in the rule antecedent. The component concentration is a linear combination of the wavelength signals. Real spectral data obtained from the flue gas is adopted in the model. Prediction models for sulfur dioxide, nitric oxide, and nitrogen dioxide were built with the Tagaki-Sugeno model, back-propagation neural network, least squares support vector machine, and partial least squares. The effectiveness of the models was primarily evaluated by root-mean-squared error of prediction. The experimental results verified that the predictive capability of the Tagaki-Sugeno model was the highest.     

基于SIRM的倒立摆模糊控制器

探讨了倒立摆控制中的一些方法和应用，指出了通常模糊控制规则过于复杂的问题，设计了基于SIRM(单输入规则模块)的模糊控制器，既解决了模糊控制规则复杂的问题，又实现了倒立摆的快速稳定。最后，通过仿真验证了控制系统的效果。     

Extraction Fuzzy Linguistic Rules from Neural Networks for Maximizing Tool Life in High-speed Milling Process

In metal cutting industry it is a common practice to search for optimal combination of cutting parameters in order to maximize the tool life for a fixed minimum value of material removal rate(MRR). After the advent of high-speed milling(HSM) pro cess, lots of experimental and theoretical researches have been done for this purpose which mainly emphasized on the optimization of the cutting parameters. It is highly beneficial to convert raw data into a comprehensive knowledge-based expert system using fuzzy logic as the reasoning mechanism. In this paper an attempt has been presented for the extraction of the rules from fuzzy neural network(FNN) so as to have the most effective knowledge-base for given set of data. Experiments were conducted to determine the best values of cutting speeds that can maximize tool life for different combinations of input parameters. A fuzzy neural network was constructed based on the fuzzification of input parameters and the cutting speed. After training process, raw rule sets were extracted and a rule pruning approach was proposed to obtain concise linguistic rules. The estimation process with fuzzy inference showed that the optimized combination of fuzzy rules provided the estimation error of only 6.34 m/min as compared to 314 m/min of that of randomized combination of rules.     

On observer-based controller design for Sugeno systems with unmeasurable premise variables

This paper considers the design of observer-based controller for a class of continuous-time nonlinear systems presented by Takagi–Sugeno (T–S) model with unmeasurable premise variables. This T–S structure can represent a larger class of nonlinear systems as compared to the measurable premise variable case but its analysis is more complicated. To reduce the design complexity, a common output model for subsystems is employed by the use of local nonlinear rules. As a result, the proposed T–S structure reduces the number of rules in the Sugeno model as well as the analysis complexity. The proposed controller guarantees exponential convergence of states based on the fuzzy Lyapunov function analysis and Linear Matrix Inequality (LMI) formulation. Simulation results illustrate effectiveness of the proposed method.     

Nonlinear dynamic systems identification using recurrent interval type-2 TSK fuzzy neural network – A novel structure

In this study, a novel structure of a recurrent interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy neural network (FNN) is introduced for nonlinear dynamic and time-varying systems identification. It combines the type-2 fuzzy sets (T2FSs) and a recurrent FNN to avoid the data uncertainties. The fuzzy firing strengths in the proposed structure are returned to the network input as internal variables. The interval type-2 fuzzy sets (IT2FSs) is used to describe the antecedent part for each rule while the consequent part is a TSK-type, which is a linear function of the internal variables and the external inputs with interval weights. All the type-2 fuzzy rules for the proposed RIT2TSKFNN are learned on-line based on structure and parameter learning, which are performed using the type-2 fuzzy clustering. The antecedent and consequent parameters of the proposed RIT2TSKFNN are updated based on the Lyapunov function to achieve network stability. The obtained results indicate that our proposed network has a small root mean square error (RMSE) and a small integral of square error (ISE) with a small number of rules and a small computation time compared with other type-2 FNNs.     

A fuzzy-nets tool-breakage detection system for end-milling operations

This paper describes a new approach, the fuzzy-nets system, for monitoring tool breakage in end-milling operations. The fuzzy-nets tool-breakage detection (FNTBD) system has a self-learning capability to generate rule bases and to fine tune the term sets of each linguistic variable to the appropriate level of granularity. A self-learning algorithm for developing the FNTBD system consists of five steps:

1. Divide the input space into fuzzy regions.
2. Generate fuzzy rules from given data pairs through experimentation.
3. Avoid conflicting rules based on top-down or bottom-up methodologies.
4. Develop a combined fuzzy rule base.
5. Determine a mapping system based on the fuzzy rule base.

Learning is accomplished by fine-tuning the parameters in the fuzzy-nets system within the on-line learning capability. Following establishment of the rule base, the performance of the FNTBD system is examined for an end-milling operation. It was observed and verified experimentally that this new FNTBD approach can successfully detect tool breakage in end-milling operations.     

Disturbance-observer-based fuzzy model predictive control for nonlinear processes with disturbances and input constraints

This paper proposes a disturbance-observer-based fuzzy model predictive control (DOBFMPC) scheme for the nonlinear process subject to disturbances and input constraints. The proposed control scheme is composed of the baseline fuzzy model predictive control (FMPC) law designed on the Takagi–Sugeno fuzzy model and the disturbance compensation law. To build a fuzzy model of appropriate complexity and accuracy for the nonlinear process model, a systematic approach is developed via the gap metric to determine the linearization points. With FMPC, the asymptotic stability is theoretically proved, and the input constraints are satisfied by both the free control variables and the future control inputs in the form of the state feedback law. The disturbance compensation gain is designed such that the influence of the disturbance is removed from the output channels by the composite DOBFMPC law at the steady state. The application to a subcritical boiler–turbine system demonstrate the effectiveness of the proposed control scheme.     

数控机床切削用量的模糊控制

阐述了在数控机床上使用模糊控制理论,对切削用量做出合理选择并使其达到最优值的一种方法。首先,利用模糊控制原理,结合数控机床实际,将切削力、毛坯材料、主轴转速、切削温度作为输入,进给速度、背吃刀量作为输出,设计出相应的模糊控制推理框图,展示推理过程;其次,依据推理框图和数控机床输入、输出条件,制订模糊推理规则,提出输入量检测的方法,推导出输入条件和输出条件的隶属度函数并以图形展示,定义输入、输出条件的模糊量子集;最后,根据输入、输出条件和模糊推理规则,构造出模糊控制规则表,实现了采用人类语言、模拟人类思维的控制方式。     

基于支持向量机模糊推理的二级倒立摆控制

本文提出了一种用于非线性系统控制的支持向量机模糊推理模型.该模型利用支持向量机回归的原理,从训练数据中提取模糊规则并进行简化;采用核函数来描述模糊推理系统,该模糊推理系统具有不必事先确定模糊规则数目、良好的泛化能力等优点.使用该模型对直线二级倒立摆系统构造模糊控制器并进行了实验研究,研究结果表明这种新的模糊规则提取方法对于非线性系统的控制是有效的,由支持向量确定的模糊规则不会出现规则数目"爆炸"的问题,该方法在不便事先确定模糊规则的复杂非线性系统控制中有着重要的应用价值.     

Investigating the surface quality of the burnished brass C3605—fuzzy rule-based approach

In this work, prediction of burnished surface roughness (R _a) is achieved by using a fuzzy rule-based system. The process state variables used were burnishing speed, feed, and depth. The fuzzy rule-based system has achieved an accuracy of 95.4 % to predict the burnished surface roughness and proved to be convenient in terms of least computational complexity and dealing with nonlinear data such as that obtained in this work.     

A new fuzzy network data envelopment analysis model for measuring the performance of agility in supply chains

Data envelopment analysis (DEA) is a linear programming method for assessing the efficiency and productivity of organizational units called decision-making units (DMUs). We propose a new network DEA (NDEA) model for measuring the performance of agility in supply chains. The uncertainty of the input and output data is modeled with linguistic terms parameterized with fuzzy sets. The proposed fuzzy NDEA model is linear and independent of the α-cut variables. The linear feature allows for a quick identification of the global optimum solution and the α-cut independency feature allows for a significant reduction in the computational efforts. We show that our model always generate solutions within a bounded feasible region. Our model also eliminates the potential for conflict by producing unique interval efficiency scores for each DMU. The proposed model is used to measure the performance of agility in a real-life case study in the dairy industry.     

基于遗传算法的多自由度非线性振动的模糊控制方法

由于非线性系统的复杂性,难以得到基于线性控制规律的有效控制方法。本文对于复杂的多自由度非线性振动系统提出了基于遗传传算法的主动模糊控制方法。     

A bi-criteria nonlinear fluctuation smoothing rule incorporating the SOM–FBPN remaining cycle time estimator for scheduling a wafer fab—a simulation study

This paper proposes a bi-criteria nonlinear fluctuation smoothing rule to further improve the performance of job scheduling in a wafer fabrication factory (wafer fab). The rule is based on the well-known fluctuation smoothing rules. First, the remaining cycle time of a job is estimated by applying the self-organization map–fuzzy back propagation network approach to improve the estimation accuracy. Second, two nonlinear forms of the fluctuation smoothing rules are obtained to enhance the balance and responsiveness. Third, the two nonlinear fluctuation smoothing rules are merged into a bi-criteria rule for considering two performance measures (average cycle time and cycle time variation) at the same time. Finally, the content of the bi-criteria rule can be tailored for the wafer fab and be scheduled with an adjustable factor. To evaluate the effectiveness of the proposed methodology, a production simulation was conducted. According to the experimental results, the proposed methodology outperformed some of the existing approaches by reducing the average cycle time and cycle time variation at the same time. In addition, the experimental results showed that the bi-criteria rule made it possible to improve one performance measure without raising the expense of another one.     

Modeling,stability analysis,and computational aspects of some simplest nonlinear fuzzy two-term controllers derived via center of area/gravity defuzzification

The mathematical models reported in the literature so far have been found using Center of Sums (CoS) defuzzification method only. It appears that no one has found models using Center of Area (CoA) or Center of Gravity (CoG) defuzzification method. Although there have been some works reported to deal with modeling of fuzzy controllers via Centroid method, all of them have in fact used CoS method only. In this paper, for the first time mathematical models of the simplest Mamdani type fuzzy Proportional Integral (PI)/Proportional Derivative (PD) controllers via CoG defuzzification are presented. L-type and Γ-type membership functions over different Universes of Discourse (UoDs) are considered for the input variables. L-type, Π-type and Γ-type membership functions are considered for the output variable. Three linear fuzzy control rules relating all four input fuzzy sets to three output fuzzy sets are chosen. Two triangular norms namely Algebraic Product (AP) and Minimum (Min), Maximum (Max) triangular co-norm, and two inference methods, Larsen Product (LP) and Mamdani Minimum (MM), are used. Properties of the models are studied. Stability analysis of closed-loop systems containing one of these controller models in the loop is done using the Small Gain theorem. Since digital controllers are implemented using digital processors, computational and memory requirements of these fuzzy controllers and conventional (nonfuzzy) controllers are compared. A rough estimate of the computational time taken by the digital computer while implementing any of these discrete-time fuzzy controllers is given. Two nonlinear plants are considered to show the superiority of the simplest fuzzy controller obtained using CoA or CoG defuzzification method over the simplest fuzzy controller obtained using CoS method and reported recently. Real-time implementation of one of the developed controller models is done on coupled tank experimental setup to show the feasibility of the developed model.     

Multiple objective evolutionary algorithm for temporal linguistic rule extraction

Autonomous temporal linguistic rule extraction is an application of growing interest for its relevance to both decision support systems and fuzzy controllers. In the presented work, rules are evaluated using three qualitative metrics based on their representation on the truth space diagram. Performance metrics are then treated as competing objectives and the multiple objective evolutionary algorithm is used to search for an optimal set of nondominant rules. Novel techniques for data pre-processing and rule set post-processing are designed that deal directly with the delays involved in dynamic systems. Data collected from a simulated hot and cold water mixer are used to validate the proposed procedure.     

Leanness evaluation using IF–THEN rules

Lean manufacturing is a manufacturing system aimed at waste elimination thereby streamlining the process. Leanness is the performance measure of lean manufacturing. The measurement of leanness necessitates fuzzy logic, due of the existence of impreciseness and vagueness. The calculation of leanness using fuzzy logic manually is a time consuming and highly repetitive job. To overcome this, fuzzy logic-based inference method has been attempted in this research study. A conceptual model consisting of three levels namely enabler, criterion, and attributes has been developed. Then the linguistic variables are assigned and the membership functions are defined. Leanness level has been computed using IF–THEN rules based interface method. This is followed by gap analysis to identify the weaker criteria. Then suitable proposals have been derived to overcome these obstacles towards leanness improvement of the organization.     

Absorption Spectral Characteristics of Infrared Radiation in Silicon Dioxide Films for Thermal Radiation Detectors

Optoelectronics, Instrumentation and Data Processing - The absorption spectral characteristics of silicon dioxide films in the IR range (λ = 8–14 µm) were studied to determine the...     

基于模糊关联规则的预测系统遗传优化

为提高预测系统中的预测精度,提出了一种基于模糊关联规则的优化的预测系统设计方法。该方法通过两个阶段来实现:首先采用竞争聚集算法得到各数量型属性优化的模糊集个数,从而挖掘出优化的模糊关联规则。在得到用于构建预测系统规则库的模糊关联规则后,采用遗传算法约简冗余规则库,实现精确性和解释性的折衷,以提高预测精度。最后将此方法运用于Abalone样本数据集进行实验分析,证实此方法解决了模糊关联规则的冗余问题,有效提高了预测精度。     

基于DSP的实时T-S型模糊控制器设计及其在直流无刷电机控制中的应用

提出了一种简化模糊控制算法，它通过把系统的输入输出空间划分为一个完备的模糊模式集，且寻找与实时输入对应的模糊模式，对整个系统进行了简化；在此基础上，设计了以数字信号处理器(DSP)为核心的模糊控制器；描述了其在直流无刷电机控制中的应用。实验结果表明，该模糊控制器具有实时性强、响应速度快、精度高的特点。     

DIAGNOSTIC RULE EXTRACTION FROM TRAINED FEEDFORWARD NEURAL NETWORKS

This paper describes a method of extracting diagnostic rules from trained diagnostic feedforward neural nets that are constructed to recognise different mechanical faults using automated weight and structure learning algorithms. The rule extracting method is based on an interpretation that considers hidden neurons as partitions in the input space. An initial set of rules is then generated from the training data and the subspaces defined by the partitions. A procedure consisting of a number of algorithms is then used to simplify and reduce the set of initial rules step by step. To demonstrate and evaluate the rule extraction method, diagnostic rules for detecting a high-pressure air compressor's (HPAC) suction and discharge valve faults were extracted from static measurements including temperatures and pressures of various stages of the compressor.