Optimization of bead geometry of submerged arc weld using fuzzy based desirability function approach

The present study highlights application of Taguchi’s robust design coupled with fuzzy based desirability function approach for optimizing multiple bead geometry parameters of submerged arc weldment. Fuzzy inference system has been adapted to avoid uncertainly, imprecision and vagueness in experimentation as well as in data analysis by traditional Taguchi based optimization approach. Detailed methodology and unique features of the proposed method has been highlighted through a case study. The said approach can efficiently be used in off-line quality control of any production process as well as automation of the process.     

Design of a switching controller for nonlinear systems with unknown parameters based on a fuzzy logic approach

This paper deals with nonlinear plants subject to unknown parameters. A fuzzy model is first used to represent the plant. An equivalent switching plant model is then derived, which supports the design of a switching controller. It will be shown that the closed-loop system formed by the plant and the switching controller is a linear system. Hence, the system performance of the closed-loop system can be designed. An application example on controlling a two-inverted pendulum system on a cart will be given to illustrate the design procedure of the proposed switching controller.     

基于正交数值试验的精馏过程参数的优化

精馏是1种重要的高成本的工业单元操作,其操作费用及设备投资费用主要和进料位置、回流比及塔板数有关.然而,过程参数对精馏操作费用和设备投资费用的影响是复杂的,且参数之间有着强耦合关系.因此本文结合丙二醇脱水塔过程参数的优化实例,引入正交试验设计思想,基于Aspen Plus的模拟结果,确定精馏过程的年度化总费用,通过正交数值试验,实现参数优化,并验证了以上方法的可行性和有效性.结果表明,依据基于正交数值试验得到的精馏过程最优参数进行设计,即第6块板进料,回流比为1,塔板数为9,可使丙二醇脱水塔年度化总费用比现行工程实际年度化总费用降低0.35%,从而弥补了依次进行单参数优化方法中的缺陷,达到了节能降耗的目的.     

车联网中基于模糊逻辑处理的多跳广播协议

多跳广播是车联网中告警消息传播的关键技术之一。在真实环境下,由于车辆的高速移动、网络信号不稳定等若干因素容易造成大量失真数据。现有的多跳广播协议在选择中继转发节点时,往往忽略了失真数据所带来的不良影响,从而导致其实际广播应用效果不佳。综合考虑交通环境下无线网络信号的可靠性、消息传输的实时性及道路交通状况信息,提出了一种基于模糊逻辑处理的广播协议(MBOF),该协议首先采用模糊逻辑选择中继节点,提高广播机制的容错能力,确保告警消息的定向传输,然后通过使用候补转发机制来降低告警消息丢失率。实验结果表明,MBOF协议在容错能力上较相关协议有较大提高。     

多Agent谈判：一种基于模糊逻辑控制的方法

在分析各种Agent谈判模型不足的基础上，首先给出基于黑板模式的谈判Agent体系结构，然后设计了一个通用的谈判Agent建议的综合评价方法。此外，应用标准可加性模型原理设计了谈判Agent的策略模型，从而构建了应用范围广泛的自动化谈判Agent模型。     

Analyzing speech of patients with vocal polyps based on channel parameters and fuzzy logic systems

In this paper, the spectrum map of speech samples is used to identify patients with vocal polypus. Comparing the spectrum map with impulse response in wireless communication channels, statistical characteristic root-mean square-delay spread (RMSD) and standard deviation (SD) are employed to describe the speech frequency domain characteristics. Fuzzy logic system (FLS) is used to make polypus patients diagnosis. RMSD and SD are used as two antecedents and fuzzy rules are designed based on the data we collected from polypus patients and normal people. Based on the real data, we demonstrate that the FLS could be used in polypus patients’ diagnosis with very low probability of miss detection and 0% false alarm rate.     

An effective hybrid approach of desirability,fuzzy logic,ANFIS and LAPO algorithm for optimizing compliant mechanism

This paper proposes an efficient hybrid approach for solving multi-objective optimization design of a compliant mechanism. The approach is developed by integrating desirability function approach, fuzzy logic system, adaptive neuro-fuzzy inference system, and Lightning attachment procedure optimization. Box–Behnken design is used to form a numerically experimental matrix. First, a refinement of design variables is conducted through analysis of variance and Taguchi approach in terms of considerably eliminating space of design variables and computation efforts. Next, desirability of two objective functions is computed and transferred into the fuzzy logic system. The output of fuzzy logic system is regarded as single combined objective function. Subsequently, a modeling for fuzzy output is developed via adaptive neuro-fuzzy inference system. Then, LAPO algorithm is adopted for solving the optimization problem. By investigating three different numerical examples, performance of the proposed approach is validated. Numerical results revealed that the proposed approach has a computational accuracy better than that of Taguchi-based fuzzy logic reasoning. Finally, case study 1 is chosen as an optimal solution for the mechanism. Furthermore, the effectiveness of proposed approach is greater than that of the Jaya algorithm and TLBO algorithm through Wilcoxon signed rank test and Friedman test. The proposed approach can be used for related engineering fields.

     

基于免疫遗传算法优化的模糊控制方法及其应用

在模糊控制器的设计过程中,为了使模糊控制器的性能达到全局优化,应用免疫遗传算法对模糊控制器参数进行优化设计;在综合考虑各种参数对控制器性能影响的基础上,给出了一种全面优化隶属度函数、比例因子和量化因子的优化方法;利用了免疫算法能保持个体的多样性和能对学习过程进行引导的特点,对模糊控制器的多个参数同时进行优化,从而显著提高了系统的收敛性、稳定性。应用该方法对数控铣削加工过程的模糊控制器的设计进行了仿真,并与其他方法进行比较和控制实例的验证,表明了该基于免疫遗传算法优化的模糊器能获得更优良的控制性能。     

Sliding mode-like fuzzy logic control with self-tuning the deadzone parameters

In this paper, a fuzzy logic controller (FLC) is designed based on the similarity between the FLC and the sliding mode control (SMC). The proposed scheme provides the sliding mode-like FLC with fast self-tuning the dead-zone parameters (boundary layer thickness) under parameter variations of the controlled system. To show the validity and the effectiveness of the proposed control method, simulations are performed for the position control of a rotary inverted pendulum     

Hybrid learning for interval type-2 fuzzy logic systems based on orthogonal least-squares and back-propagation methods

This paper presents a novel learning methodology based on a hybrid algorithm for interval type-2 fuzzy logic systems. Since only the back-propagation method has been proposed in the literature for the tuning of both the antecedent and the consequent parameters of type-2 fuzzy logic systems, a hybrid learning algorithm has been developed. The hybrid method uses a recursive orthogonal least-squares method for tuning the consequent parameters and the back-propagation method for tuning the antecedent parameters. Systems were tested for three types of inputs: (a) interval singleton, (b) interval type-1 non-singleton, and (c) interval type-2 non-singleton. Experiments were carried out on the application of hybrid interval type-2 fuzzy logic systems for prediction of the scale breaker entry temperature in a real hot strip mill for three different types of coil. The results proved the feasibility of the systems developed here for scale breaker entry temperature prediction. Comparison with type-1 fuzzy logic systems shows that hybrid learning interval type-2 fuzzy logic systems provide improved performance under the conditions tested.     

Reasoning with propositional knowledge based on fuzzy neural logic

In this article, a new kind of reasoning for propositional knowledge, which is based on the fuzzy neural logic initialed by Teh, is introduced. A fundamental theorem is presented showing that any fuzzy neural logic network can be represented by operations: bounded sum, complement, and scalar product. Propositional calculus of fuzzy neural logic is also investigated. Linear programming problems risen from the propositional calculus of fuzzy neural logic show a great advantage in applying fuzzy neural logic to answer imprecise questions in knowledge-based systems. An example is reconsidered here to illustrate the theory. © 1996 John Wiley & Sons, Inc.     

基于模糊逻辑带权重的模糊查询研究*

在已研究的模糊查询中,其逻辑连接均为and或or,具有一定的狭隘性,不符合自然语言表述的特点,也不能充分满足查询要求。引入模糊语言量词作为模糊逻辑连接词,同时引入权重,利用Zadeh 的模糊集合理论与SQL系统函数相结合,研究了基于模糊逻辑连接的带权重的模糊查询技术,进一步丰富了SQL模糊查询体系,提高了查询能力。     

基于小波变换和模糊相似关系的工艺参数优化

根据小波变换模极大值检测奇异点原理,对一个实际的反应器压力波动信号做出稳定性评价,在此基础上,通过分析生产过程中得到的结果数据与生产责任指标之间的模糊相似关系,找到最贴近生产责任指标的生产结果,将其对应的操作参数作为优化工艺参数的依据,这种优化方法简便有效,对其它生产装置有很好的借鉴作用,尤其是那些设计有缺陷却在维持生产的装置.     

Medical diagnosis with the aid of using fuzzy logic and intuitionistic fuzzy logic

The objective of the present study is to develop/establish a web-based medical diagnostic support system (MDSS) by which health care support can be provided for people living in rural areas of a country. In this respect, this research provides a novel approach for medical diagnosis driven by integrating fuzzy and intuitionistic fuzzy (IF) frameworks. Subsequently, based on the proposed approach a web-based MDSS is developed. The proposed MDSS comprises of a knowledge base (KB) and intuitionistic fuzzy inference system (IFIS). Based on the observation that medical data cannot be described with both precision and certainty, a medical KB is constructed in the form of a set of if-then decision rules by employing both fuzzy and IF logics. After constructing the medical KB, a new set of patients is considered for diagnosing the diseases. For each patient, linguistic values of the patients’ symptoms are considered as inputs of the proposed IFIS and modeled by using the generalized triangular membership functions. Subsequently, integrated fuzzy and IF rule-based inference system is used to find a valid conclusion for the new set of patients. In a nutshell, in this paper fuzzy rule-based and IFS based inference systems are combined for better and more realistic representation of uncertainty of the medical diagnosis problem and for more accurate diagnostic result. The method is composed of following four steps: (1) the modeling of antecedent part of the rules, which consist of linguistic assessments of the patients’ symptoms provided by the doctors/medical experts with their corresponding confidence levels, by using generalized fuzzy numbers; (2) the modeling of consequent part, which reveals the degree of association and the degree of non-association of diseases into the patient, by using IFSs; (3) the use of IF aggregation operator in inference process; (4) the application of relative closeness function to find the final crisp output for a given diagnosis. Finally, the applicability of the proposed approach is illustrated with a suitable case study. This article has also justified the proposed approach by using similarity measurement.     

基于NSCT与模糊逻辑的图像融合方法

提出一种基于非下采样Contourlet变换（NSCT）和模糊逻辑的图像融合方法。NSCT分解有利于更好地保持图像的边缘信息和轮廓结构,并增强图像的平移不变性。在对原图像进行多尺度几何分解后,针对图像融合过程中原图像融入信息程度的不确定性,采用基于模糊逻辑（Fuzzy logic）的融合规则指导图像的融合：根据高低频各自的频带特性和待融合图像的特点,对高频使用基于信息熵与模糊逻辑的融合方法,而对低频使用基于亮度、梯度、方差、信息熵等特性及模糊逻辑的规则得到融合系数,最终经过NSCT逆变换得到融合图像。实验结果表明,该算法的融合图像具有良好的视觉效果及客观评指标,指标与视觉效果也能够较好的统一。     

Towards a linear general type-2 fuzzy logic based approach for computing with words

Within the last two decades, the paradigm of Computing With Words (CWW) has been gaining more attention. Mainly, CWW has an exciting vision which tries to tackle the problem of human intelligence by taking the human mind as a role model. The human intelligence has been investigated by various disciplines including psychology, philosophy, neuroscience, linguistics, computer science, and cognitive sciences. Notably, it is not a straightforward task to map the human’s brain reasoning into computer processes. In this paper, we propose to facilitate such mapping by investigating a key element, which is to identify the step-by-step formation of perceptual judgments. Herein, we first introduce an approach that employs general type-2 fuzzy logic to dynamically model the human perceptions based on the human experience. This approach can be regarded as a step to enable the CWW vision. We have deployed the proposed approach in real-world settings and we will present two sets of real-world experiments which were conducted in the intelligent apartment (iSpace) in the University of Essex. The first set of experiments demonstrates the results of the proposed approach for the adaptive modeling of ambient luminance perception. In the second set of experiments, we show that our approach performs better in the rule base evaluation processing time and in output accuracy with comparison to an interval type-2 fuzzy logic system.     

用双枝模糊逻辑和模糊Petri网构建的攻击模型

以双枝模糊逻辑和模糊Petri网（Fuzzy Petri net,FPN）理论为基础,定义了一种全新的网络攻击模型BBFLPAN,将网络攻击中对攻击起促进与抑制作用的两方面进行综合考虑与分析,用变迁表示攻击、防御行为的产生发展过程,库所表示系统所处的状态,将网络攻击与防御行为和攻击与防御结果进行了区分,直观地表示网络攻击的演变情况。同时结合双枝模糊逻辑,分析了BBFLPAN模型的基本推理规则,并提出了BBFLPAN的推理算法,并通过实验验证了算法的正确性。将对网络攻击实施起正反两方面的因素一起考虑和分析,使其对网络攻击的描述更加切近实际情况。     

A fuzzy logic based approach to the SLAM problem using pseudolinear models with multiframe data association

This paper presents an alternative solution to simultaneous localization and mapping (SLAM) problem by applying a fuzzy Kalman filter using pseudolinear process and measurement models. Nonlinear process model and observation model are formulated as pseudo-linear models and rewritten with a composite model whose local models are linear according to Takagi-Sugeno (T-S) fuzzy model. Using the Kalman filter theory, each local T-S model is filtered to find the local estimates. The linear combination of these local estimates gives the global estimate for the complete system. Data association to correspond features to the observed measurement is proposed with two sensor frames obtained from two sensors. The above system is implemented and simulated with Matlab to claim that the proposed method yet finds a better solution to the SLAM problem, though nonlinearity is directly involved in the Kalman filter equations, compared to the conventional approach. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Tolerance allocation based on fuzzy logic and simulated annealing

This paper presents a new technique for dealing with tolerance allocation problems, which can be encountered in the design process of mechanical engineering practice. This technique uses a combination of fuzzy logic to evaluate manufacturing difficulties and a simulated annealing algorithm to optimize tolerance allocation over the complete dimensioning chain.