A neuro fuzzy logic approach to material processing

A new application of fuzzy systems to the processing of materials is presented. The relationships between temperature, time, and the impact strength of an austempered ductile iron (ADI) part are adaptively modeled. Four fuzzy and neuro fuzzy approaches have been used to build predictive models. These are: a fuzzy based model, a backpropagation based neuro fuzzy model, a clustering based model, and a clustering backpropagation based neuro fuzzy model. The clustering approach, using the subclustering method, yielded the best predictive results when all models had been given the same input-output training data. The backpropagation based neuro fuzzy approach suffers from the lack of a higher number of input-output data training sets. All preliminary results obtained suggest the adequacy of the fuzzy based and neuro fuzzy based modeling techniques to tackle those types of problems in the material processing areas     

Backing control problem of a mobile robot with multiple trailers:fuzzy modeling and LMI-based design

The paper presents backing control of computer simulated mobile robots with multiple trailers by fuzzy modeling and control. We deal with two kinds of mobile robots: a mobile robot with five trailers and a mobile robot with ten trailers. To design fuzzy controllers, nonlinear models of the mobile robots with multiple trailers are represented by Takagi-Sugeno fuzzy models (TS fuzzy model). Before making TS fuzzy models, we simplify the nonlinear dynamics of the mobile robots. Under an assumption, TS fuzzy models are made from the simplified nonlinear models. The so-called parallel distributed compensation (PDC) is employed to design fuzzy controllers from the TS fuzzy models. Next, we derive a stability condition based on the Lyapunov approach. The stability condition of the designed fuzzy control system is cast in terms of linear matrix inequalities (LMI's) since it is reduced to a problem of finding a common Lyapunov function for a set of Lyapunov inequalities. Convex optimization techniques based on LMI's are utilized to solve the problem of finding stable feedback gains and a common Lyapunov function for the designed fuzzy control system. The simulation results show the effects of the fuzzy modeling, the controller design via the PDC, and the stability analysis based on LMIs     

RBF模糊神经网络用于NIR鉴别羊绒和羊毛的可行性研究

为了实现羊绒、羊毛纤维的快速、无损检测,建立了羊绒、羊毛近红外光谱数据库,包括228组各地羊绒、羊毛数据,并应用于羊绒、羊毛的定性检测上。首先介绍了羊绒、羊毛近红外光谱检测的数据库建立过程;然后,在对羊绒、羊毛原始近红外光谱进行预处理的基础上,对数据进行主成分分析,选出12种主成分,并结合改进的RBF模糊神经网络,建立羊绒、羊毛检测模型。通过与主成分分析-马氏距离建模方法的对比分析实验表明,建立近红外光谱数据库,并结合主成分分析和改进的RBF模糊神经网络的方法是一种有效的无损检测羊绒、羊毛的方法,可快速建立高精度的羊绒、羊毛纤维检测模型。     

Generating optimal adaptive fuzzy-neural models of dynamicalsystems with applications to control

The paper describes an approach to generating optimal adaptive fuzzy neural models from I/O data. This approach combines structure and parameter identification of Takagi-Sugeno-Kang (TSK) fuzzy models. We propose to achieve structure determination via a combination of modified mountain clustering (MMC) algorithm, recursive least squares estimation (RLSE), and group method of data handling (GMDH). Parameter adjustment is achieved by training the initial TSK model using the algorithm of an adaptive network based fuzzy inference system (ANFIS), which employs backpropagation (BP) and RLSE. Further, a procedure for generating locally optimal model structures is suggested. The structure optimization procedure is composed of two phases: 1) locally optimal rule premise variables subsets (LOPVS) are identified using MMC, GMDH, and a search tree (ST); and 2) locally optimal numbers of model rules (LONOR) are determined using MMC/RLSE along with parallel simulation mean square error (PSMSE) as a performance index. The effectiveness of the proposed approach is verified by a variety of simulation examples. The examples include modeling of a nonlinear dynamical process from I/O data and modeling nonlinear components of dynamical plants, followed by tracking control based on a model reference adaptive scheme (MRAC). Simulation results show that this approach is fast and accurate and leads to several optimal models     

A granular description of ECG signals

In this paper, we develop a general framework of a granular representation of ECG signals. The crux of the approach lies in the development and ongoing processing realized in the setting of information granules-fuzzy sets. They serve as basic conceptual and semantically meaningful entities using which we describe signals and build their models (such as various predictive schemes or classifiers). A comprehensive two-phase scheme of the design of the information granules is proposed and described. At the first phase, we discuss the temporal granulation through a series of temporal windows (granular windows) and an aggregation of the values of signal by means of fuzzy sets. To address this issue, offered is a detailed method of building a fuzzy set based on numeric data and a certain optimization criterion that strikes a balance between the highest experimental relevance of the fuzzy set supported by numeric data and its substantial specificity. At the next phase of the granular design, a collection of information granules is further summarized with the use of fuzzy clustering (Fuzzy C-Means). The resulting prototypes (centroids) formed by this grouping process serve as elements of the granular vocabulary. We discuss ways of using these vocabularies in the knowledge-based representation, modeling, and classification of ECG beats.     

ARIADNE: A constraint-based approach to computer-aided synthesis and modeling of Analog integrated circuits

The ARIADNE approach to computer-aided synthesis and modeling of analog circuits is presented. It is a mathematical approach based on the use of equations. Equations are regarded as constraints on a circuit's design space and analog circuit design is modeled as a constraint satisfaction problem. To generate and efficiently satisfy constraints, advanced computational techniques such as constraint propagation, interval propagation, symbolic simulation, and qualitative simulation are applied. These techniques cover design problems such as topology construction, modeling, nominal analysis, tolerance analysis, sizing and optimization of analog circuits. The advantage of this approach is the clear separation of design knowledge from design procedures. Design knowledge is modeled in declarative equation-based models (DEBMs). Design procedures are implemented into general applicable CAD tools. The ARIADNE approach closely matches the reasoning style applied by experienced designers. The integration of synthesis and modeling into one frame and the clear separation of design knowledge from design procedures eases the process of extending the synthesis system with new circuit topologies, turning it into an open design system. This system can be used by both inexperienced and experienced designers in either interactive or automated mode.     

自适应图像对比度模糊增强算法

文中提出了一种基于模糊集理论和广义模糊算子GFO的图像模糊对比度增强算法。由算法分析可知,利用模糊隶属度可以对图像的细节进行增强,图像的层次更加分明。通过对几例图像增强对比实验结果的分析,表明该算法是有效的。     

Qualitative Analysis in Reliability & Safety Studies

The qualitative evaluation of system logic models is described as it pertains to assessing the reliability and safety characteristics of nuclear systems. Qualitative analysis of system logic models, i.e., models couched in an event (Boolean) algebra, is defined, and the advantages inherent in qualitative analysis are explained. Certain qualitative procedures that were developed as a part of fault-tree analysis are presented for illustration. Five fault-tree analysis computer-programs that contain a qualitative procedure for determining minimal cut sets are surveyed. For each program (SETS, MOCUS, PREP, MICSUP, ELRAFT), the minimal cut-set algorithm and limitations on its use are described. The recently developed common-cause analysis for studying the effect of common-causes of failure on system behavior is explained. This qualitative procedure does not require altering the fault tree, but does use minimal cut sets from the fault tree as part of its input. The method is applied using two different computer programs, COMCAN and SETS.     

Development process assessment toward leading edge quality

The development process assessments conducted at the Westlake Programming Lab (WPL) are described and the results summarized. An approach for merging and prioritizing the multiple assessment data, based on the Yager (1981) method of decision making using fuzzy sets, is described. A matrix that identifies action items in the unified action plan is presented. Finally, a simplified example is used to illustrate the techniques     

A spatial correlation based method for neighbor set selection inrandom field image models

Random field (RF) models have widespread application in image modeling and analysis. The effectiveness of these models is largely dependent on the choice of neighbor sets, which determine the spatial interactions that are representable by the model. We consider the problem of selecting these neighbor sets for simultaneous autoregressive and Gauss-Markov random field models, based on the correlation structure of the image to be modeled. A procedure for identifying appropriate neighbor sets is proposed, and experimental results which demonstrate the viability of this method are presented.     

A CVD epitaxial deposition in a vertical barrel reactor: processmodeling using cluster-based fuzzy logic models

A chemical vapor deposition (CVD) epitaxial deposition process modeling using fuzzy logic models (FLM's) has been proposed. The process modeling algorithm consists of a cluster estimation method and backpropagation algorithm to construct a number of modeling structures from the training data. A decision rule based on the multiple correlation factor is used to obtain the optimum structure of the fuzzy model using the testing data. Upon the optimum structure being reached, the gradient-descent method is used to refer the parameters of the final fuzzy model using both training and testing data. The algorithm has been applied to a nonlinear function and a vertical chemical vapor deposition process. The results demonstrate the efficiency and effectiveness of the proposed fuzzy logic model in comparison with existing fuzzy logic models and artificial neural network models     

Computational geometry for patient-specific reconstruction and meshing of blood vessels from MR and CT angiography

Investigation of three-dimensional (3-D) geometry and fluid-dynamics in human arteries is an important issue in vascular disease characterization and assessment. Thanks to recent advances in magnetic resonance (MR) and computed tomography (CT), it is now possible to address the problem of patient-specific modeling of blood vessels, in order to take into account interindividual anatomic variability of vasculature. Generation of models suitable for computational fluid dynamics is still commonly performed by semiautomatic procedures, in general based on operator-dependent tasks, which cannot be easily extended to a significant number of clinical cases. In this paper, we overcome these limitations making use of computational geometry techniques. In particular, 3-D modeling was carried out by means of 3-D level sets approach. Model editing was also implemented ensuring harmonic mean curvature vectors distribution on the surface, and model geometric analysis was performed with a novel approach, based on solving Eikonal equation on Voronoi diagram. This approach provides calculation of central paths, maximum inscribed sphere estimation and geometric characterization of the surface. Generation of adaptive-thickness boundary layer finite elements is finally presented. The use of the techniques presented here makes it possible to introduce patient-specific modeling of blood vessels at clinical level.     

二维模糊划分最大熵图像分割算法

该文提出了一种通过最大化二维直方图模糊划分熵分割灰度图像的新算法。首先介绍了模糊划分的原理,提出用条件概率与条件熵定义模糊划分熵。随后利用多维三角模定义了非相关模糊子集的广义直积,给出构造多维模糊划分的方法,并根据最大熵原理设计了一种基于二维直方图模糊划分熵分割灰度图像的新算法。对几例真实目标图像的对比分割实验结果表明该文方法性能优越。     

The role of qualitative knowledge in the formulation ofcompartmental models

Compartmental modeling is an approach to dynamic systems analysis that has proven useful in enhancing the understanding of biological and ecological systems. The compartmental approach emphasizes model conceptualization and is especially appropriate in applications in which quantitative behavioral data are difficult or expensive to obtain and in which qualitative understanding is the primary goal. In these applications, models are often developed by interdisciplinary teams. Domain experts contribute their understanding of component behaviors and interactions, while systems engineers guide the modeling process and insure the rigor of subsequent model-based analyses. The success of these applications clearly demands efficient and effective methods for transferring qualitative knowledge across disciplinary boundaries. The paper examines the expanded roles of knowledge acquisition, verification, and interpretation in the formulation of compartmental models. The objective is to illustrate that knowledge engineering, as currently practiced in the development of expert systems, is an essential ingredient in the compartmental approach to dynamic systems     

Multiple fault analog circuit testing by sensitivity analysis

Analog circuit testing is considered to be a very difficult task. This difficulty is mainly due to the lack of fault models and accessibility to internal nodes. To overcome this problem, an approach is presented for analog circuit modeling and testing. The circuit modeling is based on first-order sensitivity computation. The testability of the circuit is analyzed by the multiple-fault model and by functional testing. Component deviations are deduced by measuring a number of output parameters, and through sensitivity analysis and tolerance computation. Using this approach, adequate tests are identified for testing catastrophic and soft faults. Some experimental results are presented for simple models as well as multiple-fault models.     

Fuzzy repairable reliability based on fuzzy gert

Military equipment and weapon systems have become more advanced, precise and complex. Requirements of threat and readiness have been raised. Nowadays, the advance of weapon systems and their logistic support places the emphasis on the life cycle in the initial design. However, reliability analysis is the main work of logistic engineering. Its aim is to develop the best design for weapon systems operating in a special operation environment. Accordingly, there are many factors to consider in the reliability of weapon systems. Generally, those factors are of an uncertain nature. Traditionally, we use probability theory to treat the reliability of a weapon system. The probabilistic approach can only represent the randomness of a success or failure event, and requires complete data and predetermined conditions. Fuzzy sets theory can efficiently treat the above characteristics and shortcomings. Therefore we will propose a method and technology of fuzzy system reliability to solve the above problems.In this paper we first formulate the building membership functions of component reliability based on the α-cut method. Secondly, when the membership functions of the components are built, we can propose some fuzzy mathematic models for solving fuzzy system reliability. Different models and approaches have been studied and proposed in this research. In an unrepairable system, we have built two methods. In a repairable system, we will propose a fuzzy GERT (graphical evaluation and review technique) method to calculate the fuzzy reliability. For a simple and efficient computation, we have developed systematic and practical algorithms to calculate and analyze fuzzy system reliability. We have also presented an example of a military operation mission to demonstrate our proposed methods.     

Multiple fault analog circuit testing by sensitivity analysis

Analog circuit testing is considered to be a very difficult task. This difficulty is mainly due to the lack of fault models and accessibility to internal nodes. To overcome this problem, an approach is presented for analog circuit modeling and testing. The circuit modeling is based on first-order sensitivity computation. The testability of the circuit is analyzed by the multiple-fault model and by functional testing. Component deviations are deduced by measuring a number of output parameters, and through sensitivity analysis and tolerance computation. Using this approach, adequate tests are identified for testing catastrophic and soft faults. Some experimental results are presented for simple models as well as multiple-fault models.     

Evolving Fuzzy Modeling for MANETs Using Lightweight Online Unsupervised Learning

The study presents a methodology for evolving fuzzy modeling tasks in Mobile Ad hoc Networks (MANETs) based on distributed data-driven fuzzy clustering and reasoning. The fuzzy clustering is exploited for the purpose of learning fuzzy inference rules online. That calls for one-pass Lightweight Evolving Fuzzy Clustering Method (LEFCM) suitable for deploying on mobile devices with constrained resources in MANETs. There is no standard method to determine the optimal number of fuzzy rules and most of the fuzzy systems still apply the trial and error method, unsuitable for online modeling tasks. The proposed methodology addresses the issues of uncertainties, simplicity and speed to run in non-intrusive way. It estimates online the number of clusters and their centers in the input data space, accordingly the fuzzy rules, by online adaptation of the LEFCM threshold value that affects the number of clusters. Adaptation is based on the combination of geometrical and statistical analyses, as well as on incorporating a multidimensional fuzzy membership degree into the clustering process. The proposed LEFCM is proven by using traditional cluster validity indexes and tested on real data sets.     

Mismatch Modeling and Simulation—A Comprehensive Approach

This article describes a comprehensive approach to mismatch simulation and modeling as needed for integrated circuit design. Local device mismatch as well as global process variations and parameter correlations are regarded. A method for mismatch modeling based on spatial frequencies is described, which enables to overcome insufficiencies of the first order models. Measurement results are presented to demonstrate the achieved modeling precision. All models and methods mentioned here are commercially available in the simulation tool GAME (General Analysis of Mismatch Effects) which is used in the semiconductor industry since 1998.