Identification and optimization of ammonia reactors through hybrid simulation

A hybrid simulation technique for identification and steady state optimization of a tubular reactor used in ammonia synthesis is presented. The parameter identification program finds the catalyst activity factor and certain heat transfer coefficients that minimize the sum of squares of deviation from simulated and actual temperature measurements obtained from an operating plant. The optimization program finds the values of three flows to the reactor to maximize the ammonia yield using the estimated parameter values. Powell's direct method of optimization is used in both cases. The results obtained here are compared with the plant data.

Convergence problems in a class of model reference adaptive control systems

A class of model reference adaptive control system which make use of an augmented error signal has been introduced by Monopoli. Convergence problems in this attractive class of systems have been investigated in this paper using concepts from hyperstability theory. It is shown that the condition on the linear part of the system has to be stronger than the one given earlier. A boundedness condition on the input to the linear part of the system has been taken into account in the analysis - this condition appears to have been missed in the previous applications of hyperstability theory. Sufficient conditions for the convergence of the adaptive gain to the desired value are also given.

A Refined Methodology for Durability-Based Service Life Estimation of Reinforced Concrete Structural Elements Considering Fuzzy and Random Uncertainties

A reliable method for service life estimation of the structural element is a prerequisite for service life design. A new methodology for durability-based service life estimation of reinforced concrete flexural elements with respect to chloride-induced corrosion of reinforcement is proposed. The methodology takes into consideration the fuzzy and random uncertainties associated with the variables involved in service life estimation by using a hybrid method combining the vertex method of fuzzy set theory with Monte Carlo simulation technique. It is also shown how to determine the bounds for characteristic value of failure probability from the resulting fuzzy set for failure probability with minimal computational effort. Using the methodology, the bounds for the characteristic value of failure probability for a reinforced concrete T-beam bridge girder has been determined. The service life of the structural element is determined by comparing the upper bound of characteristic value of failure probability with the target failure probability. The methodology will be useful for durability-based service life design and also for making decisions regarding in-service inspections.

Fuzzy Expert Approach with Curtailed Controllers for Improved Reactive Power Dispatch

This paper addresses the problem of curtailing the number of control actions using fuzzy expert approach for voltage/reactive power dispatch. It presents an approach using fuzzy set theory for reactive power control with the purpose of improving the voltage profile of a power system. To minimize the voltage deviations from pre-desired values of all the load buses, using the sensitivities with respect to reactive power control variables form the basis of the proposed Fuzzy Logic Control (FLC). Control variables considered are switchable VAR compensators, On Load Tap Changing (OLTC) transformers and generator excitations. Voltage deviations and controlling variables are translated into fuzzy set notations to formulate the relation between voltage deviations and controlling ability of controlling devices. The developed fuzzy system is tested on a few simulated practical Indian power systems and modified IEEE-30 bus system. The performance of the fuzzy system is compared with conventional optimization technique and results obtained are encouraging. Results obtained for a modified IEEE-30 bus test system and a 205-node equivalent EHV system a part of Indian southern grid are presented for illustration purposes. The proposed fuzzy-expert technique is found suitable for on-line applications in energy control centre as the solution is obtained fast with significant speedups with few number of controllers.

El laberinto del continuo

Resumen: La expresión “Laberinto del continuo” se debe a Leibniz. Sin embargo, Leibniz carecía de los instrumentos conceptuales necesarios para tratar el tema adecuadamente. La teoría de conjuntos de Cantor hizo posible resolver el problema del continuo de modo satisfactorio desde el punto de vista conjuntista. Se debe atribuir a R. Dedekind una resolución más cabal del problema. No obstante, el autor sostiene que la concepción de éste último tampoco pone de manifiesto todos los aspectos involucrados en el continuo.

Análise de desempenho dos algoritmos Apriori e Fuzzy Apriori na extração de regras de associação aplicados a um Sistema de Detecção de Intrusos.

A extração de regras de associação (ARM - Association Rule Mining) de dados quantitativos tem sido pesquisa de grande interesse na área de mineração de dados. Com o crescente aumento das bases de dados, há um grande investimento na área de pesquisa na criação de algoritmos para melhorar o desempenho relacionado a quantidade de regras, sua relevância e a performance computacional. O algoritmo APRIORI, tradicionalmente usado na extração de regras de associação, foi criado originalmente para trabalhar com atributos categóricos. Geralmente, para usá-lo com atributos contínuos, ou quantitativos, é necessário transformar os atributos contínuos, discretizando-os e, portanto, criando categorias a partir dos intervalos discretos. Os métodos mais tradicionais de discretização produzem intervalos com fronteiras sharp, que podem subestimar ou superestimar elementos próximos dos limites das partições, e portanto levar a uma representação imprecisa de semântica. Uma maneira de tratar este problema é criar partições soft, com limites suavizados. Neste trabalho é utilizada uma partição fuzzy das variáveis contínuas, que baseia-se na teoria dos conjuntos fuzzy e transforma os atributos quantitativos em partições de termos linguísticos. Os algoritmos de mineração de regras de associação fuzzy (FARM - Fuzzy Association Rule Mining) trabalham com este princípio e, neste trabalho, o algoritmo FUZZYAPRIORI, que pertence a esta categoria, é utilizado. As regras extraídas são expressas em termos linguísticos, o que é mais natural e interpretável pelo raciocício humano. Os algoritmos APRIORI tradicional e FUZZYAPRIORI são comparado, através de classificadores associativos, baseados em regras extraídas por estes algoritmos. Estes classificadores foram aplicados em uma base de dados relativa a registros de conexões TCP/IP que destina-se à criação de um Sistema de Detecção de Intrusos.

Coalition and distribution in fuzzy dynamic supply chain systems

In this paper, two models of coalition and income's distribution in FSCS (fuzzy supply chain systems) are proposed based on the fuzzy set theory and fuzzy cooperative game theory. The fuzzy dynamic coalition choice's recursive equations are constructed in terms of sup-t composition of fuzzy relations, where t is a triangular norm. The existence of the fuzzy relations in FSCS is also proved. On the other hand, the approaches to ascertain the fuzzy coalition through the choice's recursive equations and distribute the fuzzy income in FSCS by the fuzzy Shapley values are also given. These models are discussed in two parts: the fuzzy dynamic coalition choice of different units in FSCS; the fuzzy income's distribution model among different participators in the same coalition. Furthermore, numerical examples are given aiming at illustrating these models., and the results show that these models are feasible and validity in FSCS.

一种基于模糊集合论的手势分割算法

提出了一种基于单目的复杂环境下强抗干扰性的手势分割算法,使用模糊集合的概念来描述视频流时域和空域上的不同信息,以模糊运算作为信息加工处理的工具。定义了三个模糊集合非背景集、肤色集和模糊手势集,讨论了对模糊集合的腐蚀和膨胀运算。通过对非背景集和肤色集进行模糊运算,得到原始的模糊手势集,然后对原始的模糊手势集进行求精处理。试验结果证明,该文算法实现了对人手的精确分割,且能满足实时性要求。

a fuzzy-based method for evaluating the trustworthiness of software processes

University of Paderborn; Fraunhofer Inst. Exp. Softw. Eng. (IESE); Chinese Academy of Science (ISCAS)

基于粗糙集和多Agent系统的知识挖掘

通过优化知识表达系统中条件属性对决策属性的依赖度,深入研究了粗糙集并与多Agent系统相结合。利用离散粒子群算法,提出一种基于粒子群优化的粗糙集知识约简算法,该算法解决了启发式算法无法全局搜索进行约简的问题。最后通过在矿井中调度信息的应用验证了有效性。

基于粗糙集-BP神经网络的发动机故障诊断

由于发动机光谱分析监控数据中磨损微粒种类过多,如果将这些微粒信息直接作为神经网络的输入,则存在输入层神经元过多、网络结构复杂等诸多问题。本文将粗糙集引入到发动机故障诊断中来,利用粗糙集在属性约简方面的优势,删除冗余磨损微粒,提取出重要磨损微粒,并将其作为BP神经网络的输入,建立发动机故障诊断模型。该方法降低输入层的神经元个数,简化了网络结构,缩短网络训练时间,并且由于剔除了冗余磨损微粒,减少了由该部分微粒信息不准确而带来的误差,有效提高了故障诊断的精确度。最后通过算例分析验证了相关算法和诊断模型的准确性和有效性。

Knowledge representation for fuzzy model composition

X. Fu and Q. Shen. 'Knowledge representation for fuzzy model composition', in Proceedings of the 21st International Workshop on Qualitative Reasoning, 2007, pp. 47-54. Sponsorship: EPSRC

Efficient Support for Common Relations in Lightweight Formal Reasoning Systems

In work that involves mathematical rigor, there are numerous benefits to adopting a representation of models and arguments that can be supplied to a formal reasoning or verification system: reusability, automatic evaluation of examples, and verification of consistency and correctness. However, accessibility has not been a priority in the design of formal verification tools that can provide these benefits. In earlier work [Lap09a], we attempt to address this broad problem by proposing several specific design criteria organized around the notion of a natural context: the sphere of awareness a working human user maintains of the relevant constructs, arguments, experiences, and background materials necessary to accomplish the task at hand. This work expands one aspect of the earlier work by considering more extensively an essential capability for any formal reasoning system whose design is oriented around simulating the natural context: native support for a collection of mathematical relations that deal with common constructs in arithmetic and set theory. We provide a formal definition for a context of relations that can be used to both validate and assist formal reasoning activities. We provide a proof that any algorithm that implements this formal structure faithfully will necessary converge. Finally, we consider the efficiency of an implementation of this formal structure that leverages modular implementations of well-known data structures: balanced search trees and transitive closures of hypergraphs.

Fuzzy ART: An Adaptive Resonance Algorithm for Rapid, Stable Classification of Analog Patterns

The Fuzzy ART system introduced herein incorporates computations from fuzzy set theory into ART 1. For example, the intersection (n) operator used in ART 1 learning is replaced by the MIN operator (A) of fuzzy set theory. Fuzzy ART reduces to ART 1 in response to binary input vectors, but can also learn stable categories in response to analog input vectors. In particular, the MIN operator reduces to the intersection operator in the binary case. Learning is stable because all adaptive weights can only decrease in time. A preprocessing step, called complement coding, uses on-cell and off-cell responses to prevent category proliferation. Complement coding normalizes input vectors while preserving the amplitudes of individual feature activations.

Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System

A Fuzzy ART model capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns is described. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns. The generalization to learning both analog and binary input patterns is achieved by replacing appearances of the intersection operator (n) in AHT 1 by the MIN operator (Λ) of fuzzy set theory. The MIN operator reduces to the intersection operator in the binary case. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy set theory play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Learning stops when the input space is covered by boxes. With fast learning and a finite input set of arbitrary size and composition, learning stabilizes after just one presentation of each input pattern. A fast-commit slow-recode option combines fast learning with a forgetting rule that buffers system memory against noise. Using this option, rare events can be rapidly learned, yet previously learned memories are not rapidly erased in response to statistically unreliable input fluctuations.