Extrapolation of fuzzy values from incomplete data bases

This paper presents different approaches which enable a data base management system to obtain a plausible fuzzy estimate for an attribute value of an item for which the information is not explicitly stored in the data base. This can be made either by a kind of analogical reasoning from information about particular items or by means of expert rules which specify the (fuzzy) sets of possible values of the attribute under consideration, for various classes of items. Another kind of expert rules enables the system to compute an estimate from the attribute value of another item provided that, in other respects, this latter item sufficiently resembles the item, the value of which we are interested in; then these expert rules are used either for controlling the analogical reasoning process or for enlarging the scope of application of the first kind of expert rules. The different approaches are discussed in the framework of possibility theory.     

Issues in nonlinear stochastic grey box identification

Grey box identification refers to the practice of identifying dynamical systems in model structures exploiting partial prior information. This contribution reviews a method for stochastic grey box identification and surveys experiences and lessons of applying it to a number of industrial processes. Issues to be addressed include advantages and costs of introducing stochastics into the model, the question of what contribution must be expected from the model designer as opposed to what can be formalized in computer algorithms, and an outlook on future plans to resolve present shortcomings.     

A knowledge-based system for sensor selection

This paper presents a Knowledge-based system (KBS) developed to allow users, who may not be knowledgeable about sensors, to select sensors suitable for their specific needs. The KBS runs on a micro-computer. The selection criteria are user specified and are based on the desired measurement parameters. The system output includes all of the operational and dimensional parameters of the recommended sensor, price, and vendor information.     

Sensorless control of synchronous reluctance motors using on‐line parameter identification method not affected by position estimation accuracy

This paper presents a novel on‐line parameter identification method for sensorless control of Synchronous Reluctance Motors (SynRMs). Although conventional sensorless control methods based on mathematical models usually need some complex measurements of motor parameters in advance, the proposed identification method does not require them and can be realized on‐line. The proposed method identifies motor parameters under sensorless control, so rotor position and velocity cannot be used to identify these parameters. However, the proposed method does not need rotor position and velocity, and identified parameters are not affected by these estimation errors. The sensorless control using identified motor parameters is realized, and the effectiveness of the proposed method is verified by experimental results. © 2006 Wiley Periodicals, Inc. Electr Eng Jpn, 155(3): 62–69, 2006; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20258     

贵冶动力中心二期反渗透系统改造

本文阐述对贵冶动力车间二期反渗透进行改造的成功实践 ,主要从反渗透系统设计、反渗透系统中容易出现的问题及解决方法等进行介绍。     

首尔地面公交系统改革的成功经验

随着经济、人口的快速发展,首尔开展了一次针对地面公交系统的成功而深刻的变革。其中包括组建以公众参与为基础的公交系统改革公民委员会,形成了“以线路连接为导向的公交系统”,采用新型公交智能卡、票价新制度以及信息一体化的管理系统。     

Performance analysis of the forgetting factor RLS algorithm

An analysis is given of the performance of the standard forgetting factor recursive least squares (RLS) algorithm when used for tracking time-varying linear regression models. Three basic results are obtained: (1) the ‘P-matrix’ in the algorithm remains bounded if and only if the (time-varying) covariance matrix of the regressors is uniformly non-singular; (2) if so, the parameter tracking error covariance matrix is of the order O(μ + γ²/μ), where μ = 1 - λ, λ is the forgetting factor and γ is a quantity reflecting the speed of the parameter variations; (3) this covariance matrix can be arbitrarily well approximated (for small enough μ) by an expression that is easy to compute.     

A weighted multivariate Fuzzy C-Means method in interval-valued scientific production data

Clustering is the process of organizing objects into groups whose members are similar in some way. Most of the clustering methods involve numeric data only. However, this representation may not be adequate to model complex information which may be: histogram, distributions, intervals. To deal with these types of data, Symbolic Data Analysis (SDA) was developed. In multivariate data analysis, it is common some variables be more or less relevant than others and less relevant variables can mask the cluster structure. This work proposes a clustering method based on fuzzy approach that produces weighted multivariate memberships for interval-valued data. These memberships can change at each iteration of the algorithm and they are different from one variable to another and from one cluster to another. Furthermore, there is a different relevance weight associated to each variable that may also be different from one cluster to another. The advantage of this method is that it is robust to ambiguous cluster membership assignment since weights represent how important the different variables are to the clusters. Experiments are performed with synthetic data sets to compare the performance of the proposed method against other methods already established by the clustering literature. Also, an application with interval-valued scientific production data is presented in this work. Clustering quality results have shown that the proposed method offers higher accuracy when variables have different variabilities.