一种基于模糊模型的启发式优化方法

在模糊环境下的优化向来是模糊系统理论的中心课题之一。文献中所见的绝大多数模糊系统优化技术都以线性规划为基础。本文提出了一种完全不同的模糊系统试化方法——启发式试化方法。这种方法以系统的模糊模型为基础,它允许以模糊形式表达的性能指标与约束。试化的结果,亦即最优决策,既可以是模糊值,也可以是精确值。由于所使用的模糊模型所以看作是一个规则集,所提出的试化方法便所以视作一个“剪枝”过程。一个数值例子说明了这种试化方法的有效性。     

Supervisory control design based on hybrid systems and fuzzy events detection. Application to an oxichlorination reactor

This paper presents a supervisory control scheme based on hybrid systems theory and fuzzy events detection. The fuzzy event detector is a linguistic model, which synthesizes complex relations between process variables and process events incorporating experts' knowledge about the process operation. This kind of detection allows the anticipation of appropriate control actions, which depend upon the selected membership functions used to characterize the process under scrutiny. The proposed supervisory control scheme was successfully implemented for an oxichlorination reactor in a vinyl monomer plant. This implementation has allowed improvement of reactor stability and reduction of raw material consumption.     

模糊控制在退火炉炉温控制中的应用

根据退火炉炉温控制的特点,设计并比较了传统PID控制器、模糊PI控制器、史密斯－模糊控制器和参数自校正模糊控制器的性能及控制效果,最后确定了最适于该系统的模糊－PI控制器。     

电厂锅炉液位监控系统

介绍了电厂锅炉液位与流量的监控方法和系统的特点,给出了它的硬件和软件,针对中小型电厂锅炉运行过程中的液位与流量要求,研究采用计算机与模糊控制技术,有效地保证了运行过程对液位与流量变化的要求。     

Optimal design of radial basis function neural networks for fuzzy-rule extraction in high dimensional data

The design of an optimal radial basis function neural network (RBFNF) is not a straightforward procedure. In this paper we take advantage of the functional equivalence between RBFN and fuzzy inference systems to propose a novel efficient approach to RBFN design for fuzzy rule extraction. The method is based on advanced fuzzy clustering techniques. Solutions to practical problems are proposed. By combining these different solutions, a general methodology is derived. The efficiency of our method is demonstrated on challenging synthetic and real world data sets.     

Fuzzy GMDH-type neural network model and its application to forecasting of mobile communication

In this paper, the fuzzy group method data handling-type (GMDH) neural networks and their application to the forecasting of mobile communication systems are described. At present, the GMDH family of modeling algorithms discovers the structure of empirical models and it gives only the way to get the most accurate identification and demand forecasts in case of noised and short input sampling. In distinction to neural networks, the results are explicit mathematical models, obtained in a relative short time. In this paper, an adaptive learning network is proposed as a kind of neural-fuzzy GMDH. The proposed method can be reinterpreted as a multi-stage fuzzy decision rule which is called the neural-fuzzy GMDH. The GMDH-type neural networks have several advantages compared with conventional multi-layered GMDH models. Therefore, many types of nonlinear systems can be automatically modeled by using the neuro-fuzzy GMDH. A computer program is developed and successful applications are shown in the field of estimating problems of mobile communication with a number of factors considered.     

A genetic-fuzzy-neuro model encodes FNNs using SWRM and BRM

Genetic algorithms (GAs), fuzzy logic (FL), and neural networks (NNs) are frequently used artificial intelligence (AI) techniques. Since these three methods are complementary rather than competitive, many researchers have hybridized GAs, FL, and NNs to develop a better performance model. However, most hybrid models use a multistage combination or identify partial parameters required in the model resulting in sub-optimal solutions. This research fuses GAs, FL, and NNs to develop an evolutionary fuzzy neural inference model (EFNIM) that uses GAs to simultaneously search for all parameters required in fuzzy neural networks (FNNs). Two approaches, summit and width representation method (SWRM) and block-representation method (BRM), are proposed to encode variables in FL and NNs. Simulations are conducted to evaluate the performance of EFNIM. For different problems, membership functions (MFs) with the minimum FNN structure and optimal parameters of FNN are automatically and concurrently acquired using EFNIM. The research overcomes the difficulties faced in applying FL and NNs as well as saves efforts in trial-and-error experiments, questionnaire survey, interviews with experts, etc. Both prediction accuracy and time requirement for cost estimating are much improved by the proposed method.     

Coherence measures and their relation to fuzzy similarity and inconsistency in knowledge bases

Intuitively it seems that the coherence of information received from heterogeneous sources should be one factor in determining the reliability or truthfulness of the information, yet the concept of coherence is extremely difficult to define. This paper draws on recent work on probabilistic measures of coherence by investigating two measures with contrasting properties and then explores how this work relates to similarity of fuzzy sets and comparison of knowledge bases in cases where inconsistency is present. In each area contrasting measures are proposed analogous to the probabilistic case. In particular, concepts of fuzzy and logical independence are proposed and in each area it is found that sensitivity to the relevant concept of independence is a distinguishing feature between the contrasting measures. In the case of inconsistent knowledge bases, it is argued that it is important to take agreeing information and not just conflicting and total information into account when comparing two knowledge bases. One of the measures proposed achieves this and is shown to have a number of properties which enable it to overcome some problems encountered by other approaches.     

A new approach for estimating null value in relational database

In general, a database system will not operate properly if it exist some null values of attributes in the system. In this paper, we propose a new approach to estimate null values in relational database, which utilize other clustering algorithm to cluster data, and use fuzzy correlation and distance similarity to calculate the correlation of different attribute. For verifying our method, this paper utilize mean of absolute error rate (MAER) as evaluation criterion to compare with other methods; it is shown that our proposed method proves importance than the existing methods for estimating null values in relational database systems.     

Fuzzy clusterwise linear regression analysis with symmetrical fuzzy output variable

The traditional regression analysis is usually applied to homogeneous observations. However, there are several real situations where the observations are not homogeneous. In these cases, by utilizing the traditional regression, we have a loss of performance in fitting terms. Then, for improving the goodness of fit, it is more suitable to apply the so-called clusterwise regression analysis. The aim of clusterwise linear regression analysis is to embed the techniques of clustering into regression analysis. In this way, the clustering methods are utilized for overcoming the heterogeneity problem in regression analysis. Furthermore, by integrating cluster analysis into the regression framework, the regression parameters (regression analysis) and membership degrees (cluster analysis) can be estimated simultaneously by optimizing one single objective function. In this paper the clusterwise linear regression has been analyzed in a fuzzy framework. In particular, a fuzzy clusterwise linear regression model (FCWLR model) with symmetrical fuzzy output and crisp input variables for performing fuzzy cluster analysis within a fuzzy linear regression framework is suggested. For measuring the goodness of fit of the suggested FCWLR model with fuzzy output, a fitting index is proposed. In order to illustrate the usefulness of FCWLR model in practice, several applications to artificial and real datasets are shown.