摘要:

A generic supervisory systems architecture is presented for control of two different types of systems; medical systems and industrial systems. It is structured in a hierarchical manner, consisting of a basic-level fuzzy logic controller supervised by a higher level decision-maker, which employs fuzzy logic theory to represent the human expertise used in supervising the plant including both the controller and the process. The supervisory level tasks consist of tuning, generating control rules, fault detection and diagnosis, together with an alarm and monitoring system. The paper is concerned more with the fault detection and diagnosis methods incorporated in the system. Simulation results for a medical system application are presented.

DOI:10.1049/ise.1994.0019

出版商:IEE    

年代:1994

数据来源: IET

摘要:

The paper considers the problem of decentralised fuzzy control of multivariable systems. Some definitions and theorems with regard to this problem are given. Decentralised fuzzy control algorithms, based on passive decomposition of control laws, are presented and illustrated by numerical examples. The algorithms use local sets of fuzzy relations for the subsystems whose control variables are not affected by the interactional sets of fuzzy relations. It is shown that the number of fuzzy relations is significantly reduced, and thus real-time control implementation is facilitated.

DOI:10.1049/ise.1994.0020

出版商:IEE    

年代:1994

数据来源: IET

摘要:

The paper discusses the methodological analogies and differences of the systems theory and knowledge-based approaches to modelling and simulating dynamical systems. This comparison is based on the notion of the dynamical system as defined in systems theory, in particular on the concept of state. Two examples show that these notions are relevant for quantitative models as used in systems theory and for qualitative models given in the knowledge base of a rule-based system. In addition, a formalisation is provided of rule-based systems within the concept of dynamical systems. It shows that the main motivation for using the knowledge-based approach in control engineering is the lack of information about the state of the physical system.

DOI:10.1049/ise.1994.0021

出版商:IEE    

年代:1994

数据来源: IET

摘要:

A neural function is developed that combines the characteristics of weightless and weighted binary neurons. A new combined generalisation algorithm is presented and applied to a neural state machine which is capable of learning to respond to sequences of inputs. The difficulty with such tasks lies in learning appropriate internal state assignments. A particular ‘iconic’ method of solving this problem is discussed. The analysis includes a discussion of implementational issues.

DOI:10.1049/ise.1994.0022

出版商:IEE    

年代:1994

数据来源: IET

摘要:

The use ofn-tuple or weightless neural networks as pattern recognition devices has been well documented [1]. They have a significant advantages over more common networks paradigms, such as the multi-layer perceptron in that they can be easily implemented in digital hardware using standard random access memories. To date,n-tuple networks have predominantly been used as fast pattern classification devices. The paper describes hown-tuple techniques can be used in the hardware implementation of a general auto-associative network.

DOI:10.1049/ise.1994.0023

出版商:IEE    

年代:1994

数据来源: IET

摘要:

The paper proposes a neural network technique to classify numerals using Zernike moments that are invariant to rotation only. In order to make them invariant to scale and shift, we introduce modified Zernike moments based on regular moments. Owing to the large number of Zernike moments used, it is computationally more efficient to select a subset of them that can discriminate as well as the original set. The subset is determined using stepwise discriminant analysis. The performance of a subset is examined through its comparison to the original set. The results are shown of using such a scheme to classify scaled, rotated, and shifted binary images and images that have been perturbed with random noise. In addition to the neural network approach, the Fisher's classifier is also used, which is a parametric classifier. A comparative study of their performances shows that the neural network approach produces better classification accuracy than the Fisher's classifier. When a suitable subset of Zernike moments is used, the classifiers perform well, just like the original set. The performance of the classifiers is also examined. The computational time is greatly reduced when a suitable subset of Zernike moments is used.

DOI:10.1049/ise.1994.0024

出版商:IEE    

年代:1994

数据来源: IET

摘要:

A fuzzy model of the cutting process has been obtained for a vertical milling-machine, adopting a previously used technique [1]. The inputs are cutting speed, feed rate, depth of cut, tool diameter, and workpiece hardness, and the output is the result of the three-axis force sensor signal, working directly on the machine tool. The identification approach is a black-box type, where only a file of I/O data is necessary to construct the model. The fuzzy model consists of a number of IF…THEN rules with fuzzy antecedents and consequents. Five fuzzy models have been generated according to the material type used. The output error and the relative output error have been used as performance indices of the fuzzy models.

DOI:10.1049/ise.1994.0025

出版商:IEE    

年代:1994

数据来源: IET