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Intelligent Systems Engineering

Issue 4 • Date Winter 1994

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Displaying Results 1 - 7 of 7
  • Neuro-pattern classification using Zernike moments and its reduced set of features

    Publication Year: 1994 , Page(s): 230 - 235
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (356 KB)  

    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 View full abstract»

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  • Generic system architecture for supervisory fuzzy control

    Publication Year: 1994 , Page(s): 181 - 193
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (884 KB)  

    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 View full abstract»

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  • Auto-associative memory using n-tuple techniques

    Publication Year: 1994 , Page(s): 222 - 229
    Cited by:  Patents (1)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (488 KB)  

    The use of n-tuple or weightless neural networks as pattern recognition devices has been well documented. They have a significant advantages over more common networks paradigms, such as the multilayer 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 how n-tuple techniques can be used in the hardware implementation of a general auto-associative network View full abstract»

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  • Decentralised fuzzy control of multivariable systems by passive decomposition

    Publication Year: 1994 , Page(s): 194 - 200
    Cited by:  Papers (1)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (344 KB)  

    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 View full abstract»

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  • Fuzzy model of cutting process on a milling machine

    Publication Year: 1994 , Page(s): 236 - 244
    Cited by:  Papers (1)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (512 KB)  

    A fuzzy model of the cutting process has been obtained for a vertical milling-machine, adopting a previously used technique (Sugeno-Yasukawa, 1991). 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 sensing signal, working directly on the machine tool. The identification approach is a blackbox 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 View full abstract»

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  • Notion of the state in systems theory and artificial intelligence

    Publication Year: 1994 , Page(s): 201 - 210
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (636 KB)  

    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 View full abstract»

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  • Binary neural systems: combining weighted and weightless properties

    Publication Year: 1994 , Page(s): 211 - 221
    Cited by:  Patents (1)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (804 KB)  

    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 View full abstract»

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Aims & Scope

Intelligent Systems Engineering was published by the IET between 1992 and 1994.

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