28,379 research outputs found
Self-growing neural network architecture using crisp and fuzzy entropy
The paper briefly describes the self-growing neural network algorithm, CID2, which makes decision trees equivalent to hidden layers of a neural network. The algorithm generates a feedforward architecture using crisp and fuzzy entropy measures. The results of a real-life recognition problem of distinguishing defects in a glass ribbon and of a benchmark problem of differentiating two spirals are shown and discussed
Self-adaptation and rule generation in a fuzzy system for X-ray rocking curve analysis.
X-ray rocking curve analysis is an example of a changing application domain. The salient characteristic of such a domain is that situations and facts can change over time. This means that the domain cannot be modelled by a fixed set of fuzzy rules. Instead, the rules must change over time and these changes must model actual changes that occur in the application domain. Three new techniques have been developed for altering a set of fuzzy rules: altering the credibility weight of an expert and using connection matrices to shift the focus of attention between different sets of rules; fine-tuning and changing the membership functions of fuzzy premise variables and thereby altering the meaning of the rules; and generating new fuzzy rules by inductive learning from examples.
A fuzzy system for X -ray rocking curve analysis has been developed and used to test each of these techniques. This fuzzy system uses frames, logic-based variables, connection matrices and credibility weights, fuzzy rules and a record of previous decisions in order to model X-ray rocking curve analysis. Question and answer sessions with the user are used to describe experimental rocking curves and structural parameters are deduced from this description. These structural parameters are then used to simulate a theoretical curve, which is compared with the experimental one. A performance measure is derived to calculate the degree of matching between the two curves. This performance measure is used to test each of the three techniques in turn. Tests have shown that the fuzzy system optimises its performance to suit new situations and facts
Matrix formulation of fuzzy rule-based systems
In this paper, a matrix formulation of fuzzy rule based systems is introduced. A gradient descent training algorithm for the determination of the unknown parameters can also be expressed in a matrix form for various adaptive fuzzy networks. When converting a rule-based system to the proposed matrix formulation, only three sets of linear/nonlinear equations are required instead of set of rules and an inference mechanism. There are a number of advantages which the matrix formulation has compared with the linguistic approach. Firstly, it obviates the differences among the various architectures; and secondly, it is much easier to organize data in the implementation or simulation of the fuzzy system. The formulation will be illustrated by a number of examples
Determining rules for closing customer service centers: A public utility company's fuzzy decision
In the present work, we consider the general problem of knowledge acquisition under uncertainty. A commonly used method is to learn by examples. We observe how the expert solves specific cases and from this infer some rules by which the decision was made. Unique to this work is the fuzzy set representation of the conditions or attributes upon which the decision make may base his fuzzy set decision. From our examples, we infer certain and possible rules containing fuzzy terms. It should be stressed that the procedure determines how closely the expert follows the conditions under consideration in making his decision. We offer two examples pertaining to the possible decision to close a customer service center of a public utility company. In the first example, the decision maker does not follow too closely the conditions. In the second example, the conditions are much more relevant to the decision of the expert
Model fusion using fuzzy aggregation: Special applications to metal properties
To improve the modelling performance, one should either propose a new modelling methodology or make the best of existing models. In this paper, the study is concentrated on the latter solution, where a structure-free modelling paradigm is proposed. It does not rely on a fixed structure and can combine various modelling techniques in ‘symbiosis’ using a ‘master fuzzy system’. This approach is shown to be able to include the advantages of different modelling techniques altogether by requiring less training and by minimising the efforts relating optimisation of the final structure. The proposed approach is then successfully applied to the industrial problems of predicting machining induced residual stresses for aerospace alloy components as well as modelling the mechanical properties of heat-treated alloy steels, both representing complex, non-linear and multi-dimensional environments
Data Editing for Neuro-Fuzzy Classifiers
In this paper we investigate the potential benefits and
limitations of various data editing procedures when
constructing neuro-fuzzy classifiers based on hyperbox
fuzzy sets. There are two major aspects of data editing
which we are attempting to exploit: a) removal of outliers
and noisy data; and b) reduction of training data size. We
show that successful training data editing can result in
constructing simpler classifiers (i.e. a classifier with a
smaller number and larger hyperboxes) with better
generalisation performance. However we also indicate
the potential dangers of overediting which can lead to
dropping the whole regions of a class and constructing
too simple classifiers not able to capture the class
boundaries with high enough accuracy. A more flexible
approach than the existing data editing techniques based
on estimating probabilities used to decide whether a
point should be removed from the training set has been
proposed. An analysis and graphical interpretations are
given for the synthetic, non-trivial, 2-dimensional
classification problems
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