IEEE TFS: Abstracts of Published Papers, vol. 1, no. 3
Fuzzy adaptive filters, with application to nonlinear channel
Two fuzzy adaptive filters are developed: one uses a recursive-least-squares (RLS) adaptation algorithm, and the other uses a least-mean-square (LMS) adaptation algorithm. The RLS fuzzy adaptive filter is constructed through the following four steps: (1) define fuzzy sets in the filter input space Rn whose membership functions cover U; (2) construct a set of fuzzy IF-THEN rules which either come from human experts or are determined during the adaptation procedure by matching input-output data pairs; (3) construct a filter based on the set of rules; and (4) update the free parameters of the filter using the RLS algorithm. The design procedure for the LMS fuzzy adaptive filter is similar. The most important advantage of the fuzzy adaptive filters is that linguistic information (in the form of fuzzy IF-THEN rules) and numerical information (in the form of input-output pairs) can be combined in the filters in a uniform fashion. The filters are applied to nonlinear communication channel equalization problems.
A fuzzy neural network model and its hardware implementation
A fuzzy classifier based on a four-layered feedforward neural network model is proposed. This connectionist fuzzy classifier, called CFC, realizes the weighted-Euclidean-distance fuzzy classification concept in a massively parallel manner to recognize input patterns. CFC employs a hybrid supervised/unsupervised learning scheme to organize referenced pattern vectors. This scheme not only overcomes the major drawbacks of multilayer perceptron models using the backpropagation algorithm, i.e., the local minimal problem and long training time, but also avoids the disadvantage of the huge storage space requirement of the probabilistic neural network. According to experimental results, CFC shows better accuracy for speech recognition than several existing methods, even in a noisy environment. Moreover, it has higher stability of recognition rates for different environmental conditions. A massively parallel hardware architecture has been developed to implement CFC. A bus-oriented multiprocessor, systolic processor structure, and pipelining are used to obtain low-cost, high-performance fuzzy classification.
On the completion of qualitative possibility measures
A relational description of a possibility introduced by D. Dubois and H. Prade in their 1988 book, called a qualitative possibility measure, is discussed. Among the requirements of these kinds of relationships is that they be complete. The focus is on the issue of completing these relationships. A measure associated with weak orderings, called buoyancy, is introduced. The principle of maximal buoyancy is suggested as a means for completion. The connection between the principle of maximal buoyancy and the principle of minimal specificity is shown. A special family of buoyancy measures, called BADD buoyancies, is also introduced that have the properties of entropy measures when the aggregates are restricted to be a probability distribution.
Switching regression models and fuzzy clustering
A family of objective functions called fuzzy c-regression models, which can be used too fit switching regression models to certain types of mixed data, is presented. Minimization of particular objective functions in the family yields simultaneous estimates for the parameters of c regression models, together with a fuzzy c-partitioning of the data. A general optimization approach for the family of objective functions is given and corresponding theoretical convergence results are discussed. The approach is illustrated by two numerical examples that show how it can be used to fit mixed data to coupled linear and nonlinear models.
Multistage fuzzy inference formulated as linguistic-truth-value
propagation and its learning algorithm based on back-propagating error
Multistage fuzzy inference, where in the consequence in an inference stage is passed to the next stage as a fact, is studied and formulated as a type of linguistic-truth-value propagation, based on a concept of linguistic similarities between conditional propositions in successive stages. The formulation is useful in studying the characteristics of multistage fuzzy inference and reveals its structural relationship to multilayer perceptrons. The learning algorithm for multistage fuzzy inference is then derived, using backpropagating error information. The algorithm provides a means of automatically training the multistage fuzzy inference network, using input-output exemplar patterns. Intermediate membership functions based on simulation results, which are generated automatically in the intermediate stage, are proposed. The intermediate stage fuzzy-classifies the input space using intermediate membership functions. In this way, intermediate membership functions provide information regarding regional characteristics in exemplar patterns.
Quantitative analysis of properties and spatial relations of fuzzy image
Properties of objects and spatial relations between objects play an important role in rule-based approaches for high-level vision. The partial presence or absence of such properties and relationships can supply both positive and negative evidence for region labeling hypotheses. Similarly, fuzzy labeling of a region can generate new hypotheses pertaining to the properties of the region, its relation to the neighboring regions, and, finally, hypotheses pertaining to the labels of the neighboring regions. A unified methodology that can be used to characterize both properties and spatial relationships of object regions in a digital image is presented. The methods proposed for computing the properties and relations of image regions can be used to arrive at more meaningful decisions about the contents of the scene.
These abstracts are posted in order to accelerate dissemination of evolving Fuzzy Systems information. The abstracts are from papers published in the IEEE Transactions on Fuzzy Systems (TFS).
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