This paper presents the architecture and learning procedure underlying ANFIS (AdaptiveNetwork -based Fuzzy Inference System), a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In our simulation, we employ the ANFIS architecture to model nonlinear functions, identify nonlinear components on-linely in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificail neural networks and earlier work on fuzzy modeling are listed and discussed. Other extensions of the proposed ANFIS and promising applications to automatic control and signal processing are also suggested. 1 Introduction System modeling based on conventional mathematical tools (e.g., differential equations) is not well suited for dealing with ill-define...

: In a traditional database system, the result of a query is a set of values (those values that satisfy the query). In other data servers, such as a system with queries based on image content, or many text retrieval systems, the result of a query is a sorted list. For example, in the case of a system with queries based on image content, the query might ask for objects that are a particular shade of red, and the result of the query would be a sorted list of objects in the database, sorted by how well the color of the object matches that given in the query. A multimedia system must somehow synthesize both types of queries (those whose result is a set, and those whose result is a sorted list) in a consistent manner. In this paper we discuss the solution adopted by Garlic, a multimedia information system being developed at the IBM Almaden Research Center. This solution is based on "graded" (or "fuzzy") sets. Issues of efficient query evaluation in a multimedia system are very different fro...

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An algebra is presented for a simple probabilistic data model that may be regarded as an extension of the standard relational model. The probabilistic algebra is developed in such a way that (restricted to α-acyclic database schemes) the relational algebra is a homomorphic image of it. Strictly probabilistic results are emphasized. Variations on the basic probabilistic data model are discussed. The algebra is used to explicate a commonly used statistical smoothing procedure and is shown to be potentially very useful for decision support with uncertain information.

Fundamental and advanced developments in neuro-fuzzy synergisms for modeling and control are reviewed. The essential part of neuro-fuzzy synergisms comes from a common framework called adaptive networks, which unifies both neural networks and fuzzy models. The fuzzy models under the framework of adaptive networks is called ANFIS (Adaptive-Network-based Fuzzy Inference System), which possess certain advantages over neural networks. We introduce the design methods for ANFIS in both modeling and control applications. Current problems and future directions for neuro-fuzzy approaches are also addressed.

There are essential differences between multimedia databases (which may contain complicated objects, such as images), and traditional databases. These differences lead to interesting new issues, and in particular cause us to consider new types of queries. For example, in a multimedia database it is reasonable and natural to ask for images that are somehow "similar to" some fixed image. Furthermore, there are different ways of obtaining and accessing information in a multimedia database than information in a traditional database. For example, in a multimedia database, it might be reasonable to have a query that asks for, say, the top 10 images that are similar to a fixed image. This is in contrast to a relational database, where the answer to a query is simply a set. (Of course, in a relational database, the result to a query may be sorted in some way for convenience in presentation, such as sorting department members by salary, but logically speaking, the result is still simply a set, ...

Multiple classifier fusion may generate more accurate classification than each of the constituent classifiers. Fusion is often based on fixed combination rules like the product and average. Only under strict probabilistic conditions can these rules be justified. We present here a simple rule for adapting the class combiner to the application. c decision templates (one per class) are estimated with the same training set that is used for the set of classifiers. These templates are then matched to the decision profile of new incoming objects by some similarity measure. We compare 11 versions of our model with 14 other techniques for classifier fusion on the Satimage and Phoneme datasets from the database ELENA. Our results show that decision templates based on integral type measures of similarity are superior to the other schemes on both data sets.

The fuzzy linguistic approach has been applied successfully to many problems. However, there is a limitation on this approach, the loss of information. It appears due to its information representation model (discrete terms) and the computational methods used when fusion and combination processes are performed on linguistic variables. In this contribution we propose a new fuzzy linguistic representation model based on the concept of "Symbolic Translation" for dealing with linguistic information in a continuous domain. Together with this representation model we shall develop a computational technique for fusing linguistic variables without loss of information. Keywords: Linguistic variables, linguistic modeling, fusion of linguistic information. 1 Introduction The problems depending on their aspects can deal with dierent types of information. Usually, the problems present quantitative aspects that can be assessed by means of precise numerical values, but in other cases the problems p...

This paper deals with the application of fuzzy logic in a relational database environment with the objective of capturing more meaning of the data. It is shown that with suitable interpretations for the fuzzy membership functions, a fuzzy relational data model can be used to represent ambiguities in data values as well as impreciseness in the association among them. Relational operators for fuzzy relations have been studied, and applicability of fuzzy logic in capturing integrity constraints has been investigated. By introducing a fuzzy resemblance measure EQUAL for comparing domain values, the definition of classical functional dependency has been generalized to fuzzy functional dependency (ffd). The implication problem of ffds has been examined and a set of sound and complete inference axioms has been proposed. Next, the problem of lossless join decomposition of fuzzy relations for a given set of fuzzy functional dependencies is investigated. It is proved that with a suitable restriction on EQUAL, the design theory of a classical relational database with functional dependencies can be extended to fuzzy relations satisfying fuzzy functional dependencies.

A technique to perform design calculations on imprecise representations of parameters has been developed and is presented. The level of imprecision in the description of design elements is typically high in the preliminary phase of engineering design. This imprecision is represented using the fuzzy calculus. Calculations can be performed using this method, to produce (imprecise) performance parameters from imprecise (input) design parameters. The Fuzzy Weighted Average technique is used to perform these calculations. A new metric, called the γ-level measure, is introduced to determine the relative coupling between imprecise inputs and outputs. The background and theory supporting this approach are presented, along with one example. 1.