International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems

In this paper, comparisons of OWA operators and ordinal OWA operators are carried out by using lattice theoretic methods. It is proved first that in both cases, the set of all aggregation operators forms a lattice, then the concept of positive valuation is used to measure the "orness" of aggregation operators and the structures of all such possible "orness" measures are determined.

A stroke order and number free method for on-line recognition of Chinese characters is proposed. Both input characters and the model characters are represented with complete attributed relational graphs (ARGs). The ARGs of the model base are built according to the human knowledge of the segment relation structure of Chinese characters. For the recognition purpose, an optimal matching measure between two ARGs is defined, and the graph matching is formulated as a search problem of finding the minimum cost path in a state space tree, using the A^* algorithm. Moreover, to reduce the search time of the A^*, besides a heuristic estimate, a novel strategy is employed which again uses the human knowledge of the segment position structure of Chinese characters to prune the tree. Our experimental results demonstrate the efficiency of the proposed method.

In this paper a simple procedure for pattern pre-recognition is presented. The task is to recognize whether a given pattern should be interesting for further analysis or not. The description of the procedure presented in this paper is based on formal theory of fuzzy logic in narrow sense so that the procedure – being general – can be used for many other purposes where patterns are to be recognized.

A knowledge-based controller(KBC) used in process control systems is presented. It has three features: first, it does not need the mathematical model; secondly, the adjustable parameters of KBC have practical meanings, so they can be determined easily using human experience, and thirdly, the contribution of KBC to a controlled plant is separated into two parts: steady states contribution and transient contribution. A simple fuzzy reasoning is employed to tune the KBC parameters. The experimental and simulation results show that KBC is very effective especially when there are variations in the process dynamics.

This paper proves the existence of L^p solution to the fuzzy functional equation

The paper elaborates on the encoding and decoding of numerical and nonnumerical data. Proposed are general criteria leading to the distortion-free interfacing mechanisms that help transform information between the systems (or modelling environments) operating at different levels of information granularity. Distinguished are three basic categories of information: numerical, interval-valued, and linguistic (fuzzy). As all of them are dealt with here, the paper subsumes the current studies concentrated exclusively on representing fuzzy sets through their numerical representatives (prototypes). The algorithmic framework in which the distortion-free interfacing is completed is realized through neural networks. Each category of information is treated separately and gives rise to its own specialized architecture of the neural network. Similarly, these networks require carefully designed training sets that fully capture the specificity of the reconstruction problem. Several carefully selected numerical examples are aimed at the illustration of the key ideas.

In this paper, in view of some paradoxical situations encountered in applying the Modus Ponens to Inference procedures in rule-based systems, we propose a new approach to perform the generalized Modus Ponens of Zadeh.

Specifically, truth evaluations of conditional fuzzy information will be defined and used in Modus Ponens, opening the way to a unified framework for the aggregation of rules.

Please send your abstracts or copies of papers that you want to see reviewed here to vladik@cs.utep.edu, or by regular mail to: Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA.