RESUMEN
In this paper, the concepts of structured and nonstructured concurrency are introduced to classify concurrent systems, and a hierarchical partition method is used to handle the complexity of nonstructured concurrent systems. A concurrent system with structured and nonstructured concurrency is modeled by a precedence graph. The language of the precedence graph is defined, and a representation method of the language is developed. By decomposing the language hierarchically, hierarchical partitions of the concurrent system can be obtained. From the partitions, a concurrent system can be reduced hierarchically.
RESUMEN
In this paper, a hierarchical reduction method of hypergraphs is proposed. A macro-vertex in a reduced hypergraph corresponds to an edge of the original hypergraph, and thus a reduced hypergraph can provide a partition of a system. The reduction is realized by the iterations and the sequence of hierarchical reduction gives a sequence of hierarchical partitions. The proposed method allows to reduce and decompose the complexity of the system represented by hypergraphs.
RESUMEN
In fuzzy modeling, it is relatively easy to manually define rough fuzzy rules for a target system by intuition. It is, however, time-consuming and difficult to fine-tune them to improve their behavior. This paper describes a tuning method for fuzzy models which is applicable regardless of the form of fuzzy rules and the used defuzzification method. For this purpose, this paper proposes a fuzzy neural network model which can embody fuzzy models. The proposed model provides the functions to perform fuzzy inference and to tune the parameters for the shape of antecedent linguistic terms, the relative importance degrees of rules, and the relative importance degrees of antecedent linguistic terms in rules. In addition, to show its applicability, we perform some experiments and present the results.