Narrow Results

Knowledge representation and similarity measure play an important role in classifying vague legal concepts. In order to consider fuzziness and context-sensitive effects, for the representation of the precedent, a fuzzy factor hierarchy is studied. Current distance-based and feature-based similarity measures are only surface level ones that can't make more than a comparison between objects. Therefore, a deep level similarity measure that can evaluate the results of the surface level one is needed. A structural similarity: factor-based similarity, that is integrated by the surface level and deep level ones is proposed. An argument model that is based on the proposed knowledge representation and similarity measure is proposed. Considering the vague legal concept in the United Nations Convention on Contracts for the International Sale of Goods(CISG), a fuzzy legal argument system is constructed. The main purpose of the proposed system is to support the law education.

We propose an interactive approach to data mining meant as the derivation of linguistic summaries of databases. For interactively formulating the linguistic summaries, and then for searching the database, we employ Kacprzyk and Zadrożny's [6-11]) fuzzy querying add-on, FQUERY for Access. We present an implementation for the derivation of linguistic summaries of sales data at a computer retailer.

Cutting tool condition is a major factor relating to the state of the machine tool. Monitoring tool condition by using an integrated system composed of multi-sensors, signal processing devices and intelligent decision making plans is a necessary requirement for modern automatic manufacturing processes. An intelligent tool wear monitoring system will be introduced in this paper. A unique fuzzy driven neural network based pattern recognition algorithm has been developed from this research. It can fuse the information from multiple sensors and has strong learning and noise suppression ability. This lead to successful tool wear classification under a range of machining conditions.

The problem of selecting and tuning the parameters of a neurofuzzy Power System Stabiliser (PSS) using genetic algorithms is discussed in this paper. The neurofuzzy controller is implemented as a multilayer perceptron, in which the weights are fuzzy membership functions. The optimal values of the parameters of the if-part and the then-part membership functions have been found during the learning method by applying an appropriate fitness function based on the rotor speed deviations. The overall system has been tested on a simulation model in different operating conditions and improved responses have been achieved.