Discovering Knowledge and Modelling Systems using Granular Computing and Neurofuzzy Structures
نویسندگان
چکیده
In this paper a new systematic modelling approach using Granular Computing (GrC) and Neurofuzzy modelling is presented. In this study a GrC algorithm is used to extract relational information and data characteristics out of the initial database. The extracted knowledge is then translated into a linguistic rule-base of a fuzzy system. This rule-base is finally realised via a Neurofuzzy modelling structure. The proposed methodology is then applied to the challenging environment of a multi-dimensional, non-linear and sparse data space consisting of mechanical properties of heat treated steel.
منابع مشابه
Uncertainty analysis of hierarchical granular structures for multi-granulation typical hesitant fuzzy approximation space
Hierarchical structures and uncertainty measures are two main aspects in granular computing, approximate reasoning and cognitive process. Typical hesitant fuzzy sets, as a prime extension of fuzzy sets, are more flexible to reflect the hesitance and ambiguity in knowledge representation and decision making. In this paper, we mainly investigate the hierarchical structures and uncertainty measure...
متن کاملINTERVAL ANALYSIS-BASED HYPERBOX GRANULAR COMPUTING CLASSIFICATION ALGORITHMS
Representation of a granule, relation and operation between two granules are mainly researched in granular computing. Hyperbox granular computing classification algorithms (HBGrC) are proposed based on interval analysis. Firstly, a granule is represented as the hyperbox which is the Cartesian product of $N$ intervals for classification in the $N$-dimensional space. Secondly, the relation betwee...
متن کاملSupporting Literature Exploration with Granular Knowledge Structures
Reading and literature exploration are important tasks of scientific research. However, conventional retrieval systems provide limited support for these tasks by concentrating on identifying relevant materials. New generation systems should provide additional support functionality by focusing on analyzing and organizing the retrieved materials. A framework of literature exploration support syst...
متن کاملNeurofuzzy Construction Algorithms 1 2 Year Progress Report
Neurofuzzy systems have been developed as grey box modelling technique ideal for the task of system identiication. Neurofuzzy models combine the mathematical structure of Associative Memory Networks (AMNs) with the transparency of fuzzy systems. This produces a modelling technique to which mathematical analysis can be applied, while being more transparent than traditional black box models. Unfo...
متن کاملConcept Granule-Based Granular Lattice and Application in Knowledge Retrieval
In this paper, from the viewpoint of granular computing, information granule and concept granule are introduced to information systems, and then structure of concept granules and their distance, concept granular entropy and significance degree are presented. The concept of concept granule-based granular lattice is proposed in information systems. The representation and operation rules of comple...
متن کامل