Granular fuzzy models: a study in knowledge management in fuzzy modeling
نویسندگان
چکیده
منابع مشابه
Granular fuzzy models: a study in knowledge management in fuzzy modeling
In system modeling, knowledge management comes vividly into the picture when dealing with a collection of individual models. These models being considered as sources of knowledge, are engaged in some collective pursuits of a collaborative development to establish modeling outcomes of global character. The result comes in the form of a so-called granular fuzzy model, which directly reflects upon...
متن کاملGranular Neuro-fuzzy Knowledge Compression and Expansion
In order to overcome weaknesses of the conventional crisp neural network and the fuzzy-operation-oriented neural network, we have developed a general fuzzy-reasoning-oriented fuzzy neural network called a Crisp-Fuzzy Neural Network (CFNN) which is capable of extracting high-level knowledge such as fuzzy IF-THEN rules from either crisp data or fuzzy data. A CFNN can eeectively compress a 5 5 fuz...
متن کاملqfd planning with cost consideration in fuzzy environment
در عصر حاضر که رقابت بین سازمان ها بسیار گسترش یافته است، مطالعه و طرحریزی سیستم های تولیدی و خدماتی به منظور بهینه سازی عملکرد آنها اجتناب ناپذیر می باشد. بخش عمده ای از رقابت پذیری سازمان ها نتیجه رضایتمندی مشتریان آنها است. میزان موفقیت سازمان های امروزی به تلاش آنها در جهت شناسایی خواسته ها و نیازهای مشتریان و ارضای این نیازها بستگی دارد. از طرفی کوتاه کردن زمان ارائه محصول/خدمات به مشتریان...
15 صفحه اولFuzzy Knowledge Management through Knowledge Engineering and Fuzzy Logic
Knowledge management (KM) facilitates the capture, storage, and dissemination of knowledge using information technology. In this paper, we propose a FKM (Fuzzy Knowledge Management) approach to managing fuzzy knowledge through knowledge engineering and fuzzy logic. First, fuzziness is introduced into CGs (Conceptual Graphs) for constructing fuzzy knowledge models. Fuzzy knowledge models are use...
متن کاملCommutativity as Prior Knowledge in Fuzzy Modeling
In fuzzy modeling (FM), the quantity and quality of the training set is crucial to properly grasp the behavior of the system being modeled. However, the available data is often not large enough or is deficiently distributed along the input space, not revealing the system behavior completely. In such cases, the consideration of any prior knowledge about the system can be decisive for the accurac...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Approximate Reasoning
سال: 2012
ISSN: 0888-613X
DOI: 10.1016/j.ijar.2012.05.002