Granular Neural Networks for Numerical-Linguistic Data Fusion and Knowledge Discovery  

Oleh:

Kandel, A. ; Gagliano, R. A. ; Fraser, M. D. ; Zhang, Yan-Qing

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 11 no. 3 (2000), page 658-667.
Topik: FUSION; granular; neural networks; numerical - linguistic; data fusion; knowledge discovery

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.4 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

Lihat Detail Induk

Isi artikel

We present a neural - networks - based knowledge discovery and data mining (KDDM) methodology based on granular computing, neural computing, fuzzy computing, linguistic computing, and pattern recognition. The major issues include : 1) how to make neural networks process both numerical and linguistic data in a database, 2) how to convert fuzzy linguistic data into related numerical features, 3) how to use neural networks to do numerical - linguistic data fusion, 4) how to use neural networks to discover granular knowledge from numerical - linguistic databases, and 5) how to use discovered granular knowledge to predict missing data. In order to answer the above concerns, a granular neural network (GNN) is designed to deal with numerical - linguistic data fusion and granular knowledge discovery in numerical - linguistic databases. From a data granulation point of view the GNN can process granular data in a database. From a data fusion point of view, the GNN makes decisions based on different kinds of granular data. From a KDDM point of view the GNN is able to learn internal granular relations between numerical - linguistic inputs and outputs, and predict new relations in a database. The GNN is also capable of greatly compressing low - level granular data to high - level granular knowledge with some compression error and a data compression rate. To do KDDM in huge databases, parallel GNN and distributed GNN will be investigated in the future.

Opini Anda

Klik untuk menuliskan opini Anda tentang koleksi ini!

Kembali