Neuron-Adaptive Higher Order Neural-Network Models for Automated Financial Data Modeling  

Oleh:

Fulcher, J. ; Zhang, Ming ; Xu, Shuxiang

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 13 no. 1 (2002), page 188-204.
Topik: FINANCIAL; neuron - adaptive; neural - network; automated; financial data; modeling

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.6 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

Lihat Detail Induk

Isi artikel

Real - world financial data is often nonlinear, comprises high - frequency multipolynomial components, and is discontinuous (piecewise continuous). Not surprisingly, it is hard to model such data. Classical neural networks are unable to automatically determine the optimum model and appropriate order for financial data approximation. We address this problem by developing neuron - adaptive higher order neural - network (NAHONN) models. After introducing one - dimensional (1 - D), two - dimensional (2 - D), and n - dimensional NAHONN models, we present an appropriate learning algorithm. Network convergence and the universal approximation capability of NAHONN s are also established. NAHONN Group models (NAHONG s) are also introduced. Both NAHONNs and NAHONG s are shown to be "open box" and as such are more acceptable to financial experts than classical (closed box) neural networks. These models are further shown to be capable of automatically finding not only the optimum model, but also the appropriate order for specific financial data.

Opini Anda

Klik untuk menuliskan opini Anda tentang koleksi ini!

Kembali