tailieunhanh - Báo cáo hóa học: " Surface Approximation Using the 2D FFENN Architecture"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Surface Approximation Using the 2D FFENN Architecture | EURASIP Journal on Applied Signal Processing 2004 17 2696-2704 2004 Hindawi Publishing Corporation Surface Approximation Using the 2D FFENN Architecture S. Panagopoulos Institute for Communications Signal Processing University of Strathclyde Royal College Building Glasgow G1 1XW UK Em ail spyros@ J. J. Soraghan Institute for Communications Signal Processing University of Strathclyde Royal College Building Glasgow G1 1XW UK Email j. soraghan@ Received 27 August 2003 Revised 10 March 2004 Recommended for Publication by Bernard Mulgrew A new two-dimensional feed-forward functionally expanded neural network 2D FFENN used to produce surface models in two dimensions is presented. New nonlinear multilevel surface basis functions are proposed for the network s functional expansion. A network optimization technique based on an iterative function selection strategy is also described. Comparative simulation results for surface mappings generated by the 2D FFENN multilevel 2D FFENN multilayered perceptron MLP and radial basis function RBF architectures are presented. Keywords and phrases neural networks sea clutter surface modeling. 1. INTRODUCTION One of the main properties of feed-forward neural networks is that of learning an input-output mapping from a set of examples characterizing a real system. The network is trained with some examples comprising an input signal and the desired response. The network weights are then modified using an adaptive optimization technique to minimize the difference between the desired response and actual response. Two well-known feed-forward artificial neural networks are the multilayered perceptron MLP and radial basis function RBF . Both networks have been termed as universal approximators 1 2 . Their performance has been demonstrated in various application areas such as linear and nonlinear adaptive filtering 3 time series prediction 4 dynamic reconstruction 5 and black-box modeling 6 . However these .

TÀI LIỆU LIÊN QUAN
crossorigin="anonymous">
Đã phát hiện trình chặn quảng cáo AdBlock
Trang web này phụ thuộc vào doanh thu từ số lần hiển thị quảng cáo để tồn tại. Vui lòng tắt trình chặn quảng cáo của bạn hoặc tạm dừng tính năng chặn quảng cáo cho trang web này.