An Improved Direct Neural Network Approach to Flatness Pattern Recognition Baseed on GA-RBF

Xiao Hua Feng; Xiao Juan Sun

doi:10.4028/www.scientific.net/AMR.383-390.2958

Paper Titles

Analysis for Radiation Characteristics of Horn Antenna for Out of Band
p.2935

Fatigue Life Assessment of Crane Reel
p.2941

Kinetics of the Ozonation of Tetrabromobisphenol-A in Wastewater
p.2945

Influence of Additive White Gaussian Noise on the Interpolation Method of Discrete Spectrum
p.2951

An Improved Direct Neural Network Approach to Flatness Pattern Recognition Baseed on GA-RBF
p.2958

Analysis of Electromagnetic Field and Temperature Field of Low Speed High Torque Permanent Magnet Motor under Locked-Rotor Operation
p.2963

The Characteristics of Melt Flow in Composite Spinning Micropore
p.2968

Simulation and Dynamic Characteristics of Discharging Process of Storage Tank Used in Conventional Air-Conditioning
p.2974

General Model of Optimal Control of Fully Flexible Vibrating Suspension Systems
p.2978

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 383-390An Improved Direct Neural Network Approach to...

An Improved Direct Neural Network Approach to Flatness Pattern Recognition Baseed on GA-RBF

Abstract:

In this paper, an improved the radial basis function (RBF) neural network direct recognition approach to shape flatness pattern is proposed. The genetic algorithm (GA) is employed to obtain more optimal structure and initial parameters of RBF network. The new approach with the advantages of RBF, such as fast learning and high accuracy, is efficient and intelligent. it can not only effectively settle the problem of the different topologic configurations with changing strip widths but also improve practicability and precision.Compared to the improved direct recognition method with GA-BP,The simulation results show that the speed and accuracy of the flatness pattern recognition model based on GA-RBF are obviously improved.

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Manufacturing Science and Technology, ICMST2011

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Periodical:

Advanced Materials Research (Volumes 383-390)

Pages:

2958-2962

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.383-390.2958

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Online since:

November 2011

Authors:

Xiao Hua Feng, Xiao Juan Sun

Keywords:

Genetic Algorithm (GA), Pattern Recognition, RBF Network, Shape Flateness

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