Shape recognition based on neural networks trained by differential evolution algorithm

Authors:
Ji-Xiang Du;De-Shuang Huang;Xiao-Feng Wang;Xiao Gu
Affiliations:
Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui 230031, P.R. China and Department of Automation, University of Science and Technology ...;Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui 230031, P.R. China;Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui 230031, P.R. China and Department of Automation, University of Science and Technology ...;Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui 230031, P.R. China
Venue:
Neurocomputing
Year:
2007

Citing 4
Cited 4

Contour sequence moments for the classification of closed planar shapes

Pattern Recognition
Combinatorial evolution of regression nodes in feedforward neural networks

Neural Networks
Differential Evolution Training Algorithm for Feed-Forward Neural Networks

Neural Processing Letters
Training feedforward networks with the Marquardt algorithm

IEEE Transactions on Neural Networks

Supervised locally linear embedding for plant leaf image feature extraction

ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Autonomous Shape Control of a Deformable Object by Multiple Manipulators

Journal of Intelligent and Robotic Systems
Multi-sensor based prediction of metal deposition in pulsed gas metal arc welding using various soft computing models

Applied Soft Computing
A novel modified binary differential evolution algorithm and its applications

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper a new method for recognition of 2D occluded shapes based on neural networks using generalized differential evolution training algorithm is proposed. Firstly, a generalization strategy of differential evolution algorithm is introduced. And this global optimization algorithm is applied to train the multilayer perceptron neural networks. The proposed algorithms are evaluated through a plant species identification task involving 25 plant species. For this practical problem, a multiscale Fourier descriptors (MFDs) method is applied to the plant images to extract shape features. Finally, the experimental results show that our proposed GDE training method is feasible and efficient for large-scale shape recognition problem. Moreover, the experimental results illustrated that the GDE training algorithm combined with gradient-based training algorithms will achieve better convergence performance.