Neural Networks
Practical neural network recipes in C++
Practical neural network recipes in C++
Swarm intelligence
IEEE Intelligent Systems
Detection and classification of road signs in natural environments
Neural Computing and Applications
Road-Sign Detection and Recognition Based on Support Vector Machines
IEEE Transactions on Intelligent Transportation Systems
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
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This paper presents a fast version of probabilistic neural network model for the recognition of traffic signs. The model incorporates the J-means algorithm to select the pattern layer centers and Particle Swarm Optimization (PSO) to optimize the spread parameter, enhancing its performance. In order to cope with the degradations, the Combined Blur-Affine Invariants (CBAIs) are adopted to extract the features of traffic sign symbols without any restorations which usually need a great amount of computations. The experimental results indicate that the fast version of PNN optimized using PSO is not only parsimonious but also has better generalization performance.