Modular neural network structure with fast training/recognition algorithm for pattern recognition
Yanlai Li, Kuanquan Wang, Tao Li · 2008
In this paper, modular neural network structure with fast training/recognition algorithm for pattern recognition task decomposition is presented. After the modular neural network is described, a new training algorithm, named non-gradient (NG) training algorithm, is proposed to train the sub-modules. The inputs error of the output layer is taken into account. Four classes of solution equations for parameters are deducted respectively. The advantage of the presented algorithm is that it doesn’t need calculating the gradient of error function at all. In each iteration step, the weight or threshold can be optimized one by one with other parameters fixed. In the recognition stage, a new and fast JUMP recognition algorithm is proposed to save the recognition time. Effectiveness of the presented scheme is demonstrated by a palmprint recognition experiment.