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The Back Propagation Neural Network(BPNN) has been used widely in objects recognition, but in fact, the BPNN can easily be trapped into a local minimum and has slow convergence. Moreover, the number of neural cells for hidden layer in the BPNN is hard to determine. For this reason, this paper proposes a novel method to improve the performance from the structure and the algorithm. The improved BP algorithm...
The concepts of permitted and forbidden sets enable a new perspective of the memory in neural networks. Such concepts exhibit interesting dynamics in recurrent neural networks. This paper studies the basic theories of permitted and forbidden sets of the linear threshold discrete-time recurrent neural networks. The linear threshold transfer function has been regarded as an adequate transfer function...
The convergence of background neural networks with two identical, unstructured subnetworks that inhibit each other is studied. The convergence properties include global attractivity and complete stability analysis. Furthermore, it shows that shifting the background level affects the existence and stability of the equilibrium point. Adding noise to the networks, it will fluctuate around an equilibrium...
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