A new back-propagation algorithm with coupled neuron

Abstract

Summary form only given, as follows. A novel algorithm is developed for training multilayer fully connected feedforward networks of coupled neurons with both signoid and signum functions. Such networks can be trained by the familiar backpropagation algorithm since the coupled neuron (CONE) proposed uses the differentiable sigmoid function for its trainability. The algorithm is called CNR, or coupled neuron rule. The backpropagation (BP) and MRII algorithms which have both advantages and disadvantages have been developed earlier. The CONE takes advantages of the key ideas of both methods. By applying CNR to a simple network, it is shown that the convergence of the output error is much faster than that of the BP method when the variable learning rate is used. Finally, simulation results illustrate the effective learning algorithm.<>