Bio-inspired neural network with application to license plate recognition: hysteretic ELM approach

Purpose - This paper aims to present a bio-inspired neural network for improvement of information processing capability of the existing artificial neural networks. Design/methodology/approach - In the network, the authors introduce a property often found in biological neural system - hysteresis - as the neuron activation function and a bionic algorithm - extreme learning machine (ELM) - as the learning scheme. The authors give the gradient descent procedure to optimize parameters of the hysteretic function and develop an algorithm to online select ELM parameters, including number of the hidden-layer nodes and hidden-layer parameters. The algorithm combines the idea of the cross validation and random assignment in original ELM. Finally, the authors demonstrate the advantages of the hysteretic ELM neural network by applying it to automatic license plate recognition. Findings - Experiments on automatic license plate recognition show that the bio-inspired learning system has better classification accuracy and generalization capability with consideration to efficiency. Originality/value - Comparing with the conventional sigmoid function, hysteresis as the activation function enables has two advantages: the neuron's output not only depends on its input but also on derivative information, which provides the neuron with memory; the hysteretic function can switch between the two segments, thus avoiding the neuron falling into local minima and having a quicker learning rate. The improved ELM algorithm in some extent makes up for declining performance because of original ELM's complete randomness with the cost of a litter slower than before.