In this study, one acquisition system of one channel is introduced to obtain the biological brain signals. Such system consists of a low-pass filter, an amplifier, a high-pass filter and a data acquisition board. A novel slopes algorithm with guaranteed bounded output is proposed for the approximation of the brain signals. The proposed method is compared with both the leastsquares and nearest-neighbour algorithms.

In this study, two epileptic signals are extracted from scanned images and three algorithms are proposed to characterise epileptic signals: the modified least squares, modified nearest neighbour, and slopes algorithms. The comparison results between the three algorithms are shown for the characterisation of two epileptic signals.

In this paper, a stable backpropagation algorithm is used to train an online evolving radial basis function neural network. Structure and parameters learning are updated at the same time in our algorithm, we do not make difference in structure learning and parameters learning. It generates groups with an online clustering. The center is updated to achieve the center is near to the incoming data in each iteration, so the algorithm does not need to generate a new neuron in each iteration, i.e., the algorithm does not generate many neurons and it does not need to prune the neurons. We give a time varying learning rate for backpropagation training in the parameters. We prove the stability of the proposed algorithm.

No abstract