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Local minimum is incorporated problem in neural network (NN) training. To alleviate this problem, a modification of standard backpropagation (BP) algorithm, called BPCL for training NN is proposed. When local minimum arrives in the training, the weights of NN become idle. If the chaotic variation of learning rate (LR) is included during training, the weight update may be accelerated in the local minimum...
It is important to study the neural network (NN) when it falls into chaos, because brain dynamics involve chaos. In this paper, the several chaotic behaviors of supervised neural networks using Hurst Exponent (H), fractal dimension (FD) and bifurcation diagram are studied. The update rule for NN trained with back-propagation (BP) algorithm absorbs the function of the form x(1-x) which is responsible...
Human walks, runs, dances and left behind interesting information on their actions. This paper presents how chaotic dynamics help to interpret and classify human actions. The trajectories of two legs are extracted during a motion such as walk. These trajectories of foot points are collected from an artificial human video arrangement. Each dimension of trajectory represents a time series. The phase...
This paper presents a learning approach called adaptive coherence scheme (CAS) that adaptively reduces information on input patterns in hidden layer(s) of a neural network. The hidden units in a neural network store information continuously during training session. As a result the network becomes extremely familiar with every details of input patterns. This is not desirable in training. Therefore,...
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