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In most convolutional neural networks (CNNs), the output is a single classification result by combining all the neuron activations in the last layer. As we know, local connectivity is an important characteristic of CNNs. Each neuron in the network corresponds to a local region in the original image. Hence, it is possible to simultaneously obtain local visibility of a target object by analyzing neuron...
In this paper, a kind of second order diagonal recurrent neural network (SDRNN) identification method based on dynamic back propagation(DBP) algorithm with momentum term is proposed. This identification method overcomes the disadvantages such as slow convergent speed and trapping the local minimum. The SDRNN is similar as diagonal recurrent neural network(DRNN) in the structure, two tapped delays...
The study includes the correlation dimension and the largest Lyapunov exponent of sea clutter based on real radar data obtained with IPIX X-band polarimetric coherent radar, which proved that sea clutter has chaotic characteristics. A method of prediction about sea clutter signal based on chaotic neural network and theory of phase-space reconstruction is established, which has a in-depth analysis...
Energy is a limited resource in wireless sensor networks. The reduction of energy consumption is crucial to prolong the lifetime of wireless sensor networks. Dynamic power management (DPM), which is to reduce power dissipation by putting the sensor node into different states, should be carefully taken into account in wireless sensor networks. In this paper, a new method of DPM is proposed. In this...
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