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We present a biologically inspired model for estimating the position of a moving target that is invariant to the target’s contrast. Our model produces a monotonic relationship between position and output activity using a divisive normalization between the ‘receptive fields’ of two overlapping, wide-field, small-target motion detector (STMD) neurons. These visual neurons found in flying insects, likely...
Nanoscale communication will expand the scope of nanotechnology and bring new applications to the future world. Among the different means for nanoscale communication, artificial neuronal networks are a novel paradigm. The aim of this paper is to find the optimal multiple-access scheme with the objectives of maximising the number of parallel packages and minimising the firing time difference of sensors...
Spectrum sensing is a key function for the second users (SUs) to determine availability of a channel in the primary user's (PUs) spectrum in cognitive radio(CR). In order to achieve that, much research of energy detection has been studied, but they play poor performance in low signal-to-noise (SNR) environment. In this paper, we proposed Support Vector Machines (SVM) based on Genetic Algorithms (GA),...
In order to improve the measurement precision and stability of MWD Instrument, we create Elman neural network model and utilize self-adaptive genetic algorithm to optimize weights threshold value of the right of Elman network, which overcomes the disadvantages of traditional method, such as training for a long time, easy to fall into local optimal solution. Simulation results show that the error accuracy...
This paper deals with a method of distributed behavior learning of multiple mobile robots. Various types of artificial neural networks are applied for behavior learning of mobile robots in unknown and dynamic environments. In the paper, we propose a method of distributed behavioral learning based on a spiking neural network. The robot learns the forward relationship from sensory inputs to motor outputs...
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