In this paper effects of a new evolutionary rule added to the dynamics of the steepest descending asynchronous network model were studied. By numerical simulations, we found that the neural network operates very efficiently to improve the fault-tolerance when ,its capacity a under the new rule. The simulations were conducted on both the low- and the high-dimensional networks. A modified training scheme is also introduced.
We study the mathematical and physical properties of the states which are generated by excitation on a q-analogue of the coherent state. With the help of a q-analogue of the Laguerre polynomial, the squeezing and the statistical properties of the field in such the state are also discussed.
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