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The advantages of the PSO are its simple implementation and it's quickly convergence ability.
ART based neural network (ARTNN) has some advantages such as no local extremum, quickly convergence and so on.
These theorems provide finite lower bounds on how quickly convergence is likely to occur, bounds that show one need not wait for convergence through some infinitely long run.
Experiment results show that at the same bit rate the G2LA has not only the quickly convergence time but also high quality reconstructed image than traditional GLA technique with Euclidean distance measure, especially in high dimension and a large volume data system.
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In the 21st century, the defining word in high technology may well be "convergence"–your TV is quickly converging with your computer, which is acting a lot like your Rolodex.
As P k (v, w) quickly converges toward μ(w) when k grows (convergence speed is given by λ k where λ is the magnitude of the second eigenvalue of the transition matrix Π).
These theorems provide finite lower bounds on how quickly such convergence is likely to be.
For a high coupling value,, synchronization is reached quickly, but convergence to a synchronized stable state is not always achieved.
The property of large numbers shows that almost all infinite binary sequences have limit digit frequency ½, but says nothing about how quickly this convergence happens or about the statistical properties of the initial segments.
A great advantage of the N R algorithm is that it is proven to converge quickly in general (quadratic convergence).
It is shown that the proposed algorithm can achieve convergence quickly.
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