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The brain is an organ especially differentiated to acquire algorithms in a self-organized fashion according to the genetic algorithm.
We hypothesize that two general principles give the brain its auto-designing capacity and, consequently, the potential to acquire algorithms: (a) Output-driven operation; the brain receives numerous types of input signals to which it makes a single output at an instant in time.
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Any newly acquired algorithms are added to those already acquired.
These acquired algorithms allow the brain to respond to the ever-changing environment that surrounds it.
As seen in Fig. 10, the accuracy degree of MR-DIDC algorithm does not vary obviously compared with that of the SPRINT algorithm, and as the data set increases, the two algorithms acquire similar accuracy degrees, because the accuracy lookup technology is still adopted in MR-DIDC algorithm.
Region 1 is smooth background, three different algorithms acquire almost same reflectance.
In smooth region, there are no land cover's variations, three different algorithms acquire almost same reflectance.
The simulation results show that the proposed algorithms acquire a good tradeoff between the overall system throughout and fairness, compared to maximal rate and max-min schemes.
We can see that the proposed algorithm can acquire higher throughput than the algorithm proposed in [20].
We propose consensus-making algorithms to acquire viewpoint-invariant GRR.
The derived model is then put into genetic algorithms to acquire optimized device process parameters.
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