MBEST


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MBEST

A type of MRI that consists of a heavily weighted T2 sequence—based on the echo-planar technique—used for ultra-high-speed imaging of the brain, which allows rapid screening, functional imaging and analysis of the CSF fluid and blood flow patterns, as well as rapid imaging of restless patients.

MBEST

A type of MRI consisting of a heavily-weighted T2 sequence, based on the echo-planar technique, used for ultra-high-speed imaging of the brain, which allow rapid screening, functional imaging and analysis of the CSF fluid and blood flow patterns and rapid 'shooting' of restless Pts. See BEST, Magnetic resonance imaging.
References in periodicals archive ?
function mapper (key, value) { initialize the positions of all particles evaluate the function values of positions then select the pbest and gbest // update the particle while the termination condition is not met { calculate the mbest and [alpha] for each particle { update the pbest and gbest for each dimension { update position } } calculation + 1 } emit a message (ID of gbest, string of gbest and fitness) } After being processed by mappers, the immediate key/value pairs change to denote the information of gbest and global optimum of current data block, showed as Algorithm 2.
x(I + 1) = [+ or -] [beta] x [absolute value of (mbest - x(i))] x ln (1/u).
The function getting mbest in BQPSO is called [P.sub.i] = Get_P ([pbest.sub.i], gbest).
Pseudocode 1: Pseudocode for obtaining mbest. Get_ mbest(pbest) for j = 1 to l (the length of binary string) sum = 0; for each particle i sum = sum + pbest[i][j]; endfor avg = sum/M; if avg > 0.5 mbest[j] = 1; endif if avg < 0.5 mbest[j] = 0; endif if avg = 0.5 if rand() < 0.5 mbest[j] = 0; else mbest[j] = 1; endif endif endfor Return mbest In [35], here [l.sub.d] is the length of substring [X.sub.id].
Processing of data set; Initialize the current positions and the pbest positions of all particles which are binary bits with each representing whether the corresponding gene is selected or not; do Determine the mean best position among the particles by mbest = Get_mbest(pbest), select a suitable value for [beta]; for i = 1 to population size M Call the LIBSVM tool box to construct the SVM classifier and get the classification accuracy for the data; With the classification accuracy and the number of selected genes (i.e.
X[L.sub.i](t + 1) = [p.sub.i] [+ or -] b [absolute value of (Mbest - [XL.sub.i] (t))] In 1/u.
The quantum particle swarm optimization algorithm has less operator and simplifies the calculation, and to introduce the average best position Mbest, there exists a waiting effect among the particles which can greatly improve the cooperative work capability and enhance the global search capability of the algorithm [47, 48].
At the same time, due to the change of the global best position that can make Mbest deviate from the current position, the distance between the particle current position and Mbest will also increase and then will directly lead to a certain degree of divergence of particles which will increase diversity ([S.sub.x]).
where mbest is the mean of the personal best positions among all particles:
Here, [x.sub.1] represents the particle far away from the mean best position (mbest) of the swarm and its corresponding distribution at next iteration with the center [p.sub.1] visualised on upper right; [x.sub.2] denotes the particle near the mean best position of the swarm with [p.sub.2] being the center of the exponential distribution of its position at next iteration.