7,406 research outputs found
Clinical symptom dimensions and deficits on the Continuous Performance Test in schizophrenia
No full textAn Investigation of Unbalanced-Magnetron Sputtered TiAlN Films on SKH51 High-Speed Steel
No full text
CMOS exponential-control variable gain amplifiers
No full textNew CMOS exponential-control variable-gain amplifiers (VGAs) are presented. The
control signal can be either current-mode or voltage-mode. Since no multiplier is needed in the
proposed circuits, the proposed VGAs can be very compact. For the case of supply voltages
VDD=|VSS|=1.5V, the power dissipation is only 0.48mW. The gain control range of the proposed
VGA can be 30 dB. The proposed circuits have been fabricated in a 0.5 mm n-well CMOS process.
Experimental results are given to confirm the feasibility of the proposed VGAs, which are expected
to be useful in analogue signal processing applications
Performance of Differential Evolution and Particle Swarm Methods on Some Relatively Harder Multi-modal Benchmark Functions
Full text linkOur objective in this paper is to compare the performance of the Differential Evolution (DE) and the Repulsive Particle Swarm (RPS) methods of global optimization. To this end, some relatively difficult test functions have been chosen. These functions are: Perm, Power-Sum, Bukin, Zero-Sum, Hougen, Giunta, DCS, Kowalik, Fletcher-Powell and some now functions. Our results show that DE (with the exponential crossover scheme) mostly fails to find the optimum of most of these functions. Of course, it succeeds in case of some functions (perm#2, zero-sum) for very small dimension (m), but begins to falter as soon as the dimension is increased. In case of DCS function, it works well up to m (dimension) = 5. When we use no crossover (only probabilistic replacement) we obtain better results in case of several of the functions under study. Thus, overall, table #2 presents better results than what table #1 does. In case of Perm#1, Perm#2, Zero-sum, Kowalik, Hougen and Power-sum functions the advantage is clear. Whether crossover or no crossover, DE falters when the optimand function has some element of randomness. This is indicated by the functions: Yao-Liu#7, Fletcher-Powell, and “New function#2”. DE has no problems in optimizing the “New function#1”. But the “New function #2” proves to be a hard nut. However, RPS performs much better for such stochastic functions. When the Fletcher-Powell function is optimized with non-stochastic c vector, DE works fine. But as soon as c is stochastic, it becomes unstable. Thus, it may be observed that an introduction of stochasticity into the decision variables (or simply added to the function as in Yao-Liu#7) interferes with the fundamentals of DE, which works through attainment of better and better (in the sense of Pareto improvement) population at each successive iteration.Repulsive particle swarm; Differential evolution; Global optimization; Stochasticity; random disturbances; Crossover; Perm; zero sum; Kowalik; Hougen; Power sum; DCS; Fletcher Powell; multimodal; benchmark; test functions; Bukin; Giunta
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