With the rise of big data in cloud computing, many optimization problems have gradually developed into high-dimensional large-scale optimization problems. In order to address the problem of dimensionality in optimization for genetic algorithms, an adaptive dimensionality reduction genetic optimization algorithm (ADRGA) is proposed. An adaptive vector angle factor is introduced in the algorithm. When the angle of an individual’s adjacent dimension is less than the angle factor, the value of the smaller dimension is marked as 0. Then, the angle between each individual dimension is calculated separately, and the number of zeros in the population is updated. When the number of zeros of all individuals in a population exceeds a given constant in a certain dimension, the dimension is considered to have no more information and deleted. Eight high-dimensional test functions are used to verify the proposed adaptive dimensionality reduction genetic optimization algorithm. The experimental results show that the convergence, accuracy, and speed of the proposed algorithm are better than those of the standard genetic algorithm (GA), the hybrid genetic and simulated annealing algorithm (HGSA), and the adaptive genetic algorithm (AGA).
PurposeThe purpose of this paper is to detect edge of image in high noise level, suffering Gaussian noise.Design/methodology/approachCanny edge detection algorithm performs poorly when applied to highly distorted images suffering from Gaussian noise. In Canny algorithm, 2D‐gaussian function is used to remove noise and preserve edge. In high noise level, 2D‐gaussian function cannot meet the needs. In this paper, an improving Canny edge detection algorithm is presented. The algorithm presented is based on local linear kernel smoothing, in which local neighborhoods are adapted to the local smoothness of the surface measured by the observed data. The procedure can therefore remove noise correctly in continuity regions of the surface, and preserve discontinuities at the same time.FindingsThe statistical model of removing noise and preserving edge can meet the need of edge detection in images highly corrupted by Gaussian noise.Research limitations/implicationsIt was found that when the noise ratio is higher than 40 percent, the edge detection algorithm performs poorly.Practical implicationsA very useful method for detecting highly distorted images suffering Gaussian noise.Originality/valueSince an image can be regarded as a surface of the image intensity function and such a surface has discontinuities at the outlines of objects, this algorithm can be applied directly to detect edge of image in high noise level.
This paper investigates the control issue of Euler-Lagrange systems (ELSs) subject to dynamic uncertainties and external disturbances under input and output constraints, and develops two neuroadaptive control schemes, i.e., direct neuroadaptive approximation method and indirect neuroadaptive approximation method. In the control design, a smooth saturation function called Gaussian error function is used to replace the saturation model, which is applied to solve the input saturation issue. Moreover, a new function is used to guarantee that the output does not violate the restricted boundary. The uncertain dynamic of the ELSs is reconstructed by the direct or indirect neuroadaptive method, and then a virtual-parameter learning method is proposed to reduce the computational load of control schemes. With the aid of Lyapunov stability theory, it is proven that all signals in the closed-loop control system are bounded and the tracking error of ELSs converges to zero under the proposed neuroadaptive control control schemes. The simulations on a robotic manipulator illuminate the effectiveness and preponderance of the developed neuroadaptive control schemes.INDEX TERMS Euler-Lagrange system, dynamic uncertainty, input and output constraints, neuroadaptive control.
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