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This study addresses the blind image deconvolution which uses only blurred image and less point spread function (PSF) information to restore the original image. To identify the blind image it is a very important step for restoring the image. Therefore, the first step is to look for PSF model. In this paper, particle swarm optimization (PSO) is utilized to seek the unknown PSF. The objective function...
Aim to the problems of fuzzy neural network depending on the initial conditions, the training time long, and being easy to fall into local minimum, this paper studied the combination of particle swarm optimization and fuzzy neural network, presented a fuzzy neural network model based on particle swarm optimization, and applied it in speech recognition system. This model improved the convergence speed...
This study addresses a blind image deconvolution which uses only blurred image and tiny point spread function (PSF) information to restore the original image. In order to mitigate the problem trapping into a local solution in conventional algorithms, the evolutionary learning is reasonably to apply to this task. In this paper, particle swarm optimization (PSO) is therefore utilized to seek the unknown...
In multi-objective particle swarm optimization (MOPSO) methods, selecting the best local guide (the global best particle) for each particle of the population provides great benefits on the convergence and diversity of solutions, especially when problems are optimized with a large number of objectives. This paper introduces the proportional distribution based particle swarm optimizer (PSO) with cross-over...
The purpose of this paper is to develop particle swarm optimizer (PSO) based controller for vehicle navigation system (VNS). This paper regards the mathematical model of car-like mobile robot as the vehicle dynamics behavior to develop the controller for VNS. VNS controls the vehicle via two actual control values, one is the degree variation of the front wheel and the other is the acceleration variation...
Recently, the research which based on wavelet representation to reduce noise has gotten a lot of attention. The most typical method, universal threshold proposed by Donoho, and its derivative methods have verified their efficiency on varied applications. Some settings which are signal-dependence are critical; however, they were usually given by trial and error or a rough estimate in existing algorithms...
The purpose of this paper is to search the best flight route efficiently for unmanned aerial vehicle (UAV) in the 3-dimention complicated topography. The proposed method for the best flight route is mainly utilizing evolutionary algorithm, and give the proper initial population of evolutionary algorithm through skeletonization, efficient pre-processing procedure. In order to provide a smooth flight...
Noise reduction problem is addressed by this study. Recently, wavelet thresholding has become popular and has gotten much attention among a number of de-noisy approaches. The most of threshold determination are developed from universal method proposed by Donoho. But, some shortcomings of the determination are caused from several incorrectly estimated factors and the lack of adaptability for whole...
As more and more real-world optimization problems become increasingly complex, algorithms with more capable optimizations are also increasing in demand. For solving large scale global optimization problems, this paper presents a variation on the traditional PSO algorithm, called the efficient population utilization strategy for particle swarm optimizer (EPUS-PSO). This is achieved by using variable...
The main difference between an original PSO (single-objective) with a multi-objective PSO (MOPSO) is the local guide (global best solution) distribution must be redefined in order to obtain a set of non-dominated solutions (Pareto front). In MOPSO, the selection of local guide for particles will direct affect the performance of finding Pareto optimum. This paper presents a local guide assignment strategy...
A very important step for the RBF network training is to decide a proper number of hidden nodes. This paper proposes a PSO-based algorithm for searching optimal cluster distance factor. Thus, the self-growing RBF network training algorithm can be realized by employing the optimal cluster distance factor to create hidden neurons automatically from the input data set. Two experiments that include function...
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