This paper presents a novel approach of permeability prediction by combining cuckoo, particle swarm and imperialist competitive algorithms with Levenberg-Marquardt (LM) neural network algorithm in one of heterogeneous...
详细信息
This paper presents a novel approach of permeability prediction by combining cuckoo, particle swarm and imperialist competitive algorithms with Levenberg-Marquardt (LM) neural network algorithm in one of heterogeneous oil reservoirs in Iran. First, topology and parameters of the Artificial Neural Network (ANN) as decision variables were designed without the optimization method. Then, in order to improve the effectiveness of forecasting when ANN was applied to a permeability predicting problem, the design was performed using cuckoo optimization algorithm (COA) algorithm. The validation test result from a new well data demonstrated that the trained COA-LM neural model can efficiently accomplish permeability prediction. Also, the comparison of COA with particle swarm optimization and imperialist competitive algorithms showed the superiority of COA on fast convergence and best optimum solution achievement. (C) 2013 Elsevier B.V. All rights reserved.
In this study, the cuckoo optimization algorithm was used to design a fuzzy logic model for determination of the compressive strength of 28-day-old concrete. Experimental results from 50 concrete mixtures were loaded ...
详细信息
In this study, the cuckoo optimization algorithm was used to design a fuzzy logic model for determination of the compressive strength of 28-day-old concrete. Experimental results from 50 concrete mixtures were loaded into the fuzzy logic model for training, and the model was then optimized by use of the cuckoo optimization algorithm. Input variables of the fuzzy logic model are water-to-cement weight ratio and coarse aggregate-to-fine aggregate weight ratio;the output variable is concrete compressive strength. The results obtained from the optimized model were compared with those from an adaptive neuro fuzzy inference system model;they were also validated experimentally.
Spatial image resolution explains about the pixel density in a digital image. As a result more the number of pixels more detailed visibility of information contained in the image. Hardware limitations restrict the inc...
详细信息
ISBN:
(纸本)9781479922741;9781479922758
Spatial image resolution explains about the pixel density in a digital image. As a result more the number of pixels more detailed visibility of information contained in the image. Hardware limitations restrict the increase in number of sensor elements per unit area in camera. Therefore an imaging system with inadequate sensor array will generate low resolution image which causes pixelization effect in them. This problem is solved in software level using signal processing techniques called super resolution based image reconstruction. In this paper super resolution based image reconstruction problem is addressed, which is used for resolution enhancement. Unlike interpolation, it takes information from multiple number of low resolution images with sub-pixel shifts and contain non-redundant data to generate a high resolution image. In this proposed reconstruction method, a hybrid iterative back projection technique is developed exploiting the notion of cuckoo search optimizationalgorithm in iterative back projection method. The high resolution solution from iterative back projection method is optimized using cuckoo optimization algorithm. The performance of the proposed algorithm is found to be outperforming that of existing IBP and other interpolation based reconstruction techniques.
Spatial image resolution explains about the pixel density in a digital image. As a result more the number of pixels more detailed visibility of information contained in the image. Hardware limitations restrict the inc...
详细信息
ISBN:
(纸本)9781479922765
Spatial image resolution explains about the pixel density in a digital image. As a result more the number of pixels more detailed visibility of information contained in the image. Hardware limitations restrict the increase in number of sensor elements per unit area in camera. Therefore an imaging system with inadequate sensor array will generate low resolution image which causes pixelization effect in them. This problem is solved in software level using signal processing techniques called super resolution based image reconstruction. In this paper super resolution based image reconstruction problem is addressed, which is used for resolution enhancement. Unlike interpolation, it takes information from multiple number of low resolution images with sub-pixel shifts and contain non-redundant data to generate a high resolution image. In this proposed reconstruction method, a hybrid iterative back projection technique is developed exploiting the notion of cuckoo search optimizationalgorithm in iterative back projection method. The high resolution solution from iterative back projection method is optimized using cuckoo optimization algorithm. The performance of the proposed algorithm is found to be outperforming that of existing IBP and other interpolation based reconstruction techniques.
The dynamics of fractional-order systems have attracted increasing attention in recent years. In this paper a novel fractional-order hyperchaotic system with a quadratic exponential nonlinear term is proposed and the ...
详细信息
The dynamics of fractional-order systems have attracted increasing attention in recent years. In this paper a novel fractional-order hyperchaotic system with a quadratic exponential nonlinear term is proposed and the synchronization of a new fractional-order hyperchaotic system is discussed. The proposed system is also shown to exhibit hyperchaos for orders 0.95. Based on the stability theory of fractional-order systems, the generalized backstepping method (GBM) is implemented to give the approximate solution for the fractional-order error system of the two new fractional-order hyperchaotic systems. This method is called GBM because of its similarity to backstepping method and more applications in systems than it. Generalized backstepping method approach consists of parameters which accept positive values. The system responses differently for each value. It is necessary to select proper parameters to obtain a good response because the improper selection of parameters leads to inappropriate responses or even may lead to instability of the system. Genetic algorithm (GA), cuckoo optimization algorithm (COA), particle swarm optimizationalgorithm (PSO) and imperialist competitive algorithm (ICA) are used to compute the optimal parameters for the generalized backstepping controller. These algorithms can select appropriate and optimal values for the parameters. These minimize the cost function, so the optimal values for the parameters will be found. The selected cost function is defined to minimize the least square errors. The cost function enforces the system errors to decay to zero rapidly. Numerical simulation results are presented to show the effectiveness of the proposed method.
暂无评论