An optimised product-unit neural network with a novel PSO–BP hybrid training algorithm: Applications to load–deformation analysis of axially loaded piles

Ismail, Anis; Jeng, Dong‐Sheng; Zhang, L. L.

doi:10.1016/j.engappai.2013.04.007

Cited by 67 publications

(32 citation statements)

References 20 publications

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“…Hence, the training result easily falls into the local minimum point rather than into the global optimum; thus, the network cannot segment images precisely. To overcome this shortcoming, many researchers have proposed different methods to optimize the initial connection weights and thresholds of traditional BP neural network [1,13,24,26,33,34,59]. Intelligent evolution algorithms, such as the genetic algorithm (GA) and particle swarm optimization (PSO), have also been used to select the initial connection weights and thresholds of BP neural network.…”

Section: Introductionmentioning

confidence: 99%

“…Al-geelani [1] proposed using PSO to optimize the neural network parameters. Ismail [13] proposed coupling PSO and BP neural network to develop a robust hybrid training algorithm with both local and global search capabilities. Li Zhuo [59] proposed a simulated annealing-genetic algorithm-BP neural networkbased color correction method for TCM tongue images.…”

Section: Introductionmentioning

confidence: 99%

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A new method for image segmentation based on BP neural network and gravitational search algorithm enhanced by cat chaotic mapping

Han

Xiong

2015

Appl Intell

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This paper proposes a novel image segmentation method based on BP neural network, which is optimized by an enhanced Gravitational Search Algorithm (GSA). GSA is a novel heuristic optimization algorithm based on the law of gravity and mass interactions. It has been proven that the GSA has good ability to search for the global optimum, but it suffers from the premature convergence due to the rapid reduction of diversity. This work introduces a cat chaotic mapping into the steps of population initialization and iterative stage of the original GSA, which forms a new algorithm called CCMGSA. Then the CCMGSA is employed to optimize BP neural networks, which forms a combination method called CCMGSA-BP and we use it for image segmentation. To verify the efficiency of this method, the visual and performance experiments are done. The visual results using our proposed method are compared with those using other segmentation methods including an improved k-means clustering algorithm (I-K-means), a hybrid region merging method (H-Region-merging), and manual segmentation. The comparison results show that the proposed method can get good segmentation results on grayscale images with specific characteristics. And we compare the performance of our proposed method with those of IGSA-BP, CLPSO-BP and RGA-BP for image segmentation. The results indicate that the CCMGSA-BP shows

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new method for image segmentation based on BP neural network and gravitational search algorithm enhanced by cat chaotic mapping

Han

Xiong

2015

Appl Intell

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“…On the other hand, several researchers used the ANN approach for the development of more sophisticated and integrated systems in conjunction with other techniques such as evolutionary computation and probabilistic techniques (e.g., Boukhatem et al 2011;Alkroosh, Nikraz 2012;Ismail et al 2013;Ahangar-Asr et al 2014). Nevertheless, only a few investigations were carried out on the application of a practical technique and intelligible manipulation for data analysis before learning an ANN model which may contain redundancies and correlations between them.…”

Section: Introductionmentioning

confidence: 99%

Prediction of axial capacity of piles driven in non-cohesive soils based on neural networks approach

Benali

Boukhatem

Hussien

et al. 2017

Journal of Civil Engineering and Management

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Abstract. This paper presents an application of two advanced approaches, Artificial Neural Networks (ANN) and Principal Component Analysis (PCA) in predicting the axial pile capacity. The combination of these two approaches allowed the development of an ANN model that provides more accurate axial capacity predictions. The model makes use of Back-Propagation Multi-Layer Perceptron (BPMLP) with Bayesian Regularization (BR), and it is established through the incorporation of approximately 415 data sets obtained from data published in the literature for a wide range of uncemented soils and pile configurations. The compiled database includes, respectively 247 and 168 loading tests on largeand low-displacement driven piles. The contributions of the soil above and below pile toe to the pile base resistance are pre-evaluated using separate finite element (FE) analyses. The assessment of the predictive performance of the new method against a number of traditional SPT-based approaches indicates that the developed model has attractive capabilities and advantages that render it a promising tool. To facilitate its use, the developed model is translated into simple design equations based on statistical approaches.

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“…At each iteration, the movement of each solution considers the previous position, the environment and the previous environment position. PSO-ANN is a combination of PSO and ANN that allows to get the best out of these two powerful algorithms [15].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence to predict inhibition performance of pitting corrosion

Boukhari¹,

Boucherit²,

Zaabat³

et al. 2017

J. Fundam and Appl Sci.

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An optimised product-unit neural network with a novel PSO–BP hybrid training algorithm: Applications to load–deformation analysis of axially loaded piles

Cited by 67 publications

References 20 publications

A new method for image segmentation based on BP neural network and gravitational search algorithm enhanced by cat chaotic mapping

A new method for image segmentation based on BP neural network and gravitational search algorithm enhanced by cat chaotic mapping

Prediction of axial capacity of piles driven in non-cohesive soils based on neural networks approach

Artificial intelligence to predict inhibition performance of pitting corrosion

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