A proposed iteration optimization approach integrating backpropagation neural network with genetic algorithm

Huang, Han‐Xiong; Li, Jiongcheng; Xiao, Chenglong

doi:10.1016/j.eswa.2014.07.039

Cited by 106 publications

(48 citation statements)

References 39 publications

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“…Furthermore, Tzeng et al [24] constructed a hybrid method in conjunction with Taguchi orthogonal array experiments, ANOVA, RSM, BPNN, and GA to predict the quality characteristics of SGF-and PTFE-reinforced PC composites, such as ultimate strength, flexural strength, and impact resistance, and finally generate an optimal parameter setting of the injection molding process under a MIMO consideration. Recently, Huang et al [25] proposed a hybrid optimization approach integrating BPNN with embedded SA into the GA to improve its local searching ability algorithm and optimize the thickness of the blow-molded polypropylene bellows used in cars. The above approach has shown great potential in the search for the optimal process parameter settings for the die gap profile needed to achieve the desired thickness distribution in the final bellows with a minimum of experiments and so avoid getting trapped at a local optimum in complicated manufacturing processes.…”

Section: Introductionmentioning

confidence: 99%

Optimization of the plastic injection molding process using the Taguchi method, RSM, and hybrid GA-PSO

Chen

Nguyen

Chiu

et al. 2015

Int J Adv Manuf Technol

123

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This paper proposes a systematic optimization model of process parameters in plastic injection molding (PIM). Firstly, the Taguchi method is employed for experimentation and data analysis, in which the quality characteristics for the plastic injection product are length and warpage. The control factors for the process are melt temperature, injection velocity, packing pressure, packing time, and cooling time. Moreover, the signal-to-noise (S/N) ratio and analysis of variance (ANOVA) are used to obtain a combination of parameter settings. Experimental data are set for the response surface methodology (RSM) in order to analyze and create two quality predictors and two S/N ratio predictors. The two quality predictors are associated with genetic algorithms (GA) to search for an optimal combination of process parameters that meets multiple-objective quality characteristics. Finally, four predictors are combined with the hybrid GA-PSO to find the final optimal combination of process parameters. The confirmation results show that the proposed model not only enhances the stability in the injection molding process, including the quality in length and warpage, but also reduces the costs of and time spent in the PIM process.

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Section: Introductionmentioning

confidence: 99%

Optimization of the plastic injection molding process using the Taguchi method, RSM, and hybrid GA-PSO

Chen

Nguyen

Chiu

et al. 2015

Int J Adv Manuf Technol

123

View full text Add to dashboard Cite

show abstract

“…Sathiya et al (2009) optimised friction welding parameters using ANN and three evolutionary computational techniques: genetic algorithm (GA), simulated annealing (SA), and particle swarm optimisation (PSO), where GA outperformed the other two metaheuristic search methods. Recently, Huang et al (2015) designed an iteration approach based on ANN, GA and SA to optimise the thickness of blow moulded bellows. The approached utilised BP ANN with the Bayesian regularisation for modelling, and GA combined with the elitist strategy and SA (to improve a local search) for the actual optimisation, considering a single response.…”

Section: Multiresponse Optimisation Based On Genetic Algorithmmentioning

confidence: 99%

Review of Multiresponse Process Optimisation Methods

Šibalija

Majstorovic

2015

Advanced Multiresponse Process Optimisation

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The review of methods used for multiresponse process parameter design and similar multiobjective optimisation problems is discussed in this chapter, implying the following classification: (1) conventional methods based on statistical or mathematical techniques: (i) experimental design techniques (response surface methodology, Taguchi's robust parameter design and related approaches), and (ii) iterative mathematical search techniques; (2) non-conventional methods based on artificial intelligence techniques: (i) fuzzy logic, (ii) artificial neural networks, (iii) metaheuristic search techniques (genetic algorithm, simulated annealing, particle swarm optimisation, ant colony optimisation, tabu search, and recent evolutionary algorithms such as artificial bee colony algorithm, biogeography-based optimisation, and teaching-learning-based optimisation), and (iv) expert systems.

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“…There are m nodes in the input layer, while the hidden layer has n wavelet bases and only one output. WNN not only converges quickly, but also can avoid local optima because of its strong learning and generation capacity [32]. The experimental parameters of WNN in this study are shown in Table 4.…”

Section: Wavelet Neural Network (Wnn)mentioning

confidence: 99%

A Novel Combined Model Based on an Artificial Intelligence Algorithm—A Case Study on Wind Speed Forecasting in Penglai, China

2016

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Abstract:Wind speed forecasting plays a key role in wind-related engineering studies and is important in the management of wind farms. Current forecasting models based on different optimization algorithms can be adapted to various wind speed time series data. However, these methodologies cannot aggregate different hybrid forecasting methods and take advantage of the component models. To avoid these limitations, we propose a novel combined forecasting model called SSA-PSO-DWCM, i.e., particle swarm optimization (PSO) determined weight coefficients model. This model consisted of three main steps: one is the decomposition of the original wind speed signals to discard the noise, the second is the parameter optimization of the forecasting method, and the last is the combination of different models in a nonlinear way. The proposed combined model is examined by forecasting the wind speed (10-min intervals) of wind turbine 5 located in the Penglai region of China. The simulations reveal that the proposed combined model demonstrates a more reliable forecast than the component forecasting engines and the traditional combined method, which is based on a linear method.

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A proposed iteration optimization approach integrating backpropagation neural network with genetic algorithm

Cited by 106 publications

References 39 publications

Optimization of the plastic injection molding process using the Taguchi method, RSM, and hybrid GA-PSO

Optimization of the plastic injection molding process using the Taguchi method, RSM, and hybrid GA-PSO

Review of Multiresponse Process Optimisation Methods

A Novel Combined Model Based on an Artificial Intelligence Algorithm—A Case Study on Wind Speed Forecasting in Penglai, China

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