# An FCM-GABPN Ensemble Approach for Material Feeding Prediction of Printed Circuit Board Template
## 5 Conclusions  
In order to enhance the accuracy of material feeding prediction of a PCB template, an ensemble predictor FCM-GABPN was proposed. In the proposed approach, the input templates were firstly clustered by FCM, and seven feature selection mechanisms were utilized to select critical attributes related to the scrap rate for each category of templates. Then, a GABPN was trained to predict the scrap rate for each category of templates, and the GABPNs for all the categories formed an ensemble predictor with a nonlinear aggregator BPN. Finally, the predicted feeding panel for each template was determined based on the predicted scrap rate with a transformation. The effectiveness and superiority were validated with many experiments based on the actual data. On the basis of the experimental results, conclusions and contributions are highlighted as follows:  
1. The accuracy of the proposed approach was better than those of the other approaches by achieving a 95.91%, 83.03%, and 89.57% reduction in _MSE_, _MAE_, and _MAPE_, respectively, over the comparison basis--manual feeding. Meanwhile, the FCM-GABPN's performance was superior to that of the other methods in the reduction of simulated surplus and/or supplemental feeding in most of the cases, by achieving a 70.16% reduction in _Surpr_Pd_ and a 31.03% reduction in _Supfr Pd_ over manual feeding.
2. The material feeding prediction of PCB template problem considering category fuzziness of samples and the diverse samples with different influence factors is different from the existing production quality prediction and optimization problem, to the best of our knowledge. The novelty of the proposed FCM-GABPN is that we fuzzily clustered samples into different categories with FCM and specified a membership threshold to adopt samples for each category. Meanwhile,component GABPN prediction model for each category was established with separately selected input attributes and GA optimized initial parameter. Furthermore, an aggregator BPN was employed to aggregate the predicted results of each GABPN by considering the membership values of each template.  
Training an ensemble predictor with many sub-models that can extract shared attributes for similar templates automatically without explicit pre-classification needs to be studied, in which we do not have to divide samples, select critical attributes for each category, and build the prediction model separately. Meanwhile, the rapid development and evolution of PCB template should also be considered. The transfer and lifelong learning may be the mechanisms worthy of attempting, in order to handle the aforementioned problem.  
[PERSON] proposed the algorithm and wrote the paper; [PERSON] and [PERSON] implemented the algorithm; [PERSON] conducted the experiments and analyzed the data; [PERSON] proposed the paper structure and wrote Section 4.  
This research is funded by National Natural Science Foundation of China with grant number 51605169 and Natural Science Foundation of Guangdong, China with grant number 2018A030310216.  
This paper is supported by the National Natural Science Foundation of China (Grant No. 51605169) and Natural Science Foundation of Guangdong, China (Grant No. 2018A030310216). The authors would like to express their appreciation to the agency. The authors wish to thank Guangzhou FastPrint Technology Co., Ltd. for providing data for the study.  
The authors declare no conflicts of interest.