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Materials 2015, 8(1), 117-136; doi:10.3390/ma8010117

The Bi-Directional Prediction of Carbon Fiber Production Using a Combination of Improved Particle Swarm Optimization and Support Vector Machine

1
College of Information Sciences and Technology, Donghua University, Shanghai 201620, China
2
Engineering Research Center of Digitized Textile & Apparel Technology, Ministry of Education, Shanghai 201620, China
*
Author to whom correspondence should be addressed.
Academic Editor: Geminiano Mancusi
Received: 20 September 2014 / Accepted: 19 December 2014 / Published: 30 December 2014
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Abstract

This paper creates a bi-directional prediction model to predict the performance of carbon fiber and the productive parameters based on a support vector machine (SVM) and improved particle swarm optimization (IPSO) algorithm (SVM-IPSO). In the SVM, it is crucial to select the parameters that have an important impact on the performance of prediction. The IPSO is proposed to optimize them, and then the SVM-IPSO model is applied to the bi-directional prediction of carbon fiber production. The predictive accuracy of SVM is mainly dependent on its parameters, and IPSO is thus exploited to seek the optimal parameters for SVM in order to improve its prediction capability. Inspired by a cell communication mechanism, we propose IPSO by incorporating information of the global best solution into the search strategy to improve exploitation, and we employ IPSO to establish the bi-directional prediction model: in the direction of the forward prediction, we consider productive parameters as input and property indexes as output; in the direction of the backward prediction, we consider property indexes as input and productive parameters as output, and in this case, the model becomes a scheme design for novel style carbon fibers. The results from a set of the experimental data show that the proposed model can outperform the radial basis function neural network (RNN), the basic particle swarm optimization (PSO) method and the hybrid approach of genetic algorithm and improved particle swarm optimization (GA-IPSO) method in most of the experiments. In other words, simulation results demonstrate the effectiveness and advantages of the SVM-IPSO model in dealing with the problem of forecasting. View Full-Text
Keywords: support vector machine; particle swarm optimization; intelligent computing; cell communication mechanism; carbon fiber; bi-directional prediction support vector machine; particle swarm optimization; intelligent computing; cell communication mechanism; carbon fiber; bi-directional prediction
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Xiao, C.; Hao, K.; Ding, Y. The Bi-Directional Prediction of Carbon Fiber Production Using a Combination of Improved Particle Swarm Optimization and Support Vector Machine. Materials 2015, 8, 117-136.

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