Abstract
A new approach is proposed to fit complex nonlinear function by using BRB system in this paper. BRB system is a knowledge-based system instead of a black box, but its parameters can be trained with an optimization framework. Therefore, it combines the advantage of the knowledge-based method and data-driven method, so it can not only overcome the inaccuracy of expert knowledge but also overcome the overfitting problem of data-driven method. An experiment is implemented to compare the performance of BRB system and neural network when fitting a nonlinear system, and the results indicate that BRB system performs much better than neural network when training data is insufficient.
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Tang, X., Xiao, M., Hu, B., Gao, C. (2020). Fitting Complex Nonlinear Function with Belief Rule Base. In: Jain, V., Patnaik, S., Popențiu Vlădicescu, F., Sethi, I. (eds) Recent Trends in Intelligent Computing, Communication and Devices. Advances in Intelligent Systems and Computing, vol 1006. Springer, Singapore. https://doi.org/10.1007/978-981-13-9406-5_19
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DOI: https://doi.org/10.1007/978-981-13-9406-5_19
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