Parameter Optimization of Interval Type-2 Fuzzy Neural Networks Based on PSO and BBBC Methods

Jiajun Wang; Tufan Kumbasar

doi:10.1109/JAS.2019.1911348

Volume 6 Issue 1

Jan. 2019

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2019 > 6(1): 247-257

Jiajun Wang and Tufan Kumbasar, "Parameter Optimization of Interval Type-2 Fuzzy Neural Networks Based on PSO and BBBC Methods," IEEE/CAA J. Autom. Sinica, vol. 6, no. 1, pp. 247-257, Jan. 2019. doi: 10.1109/JAS.2019.1911348

Citation:

Jiajun Wang and Tufan Kumbasar, "Parameter Optimization of Interval Type-2 Fuzzy Neural Networks Based on PSO and BBBC Methods," IEEE/CAA J. Autom. Sinica, vol. 6, no. 1, pp. 247-257, Jan. 2019. doi: 10.1109/JAS.2019.1911348

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Parameter Optimization of Interval Type-2 Fuzzy Neural Networks Based on PSO and BBBC Methods

doi: 10.1109/JAS.2019.1911348

Jiajun Wang^{1
,
,},
Tufan Kumbasar²

1.
School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
2.
Control and Automation Engineering Department, Faculty of Electrical and Electronics Engineering, Istanbul Technical University, TR-34469 Istanbul, Turkey

Funds:

the National Natural Science Foundation of China 61873079

the National Natural Science Foundation of China 51707050

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Abstract

Abstract

Interval type-2 fuzzy neural networks (IT2FNNs) can be seen as the hybridization of interval type-2 fuzzy systems (IT2FSs) and neural networks (NNs). Thus, they naturally inherit the merits of both IT2FSs and NNs. Although IT2FNNs have more advantages in processing uncertain, incomplete, or imprecise information compared to their type-1 counterparts, a large number of parameters need to be tuned in the IT2FNNs, which increases the difficulties of their design. In this paper, big bang-big crunch (BBBC) optimization and particle swarm optimization (PSO) are applied in the parameter optimization for Takagi-Sugeno-Kang (TSK) type IT2FNNs. The employment of the BBBC and PSO strategies can eliminate the need of backpropagation computation. The computing problem is converted to a simple feed-forward IT2FNNs learning. The adoption of the BBBC or the PSO will not only simplify the design of the IT2FNNs, but will also increase identification accuracy when compared with present methods. The proposed optimization based strategies are tested with three types of interval type-2 fuzzy membership functions (IT2FMFs) and deployed on three typical identification models. Simulation results certify the effectiveness of the proposed parameter optimization methods for the IT2FNNs.
- Big bang-big crunch (BBBC),
- interval type-2 fuzzy neural networks (IT2FNNs),
- parameter optimization,
- particle swarm optimization (PSO)

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References

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Parameter Optimization of Interval Type-2 Fuzzy Neural Networks Based on PSO and BBBC Methods

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