Abstract
Neuro-fuzzy systems have produced high accuracy in modeling numerous real-world applications. However, the in-built computational complexity and curse of dimensionality often cease opportunities of implementations in applications with large input size. This is also true with adaptive neuro-fuzzy inference system (ANFIS) as mostly the applications in literature are with small input size. The five-layer architecture of ANFIS is modified in this paper to reduce computational cost. For effective parameters training, the popular swarm-based metaheuristic algorithm Artificial Bee Colony (ABC) algorithm is employed after modification for enhanced convergence ability. The proposed ABC variant eliminates scout bees, hence called ABC-Scoutless, outperforms standard ABC and particle swarm optimization (PSO) on benchmark test functions. The modified ANFIS trained by ABC-Scoutless performs equally better as standard ANFIS on benchmark classification problems with different input range, but with less computational cost due to reduced number of trainable parameters.
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Acknowledgment
The authors would like to thank Universiti Tun Hussein Onn Malaysia (UTHM) for supporting this research under Postgraduate Incentive Research Grant, Vote No. U560.
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Salleh, M.N.M., Hassan, N., Hussain, K., Talpur, N., Cheng, S. (2019). Modified Adaptive Neuro-Fuzzy Inference System Trained by Scoutless Artificial Bee Colony. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication Networks. FICC 2018. Advances in Intelligent Systems and Computing, vol 887. Springer, Cham. https://doi.org/10.1007/978-3-030-03405-4_28
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