Fuzzy neural network structure identification based on soft competitive learning

Article type: Research Article

Authors: Tian, Daxin | Liu, Yanheng^{; *} | Wang, Jian

Affiliations: College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, 130012, China

Correspondence: [*] Corresponding author. E-mail: [email protected]

Abstract: Fuzzy neural network combines the learning capacity of artificial neural networks with the interpretability of the fuzzy systems. A novel structure learning algorithm for fuzzy neural networks (SLNN) is presented in this paper. The neurons of SLNN are created and adapted as online learning proceeds. The learning rule of SLNN is based on Hebb as well as soft competitive learning. The soft competitive learning cannot only let SLNN be able to learn from new data but also prevent it from losing the knowledge that has been learned earlier. To obtain a concise fuzzy rule, a pruning algorithm is adopted in SLNN, which does not disobey the basic design philosophy of fuzzy system. Simulations are performed on the primary benchmarks: circle-in-the-square, two spirals apart, UCI machine learning archive's synthetic control chart time series, and KDDCUP'99 data set. Compared with fuzzy ARTMAP, BP and hierarchical neuro-fuzzy quadtree (HNFQ), the fuzzy neural network achieves higher performance.

Keywords: Fuzzy neural network, hebb rule, competitive learning, local learning

DOI: 10.3233/HIS-2007-4403

Journal: International Journal of Hybrid Intelligent Systems, vol. 4, no. 4, pp. 231-242, 2007