Realization of an Improved Adaptive Neuro-Fuzzy Inference System in DSP

  • Authors:

  • Xingxing Wu;Xilin Zhu;Xiaomei Li;Haocheng Yu

  • Affiliations:

  • College of Mechanical science and Engineering, Jilin University, Changchun 130025, starglare@126.com, China;College of Mechanical science and Engineering, Jilin University, Changchun 130025, starglare@126.com, China;College of Mechanical science and Engineering, Jilin University, Changchun 130025, starglare@126.com, China;College of Mechanical science and Engineering, Jilin University, Changchun 130025, starglare@126.com, China

  • Venue:
  • ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
  • Year:
  • 2007

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Abstract

Scaled conjugate gradient (SCG) algorithm was used to improve adaptive neuro-fuzzy inference system (ANFIS). It's proved by applications in chaotic time-series prediction that the improved ANFIS converges with less time and fewer iterations than standard ANFIS or ANFIS improved with the Fletcher-Reeves update method. The way in which ANFIS could be improved on the basis of standard algorithm using fuzzy logic toolbox of MATLAB is dwelled on. A convenient method to realize ANFIS in TI 's digital signal processor (DSP) TMS320C5509 is presented. Results of experiments indicate that output of ANFIS realized in DSP coincides with that in MATLAB and validate this method.