Generic floating point library for neuro-fuzzy controllers based on FPGA technology

This paper aims at verifying the possibility of building a faster and more accurate generic neuro-fuzzy controller, based on FPGA hardware. In order to achieve this goal, a VHDL library, combining a number of generic modules (that could fit in any controller), is introduced. Two famous neuro-fuzzy controllers, (FSOM and ANFIS), are built by integrated modules from this VHDL library. The performance of the ANFIS controller is compared to its microprocessor-based software implementation. The results showed the hardware controllers are faster and more accurate controllers. During the learning phase, offline learning algorithms (based on Matlab's neuro-fuzzy toolbox) are used to estimate the controller's structure and the values of its parameters. The learning phase teaches the hardware controller to adapt to the chosen control process and respond to it during the testing phase.