An interval type-2 neural fuzzy chip with on-chip incremental learning ability for time-varying data sequence prediction and system control.

Abstract

This paper proposes a new circuit to implement a Mamdani-type interval type-2 neural fuzzy chip with on-chip incremental learning ability (IT2NFC-OL) for applications in changing environments. Traditional interval type-2 fuzzy systems use an iterative procedure to find the system outputs, which is computationally expensive, especially for hardware implementation. To address this problem, the IT2NFC-OL uses a simplified type reduction operation to reduce the hardware implementation cost without degrading the learning performance. The software-implemented IT2NFC-OL is characterized by online structure learning and parameter learning using a gradient descent algorithm. The learned fuzzy model is then implemented in a field-programmable gate array (FPGA) chip. The FPGA-implemented IT2NFC-OL performs not only fuzzy inference but also online consequent parameter learning for applications in changing environments. Novel circuits for the computation of system outputs and the update of interval consequent values are proposed. The learning performance of the software-implemented IT2NFC-OL and the on-chip learning ability are verified with applications to time-varying data sequence prediction and system control problems and by comparisons with different software-implemented type-1 and type-2 neural fuzzy systems and interval type-2 fuzzy chips.