Analog Implementation of Neo-Fuzzy Neuron and Its On-board Learning

A hardware implementation of the neo-fuzzy neuron with the learning mechanism by the analog technology and its application to the on-board real-time prediction of time series are described. A neo-fuzzy neuron (NFN) is proposed for a learning machine of non-linear relations and dynamics. The NFN is produced by a fusion of the fuzzy logic and the neuroscience. The NFN describes the non-linearity of a system with a linear conjunction of the non-linear functions which is described by the fuzzy if-then rules. Its advantages are the high-speed learning exceeds more than 100 times of that of a conventional multi-layer neural networks and the guarantee for the convergence to the global minimum on the error-weight space. In this paper, the NFN hardware system with the learning mechanism can be implemented by using the analog fuzzy inference chip developed by the authors and discrete components. The mechanism for weight updating in learning can be implemented to the simple circuit by using the characteristics of MOS transistor. The operation speed faster than 1 has been achieved. The performance of the proposed hardware has been confirmed by the experimental results of the on-board prediction of time series. Its efficient learning ability is shown for the adaptive estimation of signals that are generated by non-linear dynamical systems. The maximum error of the on-board prediction of one-step ahead is under several percentages in full scale of input range. Furthermore, the NFN acquires system dynamics of unknown systems as linguistic rules. Key-Words: analog hardware, prediction, on-board learning, fuzzy system, neural networks, adaptation, global minimum, high-speed learning IMACS/IEEE CSCC'99 Proceedings, Pages:4401-4406