Fuzzy Flip-Flop based Neural Networks as a novel implementation possibility of multilayer perceptrons

Fuzzy Flip-Flop based Neural Networks (FNN) constructed from fuzzy D flip-flops are studied as a novel technique to implement multilayer perceptrons. The starting point of this approach is the concept of fuzzy flip-flop (F³), as the extension of the binary counterpart. Fuzzy D flip-flop based neurons are viewed, as sigmoid function generators. Their characteristic equations contain simple fuzzy operations, thus enabling easy implementability. FNNs have an interconnected fuzzy neuron structure composed from a large number of neurons acting in parallel which are capable of learning, and are suitable for function approximation. In this paper we propose the FPGA implementation of Łukasiewicz operations, furthermore of fuzzy D flip-flop neurons based on Łukasiewicz norms.