Development of higher-order neural units for control and pattern recognition

The computational neural-network structures described in the literature are often based on the notion of linear neural units (LNUs). The biological neurons consist of complex computing elements, which perform more computations than just linear summation. The computational efficiency of the neural networks depends on their structure and the training methods employed. Higher-order combinations of inputs and weights will yield higher neural performance. In this paper, a quadratic-neural unit (QNU) has been developed using a novel general matrix form of the quadratic operation. We have used the QNU for realizing different logic circuits.