CONVOLUTIONAL EXPERT NEURAL SYSTEM (CONEXNS)

Based on the recognition that transfer functions of Boolean completeness can be expressed as a domain within a periodic function, an architecture of a artificial neuron which is fully generalized in application and capable of rapid learning with minimal memory requirements while maintaining content addressability of memory, is provided. Full functionality of this architecture is demonstrated for an input vector containing two values. Extension to three variables shows the potential for generality of this architecture to N-valued input vectors.