state equation with a discontinuous right-hand side

A hardware implementation design of parallelized fuzzy Adaptive Resonance Theory neural network is described and simulated. Parallel category choice and resonance are implemented in the network. Continuous-time and discrete-time winner-take-all neural circuits identifying the largest of M inputs are used as the winner-take-all units. The continuous-time circuit is described by a state equation with a discontinuous right-hand side. The discrete-time counterpart is governed by a difference equation.

A continuous-time network of K-winners-take-all (KWTA) neural circuit (NC) which is capable of identifying the largest K of N inputs, where a command signal 1 <= K < N is described. The network is described by a state equation with a discontinuous right-hand side and by an output equation. The state equation contains an impulse train defined by a sum of Dirac delta functions. Existence and uniqueness of the network work modes is analyzed. The main advantage of the network comparatively to other close analogs is widening convergence speed limitations to working modes.

A continuous-time K-winners-take-all (KWTA) neural network (NN) which is capable of identifying the largest K of N inputs, where a command signal has presented. The network is described by a state equation with a discontinuous right-hand side and by an output equation. The state equation contains an impulse train defined by a sum of Dirac delta functions. The main advantage of the network is not subject to the intrinsic convergence speed limitations of comparable designs. Application of the network for parallel rank-order filtering has described.