Discrete-time ZNN algorithms for time-varying linear matrix-vector inequality solving

By following Zhang et al.'s design method, a special class of recurrent neural network termed Zhang neural network (ZNN) has been proposed for online solution of time-varying linear inequalities. For the purpose of digital-hardware implementation, the resultant ZNN model is discretized by employing Euler difference rule in this paper. Thus, three discrete-time ZNN models and numerical algorithms (i.e., discrete-time ZNN algorithms, in short) are proposed and investigated for online solution of time-varying linear matrix-vector inequalities. In addition, a criterion is proposed to measure the rapidity and accuracy of the proposed discrete-time ZNN algorithms. Numerical-study results further verify and demonstrate the efficacy of the proposed discrete-time ZNN algorithms for online solution of time-varying linear matrix-vector inequalities.