Neural LS estimator with a non-quadratic energy function
Least-squares (LS) estimation with a standard feedback neural network (SFBNN) which is based on an electrical model is investigated. In the energy function of a SFBNN, a non-quadratic term is included which is often neglected while solving an optimization problem. It is shown that the non-quadratic term affects the solution of a continuous optimization problem. Properties of the non-quadratic term and the relation between the estimation error and several parameters of the SFBNN are discussed. A technique, called extended space iterative search (ESIS), is introduced to reduce the estimation error. Simulation results are presented to confirm the analysis result and the effectiveness of the proposed technique.<<ETX>>