Switched-capacitor artificial neural networks for nonlinear optimization with constraints

Switched capacitor (SC) architectures for online solving of nonlinear optimization problems are proposed, and their properties are investigated. The proposed circuit structures are suitable for VLSI MOS implementations since they use switched-capacitor techniques. The structures exhibit a high degree of modularity, and a relatively small number of basic building blocks (computing cells) are required to implement many effective and powerful optimization algorithms. Basic mathematical operations, e.g. multiplication, addition, and nonlinear scaling transformation, are accomplished using advanced SC techniques. The validity and performance of the circuit structures are illustrated by intensive computer simulations using TUTSIM and NAP programs