Traditional data envelopment analysis (DEA) models evaluate two-stage decision making unit (DMU) as a black box and neglect the connectivity may exist among the stages. This paper looks inside the system by considering the intermediate activities between the stages where the first stage uses inputs to produce outputs which are the inputs to the second stage along with its own inputs. Additional...

Two-time-scale stochastic approximation, a generalized version of the popular has found broad applications in many areas including control, optimization, and machine learning. Despite its popularity, theoretical guarantees this method, especially finite-time performance, are mostly achieved for linear case while results nonlinear counterpart very sparse. Motivated by classic control theory sing...

in this paper, the problem of decentralized model reference adaptive control (mrac) for a class of large scale systems with time varying delay in interconnected term and input and state delays is studied. to compensate the effect of input delay indirectly, a smith predictor built on. to handle the effects of the time delays in input, the adaptive controller part includes two auxiliary dynamic f...

Data envelopment analysis (DEA) is a non-parametric approach for performance analysis of decision making units (DMUs) which uses a set of inputs to produce a set of outputs without the need to consider internal operations of each unit. In recent years, there have been various studies dealt with two-stage production systems, i.e. systems which consume some inputs in their first stage to produce ...