In this paper, we formulate the manipulator Jacobian matrix in a probabilistic framework based on the random matrix theory (RMT). Due to the limited available information on the system fluctuations, the parametric approaches often prove to be inadequate to appropriately characterize the uncertainty. To overcome this difficulty, we develop two RMTbased probabilistic models for the Jacobian matri...

For highly sensitive real-world predictive analytic applications such as healthcare and medicine, having good prediction accuracy alone is often not enough. These kinds of applications require a decision making process which uses uncertainty estimation as input whenever possible. Quality of uncertainty estimation is a subject of over or under confident prediction, which is often not addressed i...

the main task of this work is related with the design of a class of siso robust control law for theregulation of substrate concentration (cdo) of an industrial activated sludge wastewater plant. the controldesign is related with an uncertainty estimator (reduced order observer) based active control. departing from the tracking error between the desired and the current substrate concentrations t...

the main task of this work is related with the design of a class of siso robust control law for theregulation of substrate concentration (cdo) of an industrial activated sludge wastewater plant. the controldesign is related with an uncertainty estimator (reduced order observer) based active control. departing from the tracking error between the desired and the current substrate concentrations t...

This paper examines reasoning under uncertainty in the case where the AI reasoning mechanism is itself subject to random error or noise in its own processes. The main result is a demonstration that systematic, directed biases naturally arise if there is random noise in a reasoning process that follows the normative rules of probability theory. A number of reliable errors in human reasoning unde...