In Q-learning, taking an action (a_{i} in A) of the ith SU source to a given state (S_{i}) (in) ∑ is based on Q-table expressed as ∑ by (A) matrix, and the decision rule of the action is expressed as the Eq. (2).
The seven management and quality tools, i.e. affinity diagrams, tree diagrams, relations diagrams, matrices and tables, process decision program charts, AHP, and blueprinting are employed to analyse and categorise those information as primary, secondary and tertiary.
The line graphs can be plotted for fuzzy decision matrix, normalized fuzzy decision matrix and weighted normalized fuzzy decision matrix.
If any null hypothesis was rejected (i.e., noninferiority was established), the corresponding superiority hypothesis was tested using the Hommel test (8) and decision matrix algorithm (9).
These two processes, made computationally operational through the development of self-transition and decision effect matrices, are then combined into a joint transition matrix, with which we can model the effects of maintenance actions on bridge performance and costs.
Relied on the use of auxiliary matrices and a positive scalar decision variable, a novel necessary and sufficient condition of positive realness is first derived in terms of a strict matrix inequality for linear time-invariant discrete-time singular systems.
An abstraction hierarchy, a series of cross-linked matrices, and a set of decision ladder models were developed to provide a principled mapping between system function decompositions produced by system engineering teams and cognitive tasks, information needs, automation requirements, and concepts for displays.
After that, all solutions are formulating as augmented matrix consisting of the condition and decision attributes ([{x_{i1},ldots},x_{in}x_{in}|{{D}}}]_{i=1}^{mathrm{PS}}), where D denotes decision attribute that takes 1 or 0 value.
This category of studies focuses on prioritizing service quality attributes using different decision-making matrices and measurements with alternatives and attributes (Chen 2011; Lin 2010).
Discrimination was tested by 2 × 2 classification matrices at decision criteria of 10%, 30%, and 50%.
