We prove that, determines a sequence of positive fuzzy numbers.
The operations sequencing sub-problem determines a sequence of operations on all the machines.
If no fixed point is found along the way, the process determines a sequence of telescopic squares which uniquely identifies a point.
(18) Therefore, (13), (18) and since (L_{n,alpha}), (R_{n,alpha}) are left continuous, we have that ([L_{n,alpha},R_{n,alpha}]) determines a sequence of positive fuzzy numbers (x_{n}) such that (11) holds.
As in statement (i), using (3.10), (3.11), (3.15)–(3.24), (3.24), Theorem 2.1 and working inductively, we get that the positive solution,,, of (3.17), determines a sequence of positive fuzzy numbers, such that (3.27) holds.
Therefore, since,, are left continuous and (3.22), (3.23), (3.25), (3.26) are satisfied, from Theorem 2.1, we get that the positive solution,,, of (3.17), with initial values,,, satisfying (3.11), (3.12), (3.14), determines a sequence of positive fuzzy numbers, such that (3.27).
In sequence recognition, we attempt to determine a sequence of events from a sequence of observations.
In step 1, we determine a sequence of transactions by improving the total utility iteratively.
More formally, we are given a sequence of observations and are required to determine a sequence of labels, that is, the sequence,, with maximum posterior probability.
In the subject of AI, planning refers to determining a sequence of actions that are known to achieve a particular objective when performed.
Some algorithms, such as GSR [13] and GPCR [14], determine a sequence of intersections to connect the source and the destination using the Dijkstra algorithm.
