Abstract
Abstract Packet sets and inverse packet sets are two kinds of novel mathematical tools to analyze dynamic information systems. With advances in inverse packet sets, random inverse packet information is proposed by introducing random characteristics into inverse packet sets. Hence, random inverse packet information has dynamic and random characteristics, and is an extended form of inverse packet sets. Furthermore, random feature, dynamic feature, and identification relation about the random inverse packet information are discussed. Finally, based on the above theory, an instance is used to illustrate the applications of intelligent acquisition of investment information.
Highlights
With regard to finite common set theory with static feature, research on some dynamic systems often faced problems because change always exists
Literatures [5,6,7,8,9,10,11,12,13,14,15,16,17] developed the latter model by taking information instead of sets to obtain the inverse packet information (IPI) model and provide some applications for information fusion– separation, hidden information discovery, intelligent data digging, and big decomposition–fusion acquisition
For inner IPI (x)F, the dynamic process is shown by adding information elements under the condition that some attributes are migrated into α, as ∃wi ∈/ (x), f = xi ∈ (x), where (x)F indicates (x) ∪ {xi|wi ∈/ (x), f = xi ∈ (x)}
Summary
With regard to finite common set theory with static feature, research on some dynamic systems often faced problems because change always exists. [1,2,3,4] proposed two types of dynamic set models-packet sets and inverse packet sets (IPSs), by replacing “static” with “dynamic” to improve the finite common set. These dynamic set models provide a better theory foundation for dealing with dynamic applied systems. The random feature, dynamic characteristics, and identification relations on RIPI are discussed and applied to intelligent acquisition–separation of investment information.
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