函数逆P-集合与信息规律融合

利用逆P-集合,提出函数逆P-集合。函数逆P-集合是把函数概念引入到逆P-集合内,改进逆P-集合得到的。函数逆P-集合具有动态特征和规律(函数)特征。函数逆P-集合是由函数内逆P-集合S珔F与函数外逆P-集合S珔F珔构成的函数集合对;或者,(S珔F,珔SF珔)是函数逆P-集合。在一定条件下,函数逆P-集合(S珔F,S珔F珔)被还原成有限普通函数集合S。逆P-集合是把动态特征引入到有限普通集合X内(Cantor set X),改进有限普通集合X被提出的。函数逆P-集合具有与函数P-集合相反的动态特征、规律(函数)特征。本文给出函数逆P-集合的结构、还原和它的函数等价类特征。利用数据拆分-合成原理,给出逆P-信息规律融合与它的生成;给出逆P-信息规律融合的属性特征与属性定理。利用这些结果,给出逆P-信息规律融合生成的隐形信息图像与它的应用。函数逆P-集合与函数P-集合是两个独立的、特征不同的新模型。

Function inverse packet sets and information law fusion

Based on inverse P-sets(inverse packet sets),function inverse P-sets is presented.Function inverse P-sets is obtained by introducing function concept into inverse P-sets and improving it,which has dynamic characteristic and law(funciton) characteristic.Function inverse P-sets is a funcion set pair composed of function internal inverse P-set F(function internal inverse packet set F) and function outer inverse P-set(function outer inverse packet set),or(F,) is function inverse P-sets.Under a certain condition,function inverse P-sets(F,) can be restored to finite general function set S.Inverse P-sets is obtained by embedding dynamic characteristic into finite general set X and improving it.Function inverse P-sets has the opposite dynamic characteristic and law(function) characteristic to function P-sets(function packet sets).Function inverse P-sets is a novel theory and method of studying another class of dynamic information law characteristic,while function P-sets is applied to study the first class of dynamic information law characteristic.The paper gives the structure,the resduction and function eqivalent class characteristic of function inverse P-sets.Using the data separation-composition principle,inverse P-information law fusion and its generation,the attribute characteristic and attribute theorem of inverse P-information law fusion are presented.Using the above results,the hidden information image generated by inverse P-information law fusion and its application are given.Function inverse P-sets and function P-sets are two novel models with mutual independence and different characteristic.