interval, i.e. − *yxnormT* [ ][ ]→× [ 1,01,01,0:),( ] and − *yxconormT* [ ][ ] [ ] →× 1,01,01,0:),( . They are monotonic, commutative and associative. TCN rule takes its source from the t-norm and t-conorm operators in fuzzy logics, where the *and* logic operator corresponds in information fusion to the conjunctive rule and the or logic operator corresponds to the disjunctive rule. In this work we propose to interpret the fusion/association between the sources of information as a vague relation, characterized with the following two characteristics: 
- *The way of association between the possible propositions, built on the base of the frame of* discernment. It is based on union and intersection operations, and their combinations. These sets' operations correspond to logic operations conjunction and disjunction and their combinations. 

- *The degree of association between the propositions.* It is obtained as a t-norm (for conjunction) or tconorm (for disjunction) operators applied over the probability masses of corresponding focal elements. While the logic operators deal with degrees of truth and false, the fusion rules deal with degrees of belief of hypotheses. We will study the way how different t-norms functions within TCN fusion rule influence over target type estimation performance. 

TCN fusion rule does not belong to the general Weighted Operator Class. The base principle of this rule developed in [12] consists in the following steps: 
Step 1: Defining the T-norm conjunctive consensus: The t-norm conjunctive consensus is based on the particular t-norm function. In general it is a function defined in fuzzy set/logic theory in order to represent the intersection between two particular fuzzy sets. If one extends t-norm to the data fusion theory, it will be a substitute for the conjunctive rule. 

$${\mathrm{(2)}}$$

The t-norm has to satisfy the following conditions: 

Associativity:  $$T_{norm}\big{(}\ T_{norm}\big{(}\ x,y\ \big{)},z\ \big{)}=T_{norm}\big{(}\ x,T_{norm}\big{(}\ y,z\ \big{)}\big{)}$$  Commutativity:  $$T_{norm}\big{(}\ x,y\ \big{)}=T_{norm}\big{(}\ y,x\ \big{)}$$  Monotonicity:
$$\mathbf{a}$$
$$(3)$$

$$\mathrm{Monotonicity}\!:$$
$\blacksquare$
$$\left(4\right)$$

_if $(\ x\leq a\ )\ \&\ (\ y\leq b\ )$ then $T_{norm}(\ x,y\ )\leq T_{norm}(\ a,b\ )$_
Boundary Conditions:  $$T_{norm}(\ 0,0\ )=0;T_{norm}(\ x,1\ )=X$$
$\blacksquare$

$$({\mathfrak{H}})$$
 *norm* = 000 1 ),x(norm = *xT;),(T* (5) 
There are many functions which satisfy these t-norm conditions: 
- Zadeh's (default) min operator: 