Theorem 3.1 Let ε ∈ ( 0, ∞ ], ( X, d ) a complete ε-chainable metric space and { T i } i = 1 ∞ a sequence of fuzzy mappings from X into I X such that, for each ζ ∈ X and i ∈ N, T i 1 ∈ C B X X ).
Corollary 3.2 Let ε ∈ ( 0, ∞ ], ( X, d ) a complete ε-chainable metric space and { T i } i = 1 ∞ a sequence of fuzzy mappings from X into I X such that, for each ζ ∈ X and i ∈ N, T i 1 ∈ C B X X ).
Suppose 0 < q < 1, define { T i } i = 1 ∞ a sequence of fuzzy self-mappings of C ( X ) as T i = q i μ + μ 0 for any μ ∈ C ( X ).
Corollary 3.1 Let ( X, G ) be a G-complete metric space and { T i } i = 1 ∞ a sequence of fuzzy self-mappings of C ( X ).
A DBMS is designed with an adaptive control based on fuzzy logic such that it has the capability to control objects, i.e., the performance parameters, and update the controller itself, i.e., a set of fuzzy rules in our case.
I once tied a piece of fuzzy orange yarn to the handle to make it conspicuous and was deflated to discover my case circling on the carrousel next to a piece exactly like it, also adorned with orange yarn.
All of a sudden, I noticed a fuzzy spot," Webber, 45, told HuffPost.
Then (i) The union of fuzzy soft sets ( F, A ) and ( G, B ) is defined as the fuzzy soft set ( H, C ) = ( F, A ) ∪ ˜ ( G, B ) over U, where C = A ∪ B and H ( c ) = { F ( c ) if c ∈ A ∖ B, G ( c ) if c ∈ B ∖ A, F ( c ) ∨ G ( c ) if c ∈ A ∩ B .
However, there are several drawbacks of this approach: (i) the design of fuzzy controllers is usually performed in an ad hoc manner where it is often difficult to choose some of the controller parameters (e.g., the membership functions), and (ii) the fuzzy controller constructed for the nominal plant may later perform inadequately if significant and unpredictable plant parameter variations occur.
Let ((X,d)) be a complete metric space and let ({F_{i}}_{i = 1}^{infty}) be a sequence of fuzzy self-mappings of (mathcal{CB}(X)).
