Feed and model uncertainties were modeled by using normal and uniform distributions respectively.
The inverse problem is solved using three different a priori models namely normal, log normal and uniform.
For example, Khamsi [23] defined normal and uniform normal structure for metric spaces and proved that if ( X, d ) is a complete bounded metric space with uniform normal structure, then it has the fixed point property for nonexpansive mappings and a kind of intersection property which extends a result of Maluta [24] to metric spaces.
On the other hand, in 1989, Khamsi [11] defined normal and uniform normal structure for metric spaces and proved that if ( X, d ) is a complete bounded metric space with uniform normal structure, then it has the fixed point property for nonexpansive mappings and a kind of intersection property which extends the result of Maluta [12] to metric spaces.
The modes and spreads of fuzzy random errors Φ t are chosen as random samples from normal and uniform distributions, N ( 0, 1 ), U [ 0, 0.5 ], respectively.
The modes and spreads of the fuzzy random errors Φ t are chosen as random samples from normal and uniform distributions, N ( 0, 4 ), U [ 0, 5 ], respectively.
Second, the underlying normal and uniform mixture distributions give equal density in the tails and is effective in reducing the influence of extreme expression values.
In 1989, Khamsi [1] defined normal structure and uniform normal structure for metric spaces and utilized the same to prove that nonexpansive mappings on a complete bounded metric space equipped with uniform normal structure have fixed point property and further satisfy a kind of intersection property which extends results of Maluta [2] to metric spaces.
We considered the following three distributions: gamma, log-normal, and uniform.
