Here we study the univariate fuzzy fractional quantitative approximation of real valued functions on a compact interval by quasi-interpolation arctangent-algebraic-Gudermannian-generalized symmetrical activation function relied neural network operators. These approximations are derived establishing Jackson type inequalities involving moduli continuity right and left...

Barron (1993) obtained a deterministic approximation rate (in L2-norm) of r-l12. for a class of single hidden layer feedforward artificial neural networks (ANN) with r hidden units and sigmoid activation functions when the target function satisfies certain smoothness conditions. Hornik, Stinchcombe, White, and Auer (HSWA, 1994) extended Barron's result to a class of ANNs with possibly non-sigmo...

An optimal artificial neural network (ANN) has been developed to predict the Nusselt number of non-Newtonian nanofluids. The resulting ANN is a multi-layer perceptron with two hidden layers consisting of six and nine neurons, respectively. The tangent sigmoid transfer function is the best for both hidden layers and the linear transfer function is the best transfer function for the output layer....

Some sufficient conditions on certain constants which are involved in some first and second-order differential subordinations associated with functions positive real part like modified Sigmoid function, exponential function Janowski obtained so that the analytic [Formula: see text] normalized by condition text], is subordinate to function. The admissibility for used as a tool proof of results. ...

A Back Propagation Artificial Neural Network (BPANN) is a well-known learning algorithmpredicated on a gradient descent method that minimizes the square error involving the networkoutput and the goal of output values. In this study, 261 GPS/Leveling and 8869 gravity intensityvalues of Iran were selected, then the geoid with three methods “ellipsoidal stokes integral”,“BPANN”, and “collocation” ...

We investigate the computational power of recurrent neural networks that apply the sigmoid activation function _(x)=[2 (1+e)]&1. These networks are extensively used in automatic learning of non-linear dynamical behavior. We show that in the noiseless model, there exists a universal architecture that can be used to compute any recursive (Turing) function. This is the first result of its kind for...

Machine learning algorithms perform differently in settings with varying levels of training set mislabeling noise. Therefore, the choice of a good algorithm for a particular learning problem is crucial. In this paper, we introduce the “Sigmoid Rule” Framework focusing on the description of classifier behavior in noisy settings. The framework uses an existing model of the expected performance of...

wandering spleen is an uncommon entity in adults and has been described only rarely with sigmoid volvulus, that rarely affects children and adolescents. it is usually described in adults.wandering spleen characterized by the abnormal location of the spleen, caused by incomplete fusion of the four primary splenic ligaments, allowing the spleen to be mobile within the abdomen.the wandering spleen...