On learning the derivatives of an unknown mapping with multilayer feedforward networks

Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks

-We give conditions ensuring that multilayer jeedJorward networks with as Jew as a single hidden layer and an appropriately smooth hidden layer activation fimction are capable o f arbitrarily accurate approximation to an arbitrao' function and its derivatives. In fact, these networks can approximate functions that are not dtifferentiable in the classical sense, but possess only a generalized de...

Using Artificial Neural Network Algorithm to Predict Tensile Properties of Cotton-Covered Nylon Core Yarns

Artificial Neural Networks are information processing systems. Over the past several years, these algorithms have received much attention for their applications in pattern completing, pattern matching and classification and also for their use as a tool in various areas of problem solving. In this work, an Artificial Neural Network model is presented for predicting the tensile &#10properties of ...

Using Artificial Neural Network Algorithm to Predict Tensile Properties of Cotton-Covered Nylon Core Yarns

Artificial Neural Networks are information processing systems. Over the past several years, these algorithms have received much attention for their applications in pattern completing, pattern matching and classification and also for their use as a tool in various areas of problem solving. In this work, an Artificial Neural Network model is presented for predicting the tensile properties of co...

Differentiating Functions of the Jacobian with Respect to the Weights

Abstract For many problems, the correct behavior of a model depends not only on its input-output mapping but also on properties of its Jacobian matrix, the matrix of partial derivatives of the model’s outputs with respect to its inputs. We introduce the J-prop algorithm, an efficient general method for computing the exact partial derivatives of a variety of simple functions of the Jacobian of a...

Approximating and learning unknown mappings using multilayer feedforward networks with bounded weights