A feedforward neural network defines a mapping from an input x to an output y through a function f of x and theta. For example, we use neural networks to produce outputs such as the location of all cars in a camera image. The function f takes an input x, and uses a set of learned...
前馈神经网络(Feedforward Neural Network BP) 常见的前馈神经网络 感知器网络 感知器(又叫感知机)是最简单的前馈网络,它主要用于模式分类,也可用在基于模式分类的学习控制和多模态控制中。感知器网络可分为单层感知器网络和多层感知器网络。 BP网络 BP网络是指连接权
(1996). Numerical solution of a calculus of variations problem using the feedforward neural network architecture. Advances in Engineering Software, 27(3), 213-225.Meade, A.J., Sonneborn, H.C.: Numerical solution of a calculus of variations problem using the feedforward neural network ...
A feedforward neural network is capable of approximating the partial derivatives of an arbitrary function. • The subinterval method is used to obtain more reliable results for problems with relatively large uncertainty levels. • Three numerical examples show that the present method can achieve fin...
Learning is carried out on a multi layer feed-forward neural network using the back-propagation technique. The properties generated for each training sample are stimulated by the inputs. The hidden layer is simultaneously fed the weighted outputs of the input layer. The weighted output of the hid...
The aim of this paper is to design feed forward neural network for solving second-order singular boundary value problems in ordinary differential equations. The neural networks use the principle of back propagation with different training algorithms such as quasi-Newton, Levenberg-Marquardt, and Bayesia...
Learning is carried out on a multi layer feed-forward neural network using the back-propagation technique. The properties generated for each training sample are stimulated by the inputs. The hidden layer is simultaneously fed the weighted outputs of the input layer. The weighted output of the hid...
Deep feedforward networks, also often calledfeedforward neural networks, ormultilayer perceptrons(MLPs), are the quintessential(精髓) deep learning models.The goal of a feedforward network is to approximate some function f ∗ f^{*} f∗.For example, for a classifier, y = f ∗ ( x ) ...
Feedforward neural network, Deep feedforward network, Multi-layer perceptron XOR Gradient-based Learning The largest difference between the linear models and neural networks is that the nonlinearity of a neural network causes most interesting loss functions to become non-convex. This means that neural...
In recent years, data-driven approaches, such as neural networks, have become an active research field in mechanics. In this contribution, deep neural networks—in particular, the feed-forward neural network (FFNN)—are utilized directly for the development of the failure model. The verification ...