(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...
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...
Perceptron(linear and non-linear) andRadial Basis Function networksare examples of feedforward networks. A single-layer perceptron network is the most basic type of neural network. It has a single layer of output nodes, and the inputs are fed directly into the outputs via a set of weights. ...
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
Example of a basic neural network The neural network in the above example comprises an input layer composed of three input nodes, two hidden layers based on four nodes each, and an output layer consisting of two nodes. Structure of Feedforward Neural Networks ...
We propose a quantum generalisation of a classical neural network. The classical neurons are firstly rendered reversible by adding ancillary bits. Then they are generalised to being quantum reversible, i.e., unitary (the classical networks we generalise are called feedforward, and have step-function...
Differentiating and pruning multilayer feedforward neural networks* James Ting-Ho Lo and Lei Yu Abstract. This paper presents two methods of evaluating the derivatives of all orders of the output variables of a multilayer feedforward neural network w.r.t. the input variables. In analogy to the ...
In particular, we attempted to solve joint inversion problem through Feedforward Neural Network (FNN) technique. In order to identify geothermal sweet spots in the subsurface, an extensive geophysical studies were conducted in Gandhar area of Gujarat, India. The data were acquired along six profile...
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 ...