This paper proposes an intelligent forecasting system based on a feedforward-neural-network-aided grey model (FNAGM), which integrates a first-order single variable grey model (GM(1,1)) and a feedforward neural network. There are three phases in the system process, including initialization phase, GM(1,1) prediction phase and FNAGM prediction phase. First, some parameters required in the FNAGM a...

In this paper, we present a review of some recent works on approximation by feedforward neural networks. A particular emphasis is placed on the computational aspects of the problem, i.e. we discuss the possibility of realizing a feedforward neural network which achieves a prescribed degree of accuracy of approximation, and the determination of the number of hidden layer neurons required to achi...

Objective(s): This study aims to evaluate and predict the thermal conductivity of iron oxide nanofluid at different temperatures and volume fractions by artificial neural network (ANN) and correlation using experimental data. Methods: Two-layer perceptron feedforward artificial neural network and backpropagation Levenberg-Marquardt (BP-LM) tra...

A vector matrix real time backpropagation algorithm for recurrent neural networks that approximate multi-valued periodic functions," Received Unlike feedforward neural networks (FFNN) which can act as universal function ap-proximators, recursive, or recurrent, neural networks can act as universal approximators for multi-valued functions. In this paper, a real time recursive backpropagation (RTR...

Feedforward neural networks have been theoretically proved to be able to approximate a nonlinear function to any degree of accuracy as long as enough nodes exist in the hidden layer(s) (Hornik et. al. 1989). However, when feedforward neural networks are applied to modeling physical systems in the real world, people care more about their prediction capabilities than accurate modeling abilities. ...

A common view of feedforward neural networks is that of a black box since the knowledge embedded in the connection weights of a feedforward neural network is generally considered incomprehensible. Many researchers have addressed this deficiency of neural networks by suggesting schemes to obtain a Boolean logic representation for the output of a neuron based on its connection weights. However, t...

The Feedforward Multilayer Perceptrons network is a widely used model in Artificial Neural Network using the backpropagation algorithm for real world data. There are two common ways to construct Feedforward Multilayer Perceptrons network, that is, either taking a large network and then pruning away the irrelevant nodes or starting from a small network and then adding new relevant nodes. An Arti...

A feedforward neural network based on multi-valued neurons is considered in the paper. It is shown that using a traditional feedforward architecture and a high functionality multi-valued neuron, it is possible to obtain a new powerful neural network. Its learning does not require a derivative of the activation function and its functionality is higher than the functionality of traditional feedfo...

The intrinsic hysteresis behavior of piezoelectric material limits the tracking control accuracy of the actuators. This paper describes a tracking control method for piezoelectric actuators based on the combination of feedforward and feedback loops. The hysteresis of piezoelectric actuators is linearized in feedforward loop with an inverse hysteresis model based on a neural network. The number ...