Optimal model for path loss predictions using feed-forward neural networks

Image Compression Using Feed Forward Neural Networks

Image compression technique is used to reduce the number of its required in representing image, which helps to reduce the storage space and transmission cost. In the present research work back propagation neural network training algorithm has been used. Back propagation neural network algorithm helps to increase the performance of the system and to decrease the convergence time for the training...

A Mathematical Model for Feed � Forward Neural Networks

We present a general model for di erentiable feed forward neural networks Its general mathemat ical description includes the standard multi layer perceptron as well as its common derivatives These standard structures assume a strong relationship between the network links and the neuron weights Our generalization takes advantage of the suppression of this assumption Since our model is especially...

Some Theorems for Feed Forward Neural Networks

This paper introduces a new method which employs the concept of “Orientation Vectors” to train a feed forward neural network. It is shown that this method is suitable for problems where large dimensions are involved and the clusters are characteristically sparse. For such cases, the new method is not NP hard as the problem size increases. We ‘derive’ the present technique by starting from Kolmo...

Estimating Model Complexity of Feed-Forward Neural Networks

Using CODEQ to Train Feed-forward Neural Networks

CODEQ is a new, population-based meta-heuristic algorithm that is a hybrid of concepts from chaotic search, opposition-based learning, differential evolution and quantum mechanics. CODEQ has successfully been used to solve different types of problems (e.g. constrained, integer-programming, engineering) with excellent results. In this paper, CODEQ is used to train feed-forward neural networks. T...