PDF] The Method of Steepest Descent for Feedforward Artificial Neural Networks

This paper implements the method of Steepest Descent in single and multilayer feedforward artificial neural networks and calculates the three new update weight equations for taking different activation function in different processing unit separately. In this paper, we implement the method of Steepest Descent in single and multilayer feedforward artificial neural networks. In all previous works, all the update weight equations for single or multilayer feedforward artificial neural networks has been calculated by choosing a single activation function for various processing unit in the network. We, at first, calculate the total error function separately for single and multilayer feedforward artificial neural networks and then calculate the three new update weight equations for taking different activation function in different processing unit separately single and multilayer feedforward artificial neural networks. An example is given to show usefulness of this implementation.

PDF) Stock Market Prediction using Feed-forward Artificial Neural

PDF] The Method of Steepest Descent for Feedforward Artificial

12.1 Feedforward Neural Network Practitioner's Guide to Data Science

Feed-forward vs feedback neural networks

Using the artificial neural networks for prediction and validating

PDF] FEED-FORWARD NEURAL NETWORKS TRAINING: A COMPARISON BETWEEN

Feed-forward vs feedback neural networks

Computation, Free Full-Text

Types of Optimization Algorithms used in Neural Networks and Ways

Frontiers Learning Without Feedback: Fixed Random Learning