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.

Gradient Descent With Momentum from Scratch

Wiener-Neural-Network-Based Modeling and Validation of Generalized

Feed forward ,back propagation,gradient descent

Mathematics, Free Full-Text

Glossary - Pattern Recognition and Neural Networks

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

The mostly complete chart of Neural Networks, explained

PDF) Training Feed-forward Neural Networks Using the Gradient

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