Adaptive Wind Driven Optimization Trained Artificial Neural Networks

11/20/2019 ∙ by Zikri Bayraktar, et al. ∙ 0 ∙ share

This paper presents the application of a newly developed nature-inspired metaheuristic optimization method, namely the Adaptive Wind Driven Optimization (AWDO), to the training of feedforward artificial neural networks (NN) and presents a discussion into the future research of AWDO implementation in Deep Learning (DL). Application example of digit classification with MNIST dataset reveals interesting behavior of the derivative-free AWDO method compared to steepest descent method where results and future work on the implementation of AWDO in deep neural networks are discussed.

READ FULL TEXT

Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both?

This work deals with the use of emerging deep learning techniques in fut...
02/05/2019 ∙ by Alessio Zappone, et al. ∙ 0 ∙ share

read it
Application of Artificial Neural Networks in Estimating Participation in Elections

It is approved that artificial neural networks can be considerable effec...
09/09/2013 ∙ by Seyyed Reza Khaze, et al. ∙ 0 ∙ share

read it
Instantaneously Trained Neural Networks

This paper presents a review of instantaneously trained neural networks ...
01/30/2006 ∙ by Abhilash Ponnath, et al. ∙ 0 ∙ share

read it
On Interpretability of Artificial Neural Networks

Deep learning has achieved great successes in many important areas to de...
01/08/2020 ∙ by Fenglei Fan, et al. ∙ 76 ∙ share

read it
Metaheuristic Design of Feedforward Neural Networks: A Review of Two Decades of Research

Over the past two decades, the feedforward neural network (FNN) optimiza...
05/16/2017 ∙ by Varun Kumar Ojha, et al. ∙ 0 ∙ share

read it
An Evolutionary Algorithm for Error-Driven Learning via Reinforcement

Although different learning systems are coordinated to afford complex be...
03/26/2015 ∙ by Yanping Liu, et al. ∙ 0 ∙ share

read it
CoCalc as a Learning Tool for Neural Network Simulation in the Special Course "Foundations of Mathematic Informatics"

The role of neural network modeling in the learning content of the speci...
07/02/2018 ∙ by Oksana Markova, et al. ∙ 0 ∙ share

read it

Adaptive Wind Driven Optimization Trained Artificial Neural Networks

Authors

Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both?

Application of Artificial Neural Networks in Estimating Participation in Elections

Instantaneously Trained Neural Networks

On Interpretability of Artificial Neural Networks

Metaheuristic Design of Feedforward Neural Networks: A Review of Two Decades of Research

An Evolutionary Algorithm for Error-Driven Learning via Reinforcement

CoCalc as a Learning Tool for Neural Network Simulation in the Special Course "Foundations of Mathematic Informatics"

This week in AI