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.

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Adaptive Wind Driven Optimization Trained Artificial Neural Networks

Authors

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

Instantaneously Trained Neural Networks

Application of Artificial Neural Networks in Estimating Participation in Elections

On Interpretability of Artificial Neural Networks

Using stigmergy to incorporate the time into artificial neural networks

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

Fast-DENSER++: Evolving Fully-Trained Deep Artificial Neural Networks

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