Recent research has shown the benefits of deep learning, a set of machine learning
techniques able to learn deep architectures, for modelling robotic perception and action. In …

To improve the autonomy and intelligence of asteroid landing control, a real-time optimal
control approach is proposed using deep neural networks (DNN) to achieve precise and …

The current study aims to approximate the solution of fractional differential equations (FDEs)
by using the fundamental properties of artificial neural networks (ANNs) for function …

This study is motivated by the requirement of on-board trajectory optimization with
guaranteed convergence and real-time performance for optimal spacecraft orbit transfers. To …

Nowadays, the study of neural networks is one of the most interesting research topics. In this
article, we introduce a novel scheme based on Physics Informed Neural Network (PINN) for …

In this paper the perceptron neural networks are applied to approximate the solution of
fractional optimal control problems. The necessary (and also sufficient in most cases) …

In this paper, considering the applications of interval analysis in various fields (such as
artificial intelligence, neural computation, genetic algorithms, information theory or fuzzy …

Batch scheduling is a crucial aspect of pipeline enterprise operation management,
especially in the context of market-oriented operation. It involves three main tasks: quickly …

We investigate the use of deep artificial neural networks to approximate the optimal state-
feedback control of continuous time, deterministic, non-linear systems. The networks are …

Pipeline transportation is cost-optimal in refined product transportation. However, the
optimization of multi-product pipeline scheduling is rather complicated due to multi-batch …