Picture of DNA strand

Pioneering chemical biology & medicinal chemistry through Open Access research...

Strathprints makes available scholarly Open Access content by researchers in the Department of Pure & Applied Chemistry, based within the Faculty of Science.

Research here spans a wide range of topics from analytical chemistry to materials science, and from biological chemistry to theoretical chemistry. The specific work in chemical biology and medicinal chemistry, as an example, encompasses pioneering techniques in synthesis, bioinformatics, nucleic acid chemistry, amino acid chemistry, heterocyclic chemistry, biophysical chemistry and NMR spectroscopy.

Explore the Open Access research of the Department of Pure & Applied Chemistry. Or explore all of Strathclyde's Open Access research...

Performance evaluation of artificial neural network-based shaping algorithm for planetary pinpoint guidance

Simo, Jules and Furfaro, Roberto and Mueting, Joel (2015) Performance evaluation of artificial neural network-based shaping algorithm for planetary pinpoint guidance. In: 25th AAS/AIAA Space Flight Mechanics Meeting, 2015-01-11 - 2015-01-15.

[img]
Preview
Text (Simo-etal-Spaceflight-Mechs-Mtg-2015-Performance-evaluation-of-shaping-algorithm-for-planetary-pinpoint-guidance)
Simo_etal_Spaceflight_Mechs_Mtg_2015_Performance_evaluation_of_shaping_algorithm_for_planetary_pinpoint_guidance.pdf
Accepted Author Manuscript

Download (1MB)| Preview

    Abstract

    Computational intelligence techniques have been used in a wide range of application areas. This paper proposes a new learning algorithm that dynamically shapes the landing trajectories, based on potential function methods, in order to provide computationally efficient on-board guidance and control. Extreme Learning Machine (ELM) devises a Single Layer Forward Network (SLFN) to learn the relationship between the current spacecraft position and the optimal velocity field. The SLFN design is tested and validated on a set of data comprising data points belonging to the training set on which the network has not been trained. Furthermore, the proposed efficient algorithm is tested in typical simulation scenarios which include a set of Monte Carlo simulation to evaluate the guidance performances