Design and Equilibrium Control of a Force-Balanced One-Leg Mechanism

Ponce, Hiram; Acevedo, Mario

doi:10.1007/978-3-030-04497-8_23

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…Literature reports many different applications of AHN. Those can be classified as follows: function approximation and modeling [1]; robust human activity recognition systems [5]; signal processing in denoising audio and face recognition [1,6]; online advertising [6]; intelligent control systems for robotics [1,7,8,11] and mechatronics [1,9,10]; bio/medical applications [5,6,12]; and, theoretical approaches such as hybrid fuzzy-molecular inference systems [8], interpretability of the model [12] and training algorithms [3,4].…”

Section: Applications Of Artificial Hydrocarbon Networkmentioning

confidence: 99%

“…This interpretability was implemented for medical diagnosis systems [12]. • Reinforcement learning for continuous domains -AHN have been applied for continuous reinforcement learning approaches, in both states and actions, typically found in robotics [11,13]. This approach has also revealed insights on using it as a transfer learning method.…”

Section: Highlights Of Artificial Hydrocarbon Networkmentioning

confidence: 99%

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Challenges and Issues on Artificial Hydrocarbon Networks: The Chemical Nature of Data-Driven Approaches

Ponce¹

2019

LatinX in AI at International Conference on Machine Learning 2019

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Inspiration in nature has been widely explored, from macro to micro-scale. When looking into chemical phenomena, stability and organization are two properties that emerge. Recently, artificial hydrocarbon networks (AHN), a supervised learning method inspired in the inner structures and mechanisms of chemical compounds, have been proposed as a data-driven approach in artificial intelligence. AHN have been successfully applied in data-driven approaches, such as: regression and classification models, control systems, signal processing, and robotics. To do so, molecules –the basic units of information in AHN– play an important role in the stability, organization and interpretability of this method. Interpretability, saving computing resources, and predictability have been handled by AHN, as any other machine learning model. This short paper aims to highlight the challenges, issues and trends of artificial hydrocarbon networks as a data-driven method. Throughout this document, it presents a description of the main insights of AHN and the efforts to tackle interpretability and training acceleration. Potential applications and future trends on AHN are also discussed.

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Section: Applications Of Artificial Hydrocarbon Networkmentioning

confidence: 99%

Section: Highlights Of Artificial Hydrocarbon Networkmentioning

confidence: 99%

Challenges and Issues on Artificial Hydrocarbon Networks: The Chemical Nature of Data-Driven Approaches

Ponce¹

2019

LatinX in AI at International Conference on Machine Learning 2019

Self Cite

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“…These instances represent just a few examples of the diverse applications where AHN has demonstrated favorable outcomes. For readers seeking more in-depth information on specific cases mentioned here or desiring a broader understanding of the varied purposes for which AHN has been employed, it is recommended to explore the lectures by Ponce et al referenced in this work [13][14][15][16][17][18][19][20][21][22].…”

mentioning

confidence: 99%

A Comparative Study for Stock Market Forecast Based on a New Machine Learning Model

González-Núñez,

Trejo,

Kampouridis

2024

BDCC

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This research aims at applying the Artificial Organic Network (AON), a nature-inspired, supervised, metaheuristic machine learning framework, to develop a new algorithm based on this machine learning class. The focus of the new algorithm is to model and predict stock markets based on the Index Tracking Problem (ITP). In this work, we present a new algorithm, based on the AON framework, that we call Artificial Halocarbon Compounds, or the AHC algorithm for short. In this study, we compare the AHC algorithm against genetic algorithms (GAs), by forecasting eight stock market indices. Additionally, we performed a cross-reference comparison against results regarding the forecast of other stock market indices based on state-of-the-art machine learning methods. The efficacy of the AHC model is evaluated by modeling each index, producing highly promising results. For instance, in the case of the IPC Mexico index, the R-square is 0.9806, with a mean relative error of 7×10−4. Several new features characterize our new model, mainly adaptability, dynamism and topology reconfiguration. This model can be applied to systems requiring simulation analysis using time series data, providing a versatile solution to complex problems like financial forecasting.

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Development of Fast and Reliable Nature-Inspired Computing for Supervised Learning in High-Dimensional Data

Ponce

Gonzalez-Mora

Morales-Olvera

et al. 2019

Nature Inspired Computing for Data Science

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Design and Equilibrium Control of a Force-Balanced One-Leg Mechanism

Cited by 5 publications

References 20 publications

Challenges and Issues on Artificial Hydrocarbon Networks: The Chemical Nature of Data-Driven Approaches

Challenges and Issues on Artificial Hydrocarbon Networks: The Chemical Nature of Data-Driven Approaches

A Comparative Study for Stock Market Forecast Based on a New Machine Learning Model

Development of Fast and Reliable Nature-Inspired Computing for Supervised Learning in High-Dimensional Data

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