Optimization and improvement of a robotics gaze control system using LSTM networks

Domingo, Jaime Duque; Gómez-García-Bermejo, Jaime; Zalama, Eduardo

doi:10.1007/s11042-021-11112-7

Cited by 8 publications

(3 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to technological advances, the research and implementation of robotic systems are in constant development, trying to optimize self-control, leading their system to be based on autonomous operations and intelligent decision making [42,43]. Especially for movement, different control methods have been designed that vary according to their field of application; however, the most used is the predictive model, which is based on generating a decision based on statistics, which in turn uses a large amount of data in industrial environments [44][45][46][47][48][49].…”

Section: Related Workmentioning

confidence: 99%

Autonomous Navigation of Robots: Optimization with DQN

et al. 2023

View full text Add to dashboard Cite

In the field of artificial intelligence, control systems for mobile robots have undergone significant advancements, particularly within the realm of autonomous learning. However, previous studies have primarily focused on predefined paths, neglecting real-time obstacle avoidance and trajectory reconfiguration. This research introduces a novel algorithm that integrates reinforcement learning with the Deep Q-Network (DQN) to empower an agent with the ability to execute actions, gather information from a simulated environment in Gazebo, and maximize rewards. Through a series of carefully designed experiments, the algorithm’s parameters were meticulously configured, and its performance was rigorously validated. Unlike conventional navigation systems, our approach embraces the exploration of the environment, facilitating effective trajectory planning based on acquired knowledge. By leveraging randomized training conditions within a simulated environment, the DQN network exhibits superior capabilities in computing complex functions compared to traditional methods. This breakthrough underscores the potential of our algorithm to significantly enhance the autonomous learning capacities of mobile robots.

show abstract

Section: Related Workmentioning

confidence: 99%

Autonomous Navigation of Robots: Optimization with DQN

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Step 2.40 GHz CUP, Tesla K80 GPU, and 8G memory, and the data are preprocessed by using SPSS software. [22,23], the relevant variables selected for this experiment are indicated in Table 1.…”

Section: Market Risk Early-warning Model Based On Improved Lstmmentioning

confidence: 99%

Construction and Simulation of the Market Risk Early-Warning Model Based on Deep Learning Methods

Lei

2022

Scientific Programming

View full text Add to dashboard Cite

To address the problem of low efficiency of existing forecasting models for market risk warning, a market risk early-warning model based on improved LSTM is suggested utilizing the whale optimization algorithm (WOA) to optimize the number of hidden layer neurons and time step parameters of long short-term memory. The proposed market risk early-warning model is validated by using 40 real estate companies as the research subjects and 20 relevant variables such as gross operating income, net profit asset growth rate, and total asset growth rate as indicators. The results demonstrate that the proposed model’s prediction accuracy for market risk is greater than 96% and that when compared to the standard CNN and LSTM models, the suggested model’s prediction accuracy for corporate finance from 2012 to 2019 is increased by 14% and 12%, respectively, and the prediction accuracy for corporate finance in 2020 is improved by 22% and 7%, respectively, which has certain practical application value and superiority.

show abstract

“…Machine learning-based approaches for time series data forecasting mainly encompass Recurrent Neural Networks (RNNs) 3 and Support Vector Machines (SVMs) 4 . The advancement of neural [5][6][7][8] networks has opened up new avenues for multivariate sequence analysis, such as graph convolutional networks (GCNs), graph attention networks (GATs), and multi-correlation techniques. Defferrard et al employed graph neural networks (GNNs) to model graph-structured data.…”

Section: Introductionmentioning

confidence: 99%

A new method of financial multivariate time series forecasting based on complex network attention mechanism

Xiong,

Zhang,

et al. 2024

Preprint

View full text Add to dashboard Cite

Due to the high degree of correlation and interaction among multiple financial series, as well as the non-stationary and nonlinear characteristics of these series, predicting financial multivariate time series accurately using traditional time series analysis methods is challenging. However, neural network models can effectively handle nonlinearity and instability, enabling relatively accurate predictions. In this paper, we propose a novel method called MTP-CVGAT(Multivariate Time-Series Prediction via Correlation and Visible Graphs Attention Network), which combines the correlation and visibility features of multivariate series. Taking into account accommodation and catering stocks of companies listed on China's Shanghai-Shenzhen A-share market as an example, our results demonstrate that the prediction performance of MTP-CVGAT surpasses that of other models including Long short-term memory neural network model(LSTM), multi-variable time series anomaly detection model based on graph attention network, and transfer learning model based on Wang X G et al.'s approach.

show abstract

Optimization and improvement of a robotics gaze control system using LSTM networks

Cited by 8 publications

References 35 publications

Autonomous Navigation of Robots: Optimization with DQN

Autonomous Navigation of Robots: Optimization with DQN

Construction and Simulation of the Market Risk Early-Warning Model Based on Deep Learning Methods

A new method of financial multivariate time series forecasting based on complex network attention mechanism

Contact Info

Product

Resources

About