A novel approach for nonlinear complex system identification based on internal recurrent neural networks (IRNN) is proposed in this paper. The computational complexity of neural identification can be greatly reduced if the whole system is decomposed into several subsystems. This approach employs internal state estimation when no measurements coming from the sensors are available for the system states. A modified backpropagation algorithm is introduced in order to train the IRNN for nonlinear system identification. The performance of the proposed design approach is proven on a car simulator case study.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1142/S0129065709001884 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Identification of the primary pollution sources and dominant influencing factors of soil heavy metals using a random forest model optimized by genetic algorithm coupled with geodetector.
Ecotoxicol Environ Saf
January 2025
College of Resource and Environment, Henan Polytechnic University, Jiaozuo 454003, China.
Identifying and quantifying the dominant factors influencing heavy metal (HM) pollution sources are essential for maintaining soil ecological health and implementing effective pollution control measures. This study analyzed soil HM samples from 53 different land use types in Jiaozuo City, Henan Province, China. Pollution sources were identified using Absolute Principal Component Score (APCS), with 8 anthropogenic factors, 9 natural factors, and 4 soil physicochemical properties mapped using Geographic Information System (GIS) kernel density estimation.
View Article and Find Full Text PDFArtificial intelligence applied in identifying left ventricular walls in myocardial perfusion scintigraphy images: Pilot study.
PLoS One
January 2025
Electrical, Mechanical & Computer Engineering School, Federal University of Goias, Goiania, Brazil.
This paper proposes the use of artificial intelligence techniques, specifically the nnU-Net convolutional neural network, to improve the identification of left ventricular walls in images of myocardial perfusion scintigraphy, with the objective of improving the diagnosis and treatment of coronary artery disease. The methodology included data collection in a clinical environment, followed by data preparation and analysis using the 3D Slicer Platform for manual segmentation, and subsequently, the application of artificial intelligence models for automated segmentation, focusing on the efficiency of identifying the walls of the left ventricular. A total of 83 clinical routine exams were collected, each exam containing 50 slices, which is 4,150 images.
View Article and Find Full Text PDFHealthcare staff perceptions of an electronic hand hygiene monitoring system within a large university system.
Antimicrob Steward Healthc Epidemiol
July 2024
Department of Public Health and Preventive Medicine, State University of New York Upstate Medical University, Syracuse, NY, USA.
Objective: The acceptability of an electronic HH monitoring system (EHHMS) was evaluated among hospital staff members.
Design: An electronic HH monitoring system was implemented in June 2020 at a large, academic medical center. An interdisciplinary team developed a cross-sectional survey to gather staff perceptions of the EHHMS.
Developing emergency medical care policy across Kenya: A framework for policy development.
Afr J Emerg Med
December 2024
Accident & Emergency Department, The Aga Khan University, Nairobi, Nairobi, Kenya.
Background And Objectives: The Kenya Emergency Medical Care (EMC) Policy 2020-2030 was created to guide the advancement of EMC throughout Kenya. This report describes and maps the ongoing EMC policy development process across Kenya's 47 counties, serving as a real-world example of EMC policy development within a decentralized healthcare system in a low-or middle-income country (LMIC).
Methods: This report evaluates the development of county-specific EMC policies using the Kenya Institute for Public Policy Research and Analysis (KIPPRA) six stages for policy development: 1) problem identification, 2) agenda setting, 3) policy design, 4) approval, 5) implementation, and 6) monitoring and evaluation.
Can Patient-Reported Outcomes Measurement Information System estimate high-impact chronic pain after total shoulder arthroplasty?
JSES Int
November 2024
Department of Population Health Sciences, Duke University, Durham, NC, USA.
Background: Identification of high-impact chronic pain (HICP) among patients receiving total shoulder arthroplasty (TSA) may allow for the design and implementation of tailored pain interventions to address the negative impact on postoperative outcomes and quality of life. This analysis sought to determine if Patient-Reported Outcome Measurement Information System (PROMIS) measures could be used to estimate HICP status following TSA.
Methods: This was a secondary analysis of a cohort of patients (n = 227) who received a TSA at a single, academic medical center, of whom 25 (11.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!