Results 71 to 80 of about 51,928 (231)
Neuro fuzzy control of the FES assisted freely swinging leg of paraplegic subjects [PDF]
, 1996 The authors designed a neuro fuzzy control strategy for control of cyclical leg movements of paraplegic subjects. The cyclical leg movements were specified by three `swing phase objectives', characteristic of natural human gait.
Spek, Jaap H. van der +3 more
core +3 more sources
Biological Reviews, EarlyView.ABSTRACT Oropharyngeal food processing exhibits a remarkable diversity among vertebrates, reflecting the evolution of specialised ‘processing centres’ associated with the mandibular, hyoid, and branchial arches. Although studies have detailed various food‐processing strategies and mechanisms across vertebrates, a coherent and comprehensive terminology ...
Daniel Schwarz +6 more
wiley +1 more source
FUZZY INFERENCE NEURAL NETWORKS WITH FUZZY PARAMETERS
TASK Quarterly, 2003 This paper concerns fuzzy neural networks and fuzzy inference neural networks, which are two different approaches to neuro-fuzzy combinations. The former is a direct fuzzification of artificial neural networks by introducing fuzzy signals and fuzzy ...
DANUTA RUTKOWSKA, YOICHI HAYASHI
doaj
Using of artificial intelligence for improving of lautering process productivity (part 2).
Kvasný průmysl, 2001 For control of lautering process firm Kiemann and Technical University Heilbronn have developed and control system Neuro-Fuzzy-Controller, that enables to obtain very good results even under using raw stuffs of different qualities.
F. MIROLL +4 more
doaj +1 more source
, 2010 Predictive modeling is the process of identifying a model of an unknown or complex process from numerical data. Due to the inherent complexity of many real processes, conventional modeling techniques have proved to be too restrictive. Recently, the hybrid approach to predictive modeling has become a popular research focus.
Azar, , Taher, Ahmad
openaire +3 more sources
Approximation properties of the neuro-fuzzy minimum function [PDF]
, 1999 The integration of fuzzy logic systems and neural networks in data driven nonlinear modeling applications has generally been limited to functions based upon the multiplicative fuzzy implication rule for theoretical and computational reasons.
Gottschling, Andreas, Kreuter, Christof
core
Data Analysis and Neuro-Fuzzy Technique for EOR Screening : Application in Angolan Oilfields [PDF]
, 2017 This study is sponsored by the Angolan National Oil Company (Sonangol EP) and the authors are grateful for their support and the permission to use the data and publish this manuscriptPeer reviewedPublisher ...
Akanji, Lateef, Ramos, Geraldo A R
core +2 more sources
Ensemble‐based soil liquefaction assessment: Leveraging CPT data for enhanced predictions
Civil Engineering Design, Volume 7, Issue 1, Page 23-35, March 2025.Abstract This study focuses on predicting soil liquefaction, a critical phenomenon that can significantly impact the stability and safety of structures during seismic events. Accurate liquefaction assessment is vital for geotechnical engineering, as it informs the design and mitigation strategies needed to safeguard infrastructure and reduce the risk ...
Arsham Moayedi Far, Masoud Zare
wiley +1 more source
Data‐driven analysis of the spatial dependence of grouting efficiency during tunnel excavation
Deep Underground Science and Engineering, EarlyView.Prediction of grouting efficiency using machine learning is enhanced by adopting a training strategy that accounts for the grouting process across multiple rounds. Abstract Grouting with water–cement mixtures is the most widely used and cost‐effective method for managing excess water inflow during tunnel construction.
Huaxin Liu, Xunchang Fei, Wei Wu
wiley +1 more source
Rockburst prediction based on data preprocessing and hyperband‐RNN‐DNN
Deep Underground Science and Engineering, EarlyView.A data preprocessing workflow is proposed to address challenges in rockburst data analysis. Coupled algorithms preprocess the data set, and hyperband optimization is used to enhance RNN performance. Results show that preprocessing improves accuracy, while dense layers enhance model stability and prediction performance.
Yong Fan +4 more
wiley +1 more source