Reference health
A Hybrid Clustering-Based Type-2 Adaptive Neuro-Fuzzy Forecasting Model for Smart Control Systems
1 of 44 checkable references need attention · checked 2026-09-13
At the dated check, the references listed below either did not resolve in Crossref or DataCite, or carried a retraction notice. Each one is shown with the registry record that put it there.
21 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
References needing attention
marked retracted — notice via Crossref, record curated by Retraction Watch10.1007/s00500-022-07299-y
RETRACTED ARTICLE: Research on safety simulation model and algorithm of dynamic system based on artificial neural network
The 43 checked references that resolve
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The application of K-means clustering for province clustering in Indonesia of the risk of the COVID-19 pandemic based on COVID-19 data
resolves10.1061/(ASCE)0733-9399(2004)130:4(407)
Fuzzy Controller for Seismically Excited Nonlinear Buildings
resolves10.1016/S0045-7949(01)00041-4
Optimal direct (static) output feedback controller using real coded genetic algorithms
resolves10.1177/1077546317696369
Semi-active seismic control of an 11-DOF building model with TMD+MR damper using type-1 and -2 fuzzy algorithms
resolves10.1016/j.egyr.2021.12.012
A new predictive energy management system: Deep learned type-2 fuzzy system based on singular value decommission
resolves10.1007/s00500-018-3128-7
Efficient extreme learning machine via very sparse random projection
resolves10.1007/s11227-016-1898-1
Improved clustering algorithms for target tracking in wireless sensor networks
resolves10.1016/j.eswa.2022.119003
A novel two-phase clustering-based under-sampling method for imbalanced classification problems
resolves10.1007/s00366-020-01231-4
An integrated approach of ANFIS-grasshopper optimization algorithm to approximate flyrock distance in mine blasting
resolves10.1016/j.fss.2021.04.014
Type–reduction of Interval Type–2 fuzzy numbers via the Chebyshev inequality
resolves10.1007/s42107-018-0016-5
Seismic control of buildings with active tuned mass damper through interval type-2 fuzzy logic controller including soil–structure interaction
resolves10.1007/s00366-019-00849-3
A new development of ANFIS–GMDH optimized by PSO to predict pile bearing capacity based on experimental datasets
resolves10.1016/j.compeleceng.2020.106940
Neural network predictive control of vibrations in tall structure: An experimental controlled vision
resolves10.1007/s00366-021-01329-3
A novel technique based on the improved firefly algorithm coupled with extreme learning machine (ELM-IFF) for predicting the thermal conductivity of soil
resolves10.1504/IJBIC.2009.022781
Imperialist competitive algorithm for minimum bit error rate beamforming
resolves10.1007/s40745-021-00344-x
A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting
resolves10.1109/ACCESS.2021.3065837
A Data-Driven Building’s Seismic Response Estimation Method Using a Deep Convolutional Neural Network
resolves10.1016/j.jweia.2007.06.034
Wind-induced vibration of high-rise building with tuned mass damper including soil–structure interaction
resolves10.1007/s10489-020-01708-1
A new parameter reduction algorithm for interval-valued fuzzy soft sets based on Pearson’s product moment coefficient
resolves10.1007/s00366-018-00694-w
Optimization of ANFIS with GA and PSO estimating α ratio in driven piles
resolves10.1007/s00366-018-0615-5
Prediction of building damage induced by tunnelling through an optimized artificial neural network
resolves10.1016/j.engstruct.2008.05.009
Active control of large structures using a bilinear pole-shifting transform with control method
resolves10.1080/00207721.2017.1421724
Bidirectional active control of structures with type-2 fuzzy PD and PID
resolves10.1016/j.istruc.2022.06.043
Prediction and optimization of the flexural behavior of corroded concrete beams using adaptive neuro fuzzy inference system
resolves10.1016/j.engstruct.2010.08.006
Control strategy for mitigating the response of structures subjected to earthquake actions
resolves10.1177/14613484211032758
Research developments in adaptive intelligent vibration control of smart civil structures
resolves10.1142/S1793431118500100
Seismic Control of High-Rise Buildings Equipped with ATMD Including Soil-Structure Interaction Effects
resolves10.1109/ACCESS.2022.3215530
Application of WOA Tuned Type-2 FLC for LFC of Two Area Power System With RFB and Solar Park Considering TCPS in Interline
resolves10.1002/(SICI)1096-9845(1998110)27:11<1127::AID-EQE774>3.0.CO;2-F
Benchmark problems in structural control: part I—Active Mass Driver system
resolves10.22161/ijaers/3.11.24
Development of ANFIS Controller and PID Controller for Seismic Vibration Control of Structural System
resolves10.1016/j.isatra.2020.01.009
Asymmetrical interval type-2 fuzzy logic control based MPPT tuning for PV system under partial shading condition
resolves10.1016/j.aei.2023.101916
Cluster-based industrial KPIs forecasting considering the periodicity and holiday effect using LSTM network and MSVR
resolves10.1016/j.comcom.2021.03.021
A novel adaptive density-based spatial clustering of application with noise based on bird swarm optimization algorithm
resolves10.1177/1077546320986380
A general inverse control model of a magneto-rheological damper based on neural network
resolves10.1016/0020-0255(75)90036-5
The concept of a linguistic variable and its application to approximate reasoning—I
resolves10.1007/s00366-021-01414-7
A new framework of multi-objective BELBIC for seismic control of smart base-isolated structures equipped with MR dampers
resolves10.3390/ma14216593
Optimized Neural Network Prediction Model of Shape Memory Alloy and Its Application for Structural Vibration Control
resolves10.1007/s00366-019-00822-0
Performance evaluation of hybrid FFA-ANFIS and GA-ANFIS models to predict particle size distribution of a muck-pile after blasting
The 21 references without a DOI — listed, not checked
no DOI — not checkedPattern recognition with fuzzy objective function algorithms
no DOI — not checkedref10
no DOI — not checkedref11
no DOI — not checkedref17
no DOI — not checkedMulti-objective optimal design of periodically stiffened panels for vibration control using data-driven optimization method
no DOI — not checkedAn exploration of the key determinants for the application of AI-enabled higher education based on a hybrid Soft-computing technique and a DEMATEL approach
no DOI — not checkedMultilayer extreme learning machines and their modeling performance on dynamical systems
no DOI — not checkedA novel multi-feature model predictive control framework for seismically excited high-rise buildings
no DOI — not checkedref25
no DOI — not checkedref26
no DOI — not checkedData mining and education
no DOI — not checkedSeismic response of high-rise buildings using long short-term memory intelligent decentralized control system
no DOI — not checkedA support vector regression (SVR)-based method for dynamic load identification using heterogeneous responses under interval uncertainties
no DOI — not checkedSensor placement and seismic response reconstruction for structural health monitoring using a deep neural network
no DOI — not checkedComparing fuzzy type-1 and-2 in semi-active control with TMD considering uncertainties
no DOI — not checkedQoS-aware cloud service composition: A systematic mapping study from the perspective of computational intelligence
no DOI — not checkedRobust and fuzzy ensemble framework via spectral learning for random projection-based fuzzy-c-means clustering
no DOI — not checkedNovel metaheuristic-based tuning of PID controllers for seismic structures and verification of robustness
no DOI — not checkedCoupled extreme learning machine and particle swarm optimization variant for projectile aerodynamic identification
no DOI — not checkedA new time-domain robust anti-windup PID control scheme for vibration suppression of building structure
no DOI — not checkedProposing several hybrid PSO-extreme learning machine techniques to predict TBM performance
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