Cited by 810Neural Networks and ApplicationsNeural dynamics and brain functionMachine Learning and ELM

The DOI matches its Crossref record.

Neural Networks

Rojas, Raúl·1996·Springer eBooks

DOI 10.1007/978-3-642-61068-4

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1007/978-3-642-61068-4

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Sorted by citations

Items 701–800 of 809

Optimizing Stressors Using Genetic Algorithm to Minimize Work-Related Stress
Ghosh, A., Tweedale, J., & Nafalski, A. (2015) · Smart innovation, systems and technologies · doi:10.1007/978-3-319-13545-8_17
Design of Artificial Neural Networks for Flood Forecasting
Cullmann, J., & Schmitz, G. H. (2010) · Springer eBooks · doi:10.1007/978-90-481-9917-4_5
Monogenic bandpass filters for the segmentation of urban zones in satellite sensor imagery
Lira, Y., Marroquin, J. L., & Hernández, A. (2006) · International Journal of Remote Sensing · doi:10.1080/01431160500486682
Hot-film sensor calibration using adaptive Neural-fuzzy Inference System
Xuan, C., Chen, Z., Wu, P. J., et al. (2010) · International Conference on Natural Computation (ICNC) · doi:10.1109/icnc.2010.5583619
KERNEL-BASED DATA COMPRESSION FOR ADVANCED MANUFACTURING SYSTEM
Ge, M., & Xu, Y. (2003) · Journal of Advanced Manufacturing Systems · doi:10.1142/s021968670300023x
Applications of Neural Networks to Data Analysis and Control of Fusion Plasma.
Takeda, T., & Iyomasa, A. (2002) · プラズマ・核融合学会誌 · doi:10.1585/jspf.78.842
Automatic radiography image orientation using machine learning
Starcevic, D. S., Ostojic, V. S., & Petrovic, V. (2014) · Telecommunications Forum Telfor (TELFOR) · doi:10.1109/telfor.2014.7034458
Neural Network Normalization (N3) to Uncover the Differential Signal of cDNA Microarrays
Deng, C., & Wang, D. (2004) · The Journal of VLSI Signal Processing Systems for Signal Image and Video Technology · doi:10.1023/b:vlsi.0000042489.45944.64
Emergence of the Symbolic Function
Terenzi, G. (2002) · Springer eBooks · doi:10.1007/978-1-4615-0753-6_6
A quantum algorithm for obtaining the lowest eigenstate of a Hamiltonian assisted with an ancillary qubit system
Bang, J., Lee, S.-W., Lee, C.-W., & Jeong, H. (2014) · Quantum Information Processing · doi:10.1007/s11128-014-0836-5
Showing a Progress Bar While Executing Stored Procedures!
Roudaki, A., Doroodchi, M., & Li, J. (2011) · Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences · doi:10.1109/hicss.2011.375
Deterministic models and Neural Nets: a successful methodology for the air dispersion models
Pelliccioni, A., Tirabassi, T., Bellantone, S., et al. (2006) · 2022 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2006.247317
Controlling statistical moments of stochastic dynamical networks
Bielievtsov, D., Ladenbauer, J., & Obermayer, K. (2016) · Physical review. E · doi:10.1103/physreve.94.012306
Nonparametric Analysis and Nonlinear State-Space Identification: A Benchmark Example
Mulders, A. V., Schoukens, J., & Vanbeylen, L. (2014) · Conference proceedings of the Society for Experimental Mechanics · doi:10.1007/978-3-319-04522-1_19
A Hand Geometry Biometric Identification System Utilizing Modular Neural Networks with Fuzzy Integration
Sánchez, J. L., & Melín, P. (2012) · Studies in computational intelligence · doi:10.1007/978-3-642-33021-6_15
Effect of Angle Incidence on the Accuracy of Indoor Prediction Model Based on Neural Network
Ayadi, M. (2016) · International Conference on Advanced Information Networking and Applications Workshops (WAINA) · doi:10.1109/waina.2016.85
Ökobilanzierer auf Datensuche
Klein, M., & Stock, M. (2016) · NachhaltigkeitsManagementForum | Sustainability Management Forum · doi:10.1007/s00550-016-0393-8
The Length and Breadth of Löwdin-Orthogonalizations
Srivastava, V. (2003) · Springer eBooks · doi:10.1007/978-94-010-0113-7_9
Evolutionary Algorithms for Multisensor Data Fusion
Yang, J., Hines, E. L., Sloper, J. E., Iliescu, D. D., & Leeson, M. S. (2011) · IGI Global eBooks · doi:10.4018/978-1-61520-915-6.ch002
Does Training Lead to the Formation of Modules in Threshold Networks?
Nicolay, D., Roli, A., & Carletti, T. (2016) · Springer proceedings in complexity · doi:10.1007/978-3-319-29228-1_16
Guided Random Search and Network Techniques
(2015) · doi:10.1002/9781118897072.ch5
Local Feature Design Concepts
Krig, S. (2016) · Springer eBooks · doi:10.1007/978-3-319-33762-3_4 · Cited by 1
Bio-Inspired Computing through Artificial Neural Network
Dash, N., Priyadarshini, R., Mishra, B. K., & Misra, R. (2016) · Advances in business strategy and competitive advantage book series · doi:10.4018/978-1-5225-1008-6.ch011
Neural Networks in Back Analysis of Tunnels
Pruška, J. (2016) · Lecture notes in mechanical engineering · doi:10.1007/978-981-10-3247-9_4
Typhoon Economic Loss Prediction in China by Applying General Regression Neural Network and Hierarchical Cluster Analysis
Cheng, B., Cheng, L., & Jiang, L. (2014) · Journal of Disaster Research · doi:10.20965/jdr.2014.p0042
Deep or Wide? Learning Policy and Value Neural Networks for Combinatorial Games
Edelkamp, S. (2017) · Communications in computer and information science · doi:10.1007/978-3-319-57969-6_2
Post optimization paradigm in maximum 3-satisfiability logic programming
Mansor, M. A., Sathasivam, S., & Kasihmuddin, M. S. M. (2017) · AIP conference proceedings · doi:10.1063/1.4995914
Artificial immune system algorithm in VLSI circuit configuration
Mansor, M. A., Sathasivam, S., & Kasihmuddin, M. S. M. (2017) · AIP conference proceedings · doi:10.1063/1.4995913
Synthesis of neural network based flight controller using a neuroemulator
Andropov, S., Guirik, A. V., Budko, M. Y., et al. (2017) · International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT) · doi:10.1109/icumt.2017.8255145
BT Görüntülerden Akciğerin Tespiti için Süper Piksel ve Yapay Sinir Ağı Tabanlı Bir Yöntem
Kılıkçıer, Ç., & Yılmaz, E. (2018) · Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi · doi:10.18185/erzifbed.384268
Performance Analysis of Different Learning Algorithms of Feed Forward Neural Network Regarding Fetal Abnormality Detection
Rawat, V., Jain, A., Shrimali, V., & Raghuvanshi, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-99810-7_6
CLASSIFICAÇÃO COMPUTACIONAL DA SEGREGAÇÃO CENTRAL DE PLACAS PELO PROCESSAMENTO DE IMAGENS DE IMPRESSÕES DE BAUMANN
Ramstorfer, F., Nunes, A. E., & Ret, L. C. P. (2014) · ABM Proceedings · doi:10.5151/1982-9345-24179
Convolutional neural networks-based anti-weapon detection for safe 3D printing
Pham, G. N., Lee, S., & Kwon, K. (2019) · doi:10.1117/12.2519447
IMPORT LIBRARY DEVELOPMENT APPROACH FOR NEURAL NETWORKS IN NNEF FORMAT FOR OPENVX STANDARD
Панфилова, К. В., Vorotnev, D. V., Голованов, Р. В., et al. (2018) · Issues of radio electronics · doi:10.21778/2218-5453-2018-8-112-119
Comparison of ANFIS and ANN modeling for predicting the behavior of a catalytic methane reformer
Parvizi, B., Khanlarkhani, A., Palizdar, Y., et al. (2019) · Bulgarian Chemical Communications · doi:10.34049/bcc.51.2.4778
A Novel Prediction Perspective to the Bending Over Sheave Fatigue Lifetime of Steel Wire Ropes by Means of Artificial Neural Networks
Onur, T. Ö., & Onur, Y. A. (2019) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-7998-0301-0.ch003
Energy Storage System Sitting and Sizing for Renewable Support
Canha, L. N., Rangel, C. A. S., & Adeyanju, O. (2020) · Energy systems · doi:10.1007/978-3-030-36115-0_5
Soft Computing-Based Control System of Intelligent Robot Navigation
Volná, E., Kotyrba, M., & Bradáč, V. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-42058-1_32
Keras: The Best Machine Learning Process for Image Recognition and Classification
Rimal, Y. (2020) · Learning and analytics in intelligent systems · doi:10.1007/978-3-030-42363-6_123 · Cited by 1
tanh Neurons Are Bayesian Decision Makers
Bauckhage, C., Sifa, R., & Hecker, D. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-55190-2_56
Yuragi-Based Virtual Network Control
Koizumi, Y. (2020) · Springer eBooks · doi:10.1007/978-981-33-4976-6_4
Resurrection of Neural Networks
Purves, D. (2021) · Springer eBooks · doi:10.1007/978-3-030-71064-4_6
Neural networks based on the 1С:enterprise 8.3 platform in data analysis systems for self-driving cars
Prokurovsky, A., & Gematudinov, R. А. (2021) · Informatics and Education · doi:10.32517/0234-0453-2021-36-3-50-55
Hydrological Processes of the Changjiang River
Dai, Z. (2021) · Springer eBooks · doi:10.1007/978-981-16-3771-1_2
On-chip online learning and inference for photonic pattern recognition
Márquez, B. A., Guo, Z., Morison, H., Shekhar, S., Chrostowski, L., Prucnal, P. R., & Shastri, B. J. (2021) · Conference on Lasers and Electro-Optics · doi:10.1364/cleo_si.2021.sm1b.5
Analysing the Impact of Human Behaviour over Machine Learning
Yadav, S. S., & Verma, B. (2015) · Advances in intelligent systems and computing · doi:10.1007/978-81-322-2268-2_4
Autonomous Robot Control by Neural Networks
Pinto, A. B., Barbosa, R. S., & Silva, M. F. (2014) · Lecture notes in electrical engineering · doi:10.1007/978-3-319-10380-8_57
Modelling the Aerodynamic Subsystem
Gambier, A. (2022) · Advances in industrial control · doi:10.1007/978-3-030-84895-8_3
References
Dawson, M. R. W. (2004) · Minds and Machines · doi:10.1002/9780470752999.refs
Bringing Networks to the Next Level: Self-modeling Networks for Adaptivity and Control of Mental Models
Jan Treur, A. (2022) · Studies in systems, decision and control · doi:10.1007/978-3-030-85821-6_2
Machine Learning
Hannachi, A. (2021) · Springer atmospheric sciences · doi:10.1007/978-3-030-67073-3_17
Speech Coder Identification Using Chaotic Features Based on Steganalyzer Models
Yargicoglu, A. U., & İlk, H. G. (2012) · Communications - Scientific letters of the University of Zilina · doi:10.26552/com.c.2012.2.63-69
Abordagens evolutivas para otimização de redes neurais convolucionais baseadas em algoritmos genéticos
Mendes, R. (2021) · doi:10.14393/ufu.di.2021.694
Application of Sampling Theory to Forecast Ozone by Neural Network
Pelliccioni, A., & Cotroneo, R. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34478-7_28
Mathematical Model of Neural Network Development for Morphological Assessment of Repair and Remodeling of Bone Defect
Fedosova, N. V., Gn, B., & Mashoshin, D. V. (2022) · Mathematical Models and Computer Simulations · doi:10.1134/s2070048222020065
A Novel Prediction Perspective to the Bending Over Sheave Fatigue Lifetime of Steel Wire Ropes by Means of Artificial Neural Networks
Onur, T. Ö., & Onur, Y. A. (2021) · IGI Global eBooks · doi:10.4018/978-1-6684-2408-7.ch030
References
Dawson, M. R. W. (2005) · Wiley eBooks · doi:10.1002/9780470694077.refs
Neural Networks
Ertel, W. (2011) · Undergraduate topics in computer science · doi:10.1007/978-0-85729-299-5_9
Quantum Backpropagation Neural Network Approach for Modeling of Phenol Adsorption from Aqueous Solution by Orange Peel Ash
Bhattacharjee, S., Bhattacharyya, S., & Mondal, N. K. (2014) · IGI Global eBooks · doi:10.4018/978-1-4666-5125-8.ch057
Bibliography
(2007) · doi:10.1201/9781420043037.axa
Parameter Training
Axelson-Fisk, M. (2015) · Computational biology · doi:10.1007/978-1-4471-6693-1_6
Parameter Training
Axelson-Fisk, M. (2010) · Computational biology · doi:10.1007/978-1-84996-104-2_6
Bibliography
(2007) · CRC Press eBooks · doi:10.1201/9781420043037.bmatt
Characterization and Classification of Local Protein Surfaces Using Self-Organizing Map
Sael, L., & Kihara, D. (2013) · IGI Global eBooks · doi:10.4018/978-1-4666-1785-8.ch004
Bibliography
(2010) · Chapman & Hall/CRC data mining and knowledge discovery series · doi:10.1201/b10328-7
Bio-Inspired Computing through Artificial Neural Network
Dash, N., Priyadarshini, R., Mishra, B. K., & Misra, R. (2017) · IGI Global eBooks · doi:10.4018/978-1-5225-1908-9.ch053
1. Introduction
(2016) · CRC Press eBooks · doi:10.1201/9781315368740-2
Kinetic, Artificial Neural Network and Statistical Modeling to Optimize the Parameters of the Air Flotation Process to Remove Latex Suspended Solids in Abs WastewaterPreprint
Wei, M., Meng, X., Tan, J., et al. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4136342
Current and prospective estimate of counterparty risk through dynamic neural networks
Energia, D., Agnese, A., Giribone, P. G., BPER), B. C. (., & Querci, F. (2022) · Risk Management Magazine · doi:10.47473/2020rmm0112
PANDEMİ SÜRECİNDE ŞEHİR İÇİ YOLCU TAŞIMA SAYILARININ VERİ MADENCİLİĞİ YÖNTEMİ İLE BELİRLENMESİ: KAYSERİ ÖRNEĞİ
Canbulut, G. (2022) · Mühendislik Bilimleri ve Tasarım Dergisi · doi:10.21923/jesd.925178
Efficiency Increase through Data-Based Modeling of Quality and Production Cost Factors in the Nonwoven Industry
Cloppenburg, F. (2023) · Springer eBooks · doi:10.1007/978-3-662-66509-1_17
Análisis de expedientes clínicos que apoyan al diagnóstico de cáncer a partir de memorias asociativas evolutivas
Vizueth, F. T., & TORRES VIZUETH, F. 3. (2022) · doi:10.24275/uama.6732.9484
Scoring of Professional Longevity of Patients with Arterial Hypertension of Military Personnel – the Participants of Modern Armed Conflicts
Voronko, A., Selіuk, O., Buzhenko, A., et al. (2022) · Family medicine European practices · doi:10.30841/2786-720x.4.2022.274623
Machine Learning Approach to Classify Breast Tissues: A Case Study Using Six-classed Breast Tissue Data
Santharooban, S., & Abeysundara, S. P. (2022) · Sri Lankan Journal of Applied Statistics · doi:10.4038/sljastats.v23i3.8081
Comprehensive Study of Rheological Behaviour of Cao-Mgo-Sio2-Al2o3-B2o3 System with Varying B2o3 Content Up to 30 Wt% at Basicity of 0.4Preprint
Řeháčková, L., Novák, V., Tokarský, J., et al. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4528247
Reduction of Selective Harmonics in PWM AC Chopper Using Artificial Neural Network
Sabir, S. U., & Koshti, A. S. (2023) · International Conference on Integration of Computational Intelligent System (ICICIS) · doi:10.1109/icicis56802.2023.10430273
BP Neural Network-Based Drug Sale Forecasting Model Design
He, Y., Gong, Z., Han, D. S., et al. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-1332-5_18
From the Perceptron to Artificial Neural Networks
Sandfeld, S. (2023) · The Materials Research Society series · doi:10.1007/978-3-031-46565-9_17
Recognizing Volcano-Tectonic Events Employing Ann at a Dormant StratovolcanoPreprint
Que-Salinas, S. U., Montiel, F. C., & Sieron, K. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4823458
Determining International Irregularity Index (IRI) Values Through Artificial Neural Network (ANN) Modelling
Aslan, H., Kıyıldı, R. K., & Ermiş, K. (2025) · Academic Platform Journal of Engineering and Smart Systems · doi:10.21541/apjess.1542797
Performance Evaluation of Artificial Neural Network Algorithms: A Case of Classification Problem
Srivastava, D. K., & Akhtar, M. A. (2025) · Signals and communication technology · doi:10.1007/978-3-031-68952-9_61
Gestão Preditiva de Falhas em Dutos por Fadiga: uma Abordagem Híbrida de Elementos Finitos e Redes Neurais
Fragata, V. H. F., Santander, E. J. O., Fontes, J. V. H., et al. (2025) · Revista de Gestão e Secretariado (Management and Administrative Professional Review) · doi:10.7769/gesec.v16i6.4991
Output Voltage Control of a Synchronous Buck DC/DC Converter Using Artificial Neural Networks
Šimko, J., Praženica, M., Koňarik, R., et al. (2025) · Algorithms · doi:10.3390/a18090555
Artificial Neural Network (ANN) Based Structural Health Monitoring for Damage Detection in RC Frame
Khan, F. U. F., & Ahmed, A. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-3-031-84331-0_86 · Cited by 1
A Higher-Order Ising Model with Gradient-Free Update
Zeng, G. L. (2025) · Axioms · doi:10.3390/axioms14120879
Non-fickian Solute Transport
Kulasiri, D. (2013) · Advances in geophysical and environmental mechanics and mathematics · doi:10.1007/978-3-642-34985-0_1
Multi-stage predictive framework for early anomaly detection and real-time alerts in data center thermal systems
Patel, R. K., Yang, Y., Kulkarni, R., et al. (2026) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-025-17273-1
On WRF hybrid-dynamic regression approach in heavy rainfall forecasting over the Northern of Thailand
Sirisombat, N., Chinram, R., Panityakul, T., et al. (2026) · Frontiers in Environmental Science · doi:10.3389/fenvs.2026.1659521
Fast variable selection under $$\ell _0$$ regularization in high-dimensions
Xiao, Y., & Walker, S. G. (2026) · Statistics and Computing · doi:10.1007/s11222-026-10853-5
“Importantly, the Hopﬁeld network is proven to be solved in polynomial time, which is remarkably fast, see Fausett (1994); Takefuji (1992); Bruck (1990); Rojas (1996).”
Development of Novel Metal-Thiosemicarbazone Complexes Targeting HT-116 Cell Line for Anti-Colon Cancer Using in Silico Methods
Quang, N. M., & Minh, N. H. (2025) · International Conference on Health Science and Technology (ICHST) · doi:10.1109/ichst66555.2025.11428520
“In addition, R 2train , Q 2validation , and Q 2test were used to analyze the QSAR ANN model's training phase [5,21].”
Treillis de Galois et réseaux de neurones : une approche constructive d'architecture des réseaux de neurones
Tsopzé, N. (2010) · theses.fr (ABES) · doi:10.70675/d337c17cz108az44bdz843cz6b57a560e0d7
Réduction des coûts de développement de systèmes de reconnaissance de la parole à grand vocabulaire
da Silva, T. F. (2014) · doi:10.70675/ef288619z66ddz4cf3z97f7z2111e01faf53
Applicabilité de la réduction de modèles à la conception aérothermique collaborative des systèmes d'air secondaire des turbomachines
Costini, P. (2017) · doi:10.70675/594d4c72ze222z42b1z9ab0ze423defb358c
Deep neural networks for source separation and noise-robust speech recognition
Nugraha, A. A. (2017) · theses.fr (ABES) · doi:10.70675/3491cabazda49z4945zbaf4z883f0ecfe928
Growth kinetics of individual carbon nanotubes studied by in situ optical microscopy
Pimonov, V. (2021) · theses.fr (ABES) · doi:10.70675/ff3af857z618ez43e9za2b5zfb2a1c62568e
Pictorial composition : modeling, perception & creation
Lelièvre, P. (2022) · theses.fr (ABES) · doi:10.70675/cc153babzed95z4048z9594z3b1b65403dbe
Autoencoder Mathematical Model for Analysis of Electrochemical Impedance of Chemical Power Sources
Popov, A. (2026) · Mathematical Models and Computer Simulations · doi:10.1134/s2070048225700759
Tracing Genetic Images Formed During Evolution
Kasperski, A. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27093864
“The online backpropagation algorithm was used to teach the neural networks [96].”
Neural network‐based optimal parameter identification and variable‐gain adaptive integral terminal sliding mode control for flexible joint robots
葛欣鹏, Guo, K., Ren, X., et al. (2026) · Asian Journal of Control · doi:10.1002/asjc.70183
Prediction of Transmission Line Power in a Scaled Down IEEE 5 Bus System Using Machine Learning Technique
Raghavan, S., Vishwash, G. P., & Lekshmi, R. R. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-95-0705-4_1

Authors: Rojas, R.
Affiliations: Freie Universität Berlin (DE)
Corresponding author: Rojas, Raúl
ORCID: Rojas, Raúl
Venue: Springer eBooks
Publisher: Springer Berlin Heidelberg
Published: 1996
DOI: 10.1007/978-3-642-61068-4
OpenAlex: W4241395986
Cited by: 810
References: 0
Citations by year: 2026: 15 · 2025: 20 · 2024: 24 · 2023: 40 · 2022: 38 · 2021: 50 · 2020: 47 · 2019: 47 · 2018: 61 · 2017: 51 · 2016: 62 · 2015: 53 · 2014: 49 · 2013: 28 · 2012: 35
FWCI: 1.07
Type: book
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
Locations: Springer eBooks (publishedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
Keywords: artificial neural networks, neural network theory, computational model, computing paradigms

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Neural Networks

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cited by

Related

Metadata

Record

About these checks