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Yu H, Chen C, Hu X, Yang H. An Efficient Recognition Method for Orbital Angular Momentum via Adaptive Deep ELM. SENSORS (BASEL, SWITZERLAND) 2023; 23:8737. [PMID: 37960437 PMCID: PMC10649547 DOI: 10.3390/s23218737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 10/18/2023] [Accepted: 10/24/2023] [Indexed: 11/15/2023]
Abstract
For orbital angular momentum (OAM) recognition in atmosphere turbulence, how to design a self-adapted model is a challenging problem. To address this issue, an efficient deep learning framework that uses a derived extreme learning machine (ELM) has been put forward. Different from typical neural network methods, the provided analytical machine learning model can match the different OAM modes automatically. In the model selection phase, a multilayer ELM is adopted to quantify the laser spot characteristics. In the parameter optimization phase, a fast iterative shrinkage-thresholding algorithm makes the model present the analytic expression. After the feature extraction of the received intensity distributions, the proposed method develops a relationship between laser spot and OAM mode, thus building the steady neural network architecture for the new received vortex beam. The whole recognition process avoids the trial and error caused by user intervention, which makes the model suitable for a time-varying atmospheric environment. Numerical simulations are conducted on different experimental datasets. The results demonstrate that the proposed method has a better capacity for OAM recognition.
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Affiliation(s)
- Haiyang Yu
- School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China; (C.C.); (X.H.); (H.Y.)
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Gul E, Safari MJS. Hybrid Generalized Regularized Extreme Learning Machine Through Gradient-Based Optimizer Model for Self-Cleansing Nondeposition with Clean Bed Mode of Sediment Transport. BIG DATA 2023. [PMID: 36881757 DOI: 10.1089/big.2022.0120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Sediment transport modeling is an important problem to minimize sedimentation in open channels that could lead to unexpected operation expenses. From an engineering perspective, the development of accurate models based on effective variables involved for flow velocity computation could provide a reliable solution in channel design. Furthermore, validity of sediment transport models is linked to the range of data used for the model development. Existing design models were established on the limited data ranges. Thus, the present study aimed to utilize all experimental data available in the literature, including recently published datasets that covered an extensive range of hydraulic properties. Extreme learning machine (ELM) algorithm and generalized regularized extreme learning machine (GRELM) were implemented for the modeling, and then, particle swarm optimization (PSO) and gradient-based optimizer (GBO) were utilized for the hybridization of ELM and GRELM. GRELM-PSO and GRELM-GBO findings were compared to the standalone ELM, GRELM, and existing regression models to determine their accurate computations. The analysis of the models demonstrated the robustness of the models that incorporate channel parameter. The poor results of some existing regression models seem to be linked to the disregarding of the channel parameter. Statistical analysis of the model outcomes illustrated the outperformance of GRELM-GBO in contrast to the ELM, GRELM, GRELM-PSO, and regression models, although GRELM-GBO performed slightly better when compared to the GRELM-PSO counterpart. It was found that the mean accuracy of GRELM-GBO was 18.5% better when compared to the best regression model. The promising findings of the current study not only may encourage the use of recommended algorithms for channel design in practice but also may further the application of novel ELM-based methods in alternative environmental problems.
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Affiliation(s)
- Enes Gul
- Department of Civil Engineering, Inonu University, Malatya, Turkey
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Doroudi S. The Intertwined Histories of Artificial Intelligence and Education. INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION 2022. [DOI: 10.1007/s40593-022-00313-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
AbstractIn this paper, I argue that the fields of artificial intelligence (AI) and education have been deeply intertwined since the early days of AI. Specifically, I show that many of the early pioneers of AI were cognitive scientists who also made pioneering and impactful contributions to the field of education. These researchers saw AI as a tool for thinking about human learning and used their understanding of how people learn to further AI. Furthermore, I trace two distinct approaches to thinking about cognition and learning that pervade the early histories of AI and education. Despite their differences, researchers from both strands were united in their quest to simultaneously understand and improve human and machine cognition. Today, this perspective is neither prevalent in AI nor the learning sciences. I conclude with some thoughts on how the artificial intelligence in education and learning sciences communities might reinvigorate this lost perspective.
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Kafiyan-Safari M, Rouhani M. Adaptive one-pass passive-aggressive radial basis function for classification problems. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.03.047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Yu H, Chen C, Yang H. Two-Stage Game Strategy for Multiclass Imbalanced Data Online Prediction. Neural Process Lett 2020. [DOI: 10.1007/s11063-020-10358-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Zhu L, Lian C, Zeng Z, Su Y. A Broad Learning System with Ensemble and Classification Methods for Multi-step-ahead Wind Speed Prediction. Cognit Comput 2019. [DOI: 10.1007/s12559-019-09698-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Yu H, Sun X, Yan X. Sequential prediction for imbalanced data stream via weighted OS-ELM and dynamic GAN. INTELL DATA ANAL 2019. [DOI: 10.3233/ida-184377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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A Framework for Privacy Quantification: Measuring the Impact of Privacy Techniques Through Mutual Information, Distance Mapping, and Machine Learning. Cognit Comput 2018. [DOI: 10.1007/s12559-018-9604-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Li C, Deng C, Zhou S, Zhao B, Huang GB. Conditional Random Mapping for Effective ELM Feature Representation. Cognit Comput 2018. [DOI: 10.1007/s12559-018-9557-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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An Online Sequential Learning Non-parametric Value-at-Risk Model for High-Dimensional Time Series. Cognit Comput 2017. [DOI: 10.1007/s12559-017-9516-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Liu Y, Vong CM, Wong PK. Extreme Learning Machine for Huge Hypotheses Re-ranking in Statistical Machine Translation. Cognit Comput 2017. [DOI: 10.1007/s12559-017-9452-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Pratama M, Zhang G, Er MJ, Anavatti S. An Incremental Type-2 Meta-Cognitive Extreme Learning Machine. IEEE TRANSACTIONS ON CYBERNETICS 2017; 47:339-353. [PMID: 26812744 DOI: 10.1109/tcyb.2016.2514537] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Existing extreme learning algorithm have not taken into account four issues: 1) complexity; 2) uncertainty; 3) concept drift; and 4) high dimensionality. A novel incremental type-2 meta-cognitive extreme learning machine (ELM) called evolving type-2 ELM (eT2ELM) is proposed to cope with the four issues in this paper. The eT2ELM presents three main pillars of human meta-cognition: 1) what-to-learn; 2) how-to-learn; and 3) when-to-learn. The what-to-learn component selects important training samples for model updates by virtue of the online certainty-based active learning method, which renders eT2ELM as a semi-supervised classifier. The how-to-learn element develops a synergy between extreme learning theory and the evolving concept, whereby the hidden nodes can be generated and pruned automatically from data streams with no tuning of hidden nodes. The when-to-learn constituent makes use of the standard sample reserved strategy. A generalized interval type-2 fuzzy neural network is also put forward as a cognitive component, in which a hidden node is built upon the interval type-2 multivariate Gaussian function while exploiting a subset of Chebyshev series in the output node. The efficacy of the proposed eT2ELM is numerically validated in 12 data streams containing various concept drifts. The numerical results are confirmed by thorough statistical tests, where the eT2ELM demonstrates the most encouraging numerical results in delivering reliable prediction, while sustaining low complexity.
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Yang Y, Wu QMJ. Extreme Learning Machine With Subnetwork Hidden Nodes for Regression and Classification. IEEE TRANSACTIONS ON CYBERNETICS 2016; 46:2885-2898. [PMID: 26552104 DOI: 10.1109/tcyb.2015.2492468] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
As demonstrated earlier, the learning effectiveness and learning speed of single-hidden-layer feedforward neural networks are in general far slower than required, which has been a major bottleneck for many applications. Huang et al. proposed extreme learning machine (ELM) which improves the training speed by hundreds of times as compared to its predecessor learning techniques. This paper offers an ELM-based learning method that can grow subnetwork hidden nodes by pulling back residual network error to the hidden layer. Furthermore, the proposed method provides a similar or better generalization performance with remarkably fewer hidden nodes as compared to other ELM methods employing huge number of hidden nodes. Thus, the learning speed of the proposed technique is hundred times faster compared to other ELMs as well as to back propagation and support vector machines. The experimental validations for all methods are carried out on 32 data sets.
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Antuvan CW, Bisio F, Marini F, Yen SC, Cambria E, Masia L. Role of Muscle Synergies in Real-Time Classification of Upper Limb Motions using Extreme Learning Machines. J Neuroeng Rehabil 2016; 13:76. [PMID: 27527511 PMCID: PMC4986359 DOI: 10.1186/s12984-016-0183-0] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2016] [Accepted: 07/29/2016] [Indexed: 11/26/2022] Open
Abstract
Background Myoelectric signals offer significant insights in interpreting the motion intention and extent of effort involved in performing a movement, with application in prostheses, orthosis and exoskeletons. Feature extraction plays a vital role, and follows two approaches: EMG and synergy features. More recently, muscle synergy based features are being increasingly explored, since it simplifies dimensionality of control, and are considered to be more robust to signal variations. Another important aspect in a myoelectrically controlled devices is the learning capability and speed of performance for online decoding. Extreme learning machine (ELM) is a relatively new neural-network based learning algorithm: its performance hasn’t been explored in the context of online control, which is a more reliable measure compared to offline analysis. To this purpose we aim at focusing our investigation on a myoelectric-based interface which is able to identify and online classify, upper limb motions involving shoulder and elbow. The main objective is to compare the performance of the decoder trained using ELM, for two different features: EMG and synergy features. Methods The experiments are broadly divided in two phases training/calibration and testing respectively. ELM is used to train the decoder using data acquired during the calibration phase. The performance of the decoder is then tested in online motion control by using a simulated graphical user interface replicating the human limb: subjects are requested to control a virtual arm by using their muscular activity. The decoder performance is quantified using ad-hoc metrics based on the following indicators: motion selection time, motion completion time, and classification accuracy. Results Performance has been evaluated for both offline and online contexts. The offline classification results indicated better performance in the case of EMG features, whereas a better classification accuracy for synergy feature was observed for online decoding. Also the other indicators as motion selection time and motion completion time, showed better trend in the case of synergy than time-domain features. Conclusion This work demonstrates better robustness of online decoding of upper-limb motions and motor intentions when using synergy feature. Furthermore, we have quantified the performance of the decoder trained using ELM for online control, providing a potential and viable option for real-time myoelectric control in assistive technology. Electronic supplementary material The online version of this article (doi:10.1186/s12984-016-0183-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Chris Wilson Antuvan
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore
| | - Federica Bisio
- Department of Naval, Electrical, Electronic and Telecommunications Engineering, University of Genoa, Genoa, Italy
| | - Francesca Marini
- Department of Robotics, Brain and Cognitive Sciences, Italian Institute of Technology, Genoa, Italy
| | - Shih-Cheng Yen
- Department of Electrical and Computer Engineering; Singapore Institute of Neurotechnology (SINAPSE), National University of Singapore, Singapore, Singapore
| | - Erik Cambria
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| | - Lorenzo Masia
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore.
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Mirza B, Lin Z. Meta-cognitive online sequential extreme learning machine for imbalanced and concept-drifting data classification. Neural Netw 2016; 80:79-94. [DOI: 10.1016/j.neunet.2016.04.008] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2015] [Revised: 04/08/2016] [Accepted: 04/21/2016] [Indexed: 11/25/2022]
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Venkatesh Babu R, Rangarajan B, Sundaram S, Tom M. Human action recognition in H.264/AVC compressed domain using meta-cognitive radial basis function network. Appl Soft Comput 2015. [DOI: 10.1016/j.asoc.2015.06.054] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Yang Y, Wu QMJ, Wang Y, Zeeshan KM, Lin X, Yuan X. Data Partition Learning With Multiple Extreme Learning Machines. IEEE TRANSACTIONS ON CYBERNETICS 2015; 45:1463-1475. [PMID: 25216495 DOI: 10.1109/tcyb.2014.2352594] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
As demonstrated earlier, the learning accuracy of the single-layer-feedforward-network (SLFN) is generally far lower than expected, which has been a major bottleneck for many applications. In fact, for some large real problems, it is accepted that after tremendous learning time (within finite epochs), the network output error of SLFN will stop or reduce increasingly slowly. This report offers an extreme learning machine (ELM)-based learning method, referred to as the parent-offspring progressive learning method. The proposed method works by separating the data points into various parts, and then multiple ELMs learn and identify the clustered parts separately. The key advantages of the proposed algorithms as compared to the traditional supervised methods are twofold. First, it extends the ELM learning method from a single neural network to a multinetwork learning system, as the proposed multiELM method can approximate any target continuous function and classify disjointed regions. Second, the proposed method tends to deliver a similar or much better generalization performance than other learning methods. All the methods proposed in this paper are tested on both artificial and real datasets.
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Specific Biomarkers: Detection of Cancer Biomarkers Through High-Throughput Transcriptomics Data. Cognit Comput 2015. [DOI: 10.1007/s12559-015-9336-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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Autonomous Driver Based on an Intelligent System of Decision-Making. Cognit Comput 2015; 7:569-581. [PMID: 26380583 PMCID: PMC4564448 DOI: 10.1007/s12559-015-9320-5] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2014] [Accepted: 01/28/2015] [Indexed: 11/06/2022]
Abstract
The paper presents and discusses a system (xDriver) which uses an Intelligent System of Decision-making (ISD) for the task of car driving. The principal subject is the implementation, simulation and testing of the ISD system described earlier in our publications (Kowalczuk and Czubenko in artificial intelligence and soft computing lecture notes in computer science, lecture notes in artificial intelligence, Springer, Berlin, 2010, 2010, In Int J Appl Math Comput Sci 21(4):621–635, 2011, In Pomiary Autom Robot 2(17):60–5, 2013) for the task of autonomous driving. The design of the whole ISD system is a result of a thorough modelling of human psychology based on an extensive literature study. Concepts somehow similar to the ISD system can be found in the literature (Muhlestein in Cognit Comput 5(1):99–105, 2012; Wiggins in Cognit Comput 4(3):306–319, 2012), but there are no reports of a system which would model the human psychology for the purpose of autonomously driving a car. The paper describes assumptions for simulation, the set of needs and reactions (characterizing the ISD system), the road model and the vehicle model, as well as presents some results of simulation. It proves that the xDriver system may behave on the road as a very inexperienced driver.
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Sachnev V, Ramasamy S, Sundaram S, Kim HJ, Hwang HJ. A Cognitive Ensemble of Extreme Learning Machines for Steganalysis Based on Risk-Sensitive Hinge Loss Function. Cognit Comput 2014. [DOI: 10.1007/s12559-014-9268-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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A Two-Stage Methodology Using K-NN and False-Positive Minimizing ELM for Nominal Data Classification. Cognit Comput 2014. [DOI: 10.1007/s12559-014-9253-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Feng R, Xiao Y, Leung CS, Tsang PWM, Sum J. An Improved Fault-Tolerant Objective Function and Learning Algorithm for Training the Radial Basis Function Neural Network. Cognit Comput 2013. [DOI: 10.1007/s12559-013-9236-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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