Pub Date : 2019-03-01DOI: 10.1109/CISS.2019.8692902
N. Rodríguez, P. Julián, E. Paolini
Neural Networks (NN) have been a matter of research because of their capability to solve complex problems where other topologies fail. However, current implementations of practical NNs require powerful computers for their deployment, like the ones used in data centers which typically require a high energy budget. This work proposes a simplicial piecewise linear algorithm as an alternative to implement NNs, which in addition can be implemented in low-power microelectronics. We illustrate the feasibility of the approach giving some examples where the simplicial algorithms replace conventional NNs with similar results.
{"title":"A Simplicial Piecewise Linear Approach for Efficient Hardware Realization of Neural Networks : (Invited Presentation)","authors":"N. Rodríguez, P. Julián, E. Paolini","doi":"10.1109/CISS.2019.8692902","DOIUrl":"https://doi.org/10.1109/CISS.2019.8692902","url":null,"abstract":"Neural Networks (NN) have been a matter of research because of their capability to solve complex problems where other topologies fail. However, current implementations of practical NNs require powerful computers for their deployment, like the ones used in data centers which typically require a high energy budget. This work proposes a simplicial piecewise linear algorithm as an alternative to implement NNs, which in addition can be implemented in low-power microelectronics. We illustrate the feasibility of the approach giving some examples where the simplicial algorithms replace conventional NNs with similar results.","PeriodicalId":123696,"journal":{"name":"2019 53rd Annual Conference on Information Sciences and Systems (CISS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128609355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1109/CISS.2019.8692810
C. Botts
In this paper, I focus on the estimates produced by the extended Kalman filter (EKF) and their corresponding predicted error covariance matrices. The EKF is often used in place of the standard Kalman filter when the functional relationship between consecutive states and/or the functional relationship between the states and their measurements is not linear. In these cases, the state estimates and their predicted error covariances are biased. In this paper, I mathematically show that as the nonlinear relationship between the states and the measurements approaches linearity, these biases go to 0.
{"title":"A Note on the Limiting Properties of the Extended Kalman Filter’s Estimates : Invited Presentation","authors":"C. Botts","doi":"10.1109/CISS.2019.8692810","DOIUrl":"https://doi.org/10.1109/CISS.2019.8692810","url":null,"abstract":"In this paper, I focus on the estimates produced by the extended Kalman filter (EKF) and their corresponding predicted error covariance matrices. The EKF is often used in place of the standard Kalman filter when the functional relationship between consecutive states and/or the functional relationship between the states and their measurements is not linear. In these cases, the state estimates and their predicted error covariances are biased. In this paper, I mathematically show that as the nonlinear relationship between the states and the measurements approaches linearity, these biases go to 0.","PeriodicalId":123696,"journal":{"name":"2019 53rd Annual Conference on Information Sciences and Systems (CISS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127031736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1109/CISS.2019.8692878
Chunying Jia, M. A. Akhonda, Qunfang Long, V. Calhoun, S. Waldstein, T. Adalı
Fusing datasets from different brain signal modalities improves accuracy in finding biomarkers of neuropsychiatric diseases. Several approaches, such as joint independent component analysis (ICA) and independent vector analysis (IVA), are useful but fall short of exploring multiple associations between different modalities, especially for the case where one underlying component in one modality might have multiple associations with others in another modality. This relationship is possible since one component in a given modality might have associations such as subject covariation with multiple components in another modality. We show that the consecutive independence and correlation transform (C-ICT) model, which successively performs ICA and canonical correlation analysis, is able to discover such multiple associations. C-ICT has been demonstrated to be useful for the fusion of functional magnetic resonance imaging (fMRI) and electroencephalography data but has not been tested for other data combinations. In this study, we apply the C-ICT to fuse fMRI and MRI-based diffusion tensor imaging (DTI) datasets collected from healthy controls and patients with schizophrenia. In addition to independent components that show significant differences between the two groups in the fMRI and DTI datasets separately, we find multiple associations between these components from the two modalities, which provide a unique potential biomarker for schizophrenia.
{"title":"C-ICT for Discovery of Multiple Associations in Multimodal Imaging Data: Application to Fusion of fMRI and DTI Data","authors":"Chunying Jia, M. A. Akhonda, Qunfang Long, V. Calhoun, S. Waldstein, T. Adalı","doi":"10.1109/CISS.2019.8692878","DOIUrl":"https://doi.org/10.1109/CISS.2019.8692878","url":null,"abstract":"Fusing datasets from different brain signal modalities improves accuracy in finding biomarkers of neuropsychiatric diseases. Several approaches, such as joint independent component analysis (ICA) and independent vector analysis (IVA), are useful but fall short of exploring multiple associations between different modalities, especially for the case where one underlying component in one modality might have multiple associations with others in another modality. This relationship is possible since one component in a given modality might have associations such as subject covariation with multiple components in another modality. We show that the consecutive independence and correlation transform (C-ICT) model, which successively performs ICA and canonical correlation analysis, is able to discover such multiple associations. C-ICT has been demonstrated to be useful for the fusion of functional magnetic resonance imaging (fMRI) and electroencephalography data but has not been tested for other data combinations. In this study, we apply the C-ICT to fuse fMRI and MRI-based diffusion tensor imaging (DTI) datasets collected from healthy controls and patients with schizophrenia. In addition to independent components that show significant differences between the two groups in the fMRI and DTI datasets separately, we find multiple associations between these components from the two modalities, which provide a unique potential biomarker for schizophrenia.","PeriodicalId":123696,"journal":{"name":"2019 53rd Annual Conference on Information Sciences and Systems (CISS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130567380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1109/CISS.2019.8692785
R. Baxter, W. Levy
This paper studies the developmental dynamics of connectivity and function of a three layer neural network that starts with zero connections. Adaptive synaptogenesis networks combine random synaptogenesis, associative synaptic modification, and synaptic shedding to construct sparse networks that develop codes useful for discriminating input patterns. Empirical observations of brain development inspire several extensions to adaptive synaptogenesis networks. These extensions include: (i) multiple neuronal layers, (ii) neuron survival and death based on information transmission, and (iii) bigrade growth factor signaling to control the onset of synaptogenesis in succeeding layers and to control neuron survival and death in preceding layers. Simulations of the network model demonstrate the parametric and functional control of both performance and energy expenditures, where performance is measured in terms of information loss and classification errors, and energy expenditures are assumed to be a function of the number of neurons. Major insights from this study include (a) the key role a neural layer between two other layers has in controlling synaptogenesis and neuron elimination, (b) the performance and energy-savings benefits of delaying the onset of synaptogenesis in a succeeding layer, and (c) the elimination of neurons is accomplished without significantly degrading information transfer or classification performance while providing energy savings and code compression.
{"title":"Multilayered Neural Networks With Sparse, Data-driven Connectivity and Balanced Information and Energy Efficiency","authors":"R. Baxter, W. Levy","doi":"10.1109/CISS.2019.8692785","DOIUrl":"https://doi.org/10.1109/CISS.2019.8692785","url":null,"abstract":"This paper studies the developmental dynamics of connectivity and function of a three layer neural network that starts with zero connections. Adaptive synaptogenesis networks combine random synaptogenesis, associative synaptic modification, and synaptic shedding to construct sparse networks that develop codes useful for discriminating input patterns. Empirical observations of brain development inspire several extensions to adaptive synaptogenesis networks. These extensions include: (i) multiple neuronal layers, (ii) neuron survival and death based on information transmission, and (iii) bigrade growth factor signaling to control the onset of synaptogenesis in succeeding layers and to control neuron survival and death in preceding layers. Simulations of the network model demonstrate the parametric and functional control of both performance and energy expenditures, where performance is measured in terms of information loss and classification errors, and energy expenditures are assumed to be a function of the number of neurons. Major insights from this study include (a) the key role a neural layer between two other layers has in controlling synaptogenesis and neuron elimination, (b) the performance and energy-savings benefits of delaying the onset of synaptogenesis in a succeeding layer, and (c) the elimination of neurons is accomplished without significantly degrading information transfer or classification performance while providing energy savings and code compression.","PeriodicalId":123696,"journal":{"name":"2019 53rd Annual Conference on Information Sciences and Systems (CISS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129286502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1109/CISS.2019.8692896
H. Yazdani, A. Vosoughi
In this paper, we consider a cognitive radio (CR) system consisting of a primary user (PU) and a pair of secondary user transmitter (SUtx) and secondary user receiver (SUrx). The SUtx is equipped with a reconfigurable antenna (RA) which divides the angular space into M sectors. The RA chooses one sector among M sectors for its data transmission to (SUrx). The SUtx first senses the channel and monitors the activity of PU for a duration of Tsen seconds. We refer to this period as channel sensing phase. Depending on the outcome of this phase, SUtx stays in this phase or enters the next phase, which we refer to as transmission phase. The transmission phase itself consists of two phases: channel training phase followed by data transmission phase. During the former phase, SUtx sends pilot symbols to enable channel training and estimation at (SUrx). The SUrx selects the best beam (sector) for data transmission and feeds back the index of the selected beam as well as its corresponding channel gain. We also derive the probability of determining the true beam and take into account this probability in our system design. During the latter phase, SUtx sends data symbols to SUrx over the selected beam with constant power $Phi$ if the gain corresponding to the selected beam is bigger than the threshold $zeta$. We find the optimal channel sensing duration Tsen, the optimal power level $Phi$ and a optimal threshold $zeta$, such that the ergodic capacity of CR system is maximized, subject to average interference and power constraints. In addition, we derive closed form expressions for outage and symbol error probabilities of our CR system.
{"title":"On the Spectrum Sensing, Beam Selection and Power Allocation in Cognitive Radio Networks Using Reconfigurable Antennas","authors":"H. Yazdani, A. Vosoughi","doi":"10.1109/CISS.2019.8692896","DOIUrl":"https://doi.org/10.1109/CISS.2019.8692896","url":null,"abstract":"In this paper, we consider a cognitive radio (CR) system consisting of a primary user (PU) and a pair of secondary user transmitter (SUtx) and secondary user receiver (SUrx). The SUtx is equipped with a reconfigurable antenna (RA) which divides the angular space into M sectors. The RA chooses one sector among M sectors for its data transmission to (SUrx). The SUtx first senses the channel and monitors the activity of PU for a duration of Tsen seconds. We refer to this period as channel sensing phase. Depending on the outcome of this phase, SUtx stays in this phase or enters the next phase, which we refer to as transmission phase. The transmission phase itself consists of two phases: channel training phase followed by data transmission phase. During the former phase, SUtx sends pilot symbols to enable channel training and estimation at (SUrx). The SUrx selects the best beam (sector) for data transmission and feeds back the index of the selected beam as well as its corresponding channel gain. We also derive the probability of determining the true beam and take into account this probability in our system design. During the latter phase, SUtx sends data symbols to SUrx over the selected beam with constant power $Phi$ if the gain corresponding to the selected beam is bigger than the threshold $zeta$. We find the optimal channel sensing duration Tsen, the optimal power level $Phi$ and a optimal threshold $zeta$, such that the ergodic capacity of CR system is maximized, subject to average interference and power constraints. In addition, we derive closed form expressions for outage and symbol error probabilities of our CR system.","PeriodicalId":123696,"journal":{"name":"2019 53rd Annual Conference on Information Sciences and Systems (CISS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114171573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1109/CISS.2019.8692869
Zisheng Wang, Rick S. Blum
Whenever a system operator is led to believe that the system topology is different from the actual system topology, we call this a topology attack. These attacks can cause tremendous damage from the result of improper network control. A number of algorithms to detect these attacks for electrical networks have been suggested in the literature, but such algorithms are lacking for gas networks. In this paper, we design a new detection algorithm to detect topology attacks in gas networks based on modifying the generalized likelihood ratio test in a novel way. Simulation results indicate the detection algorithm will provide good performance given a sufficient amount of data. Results also demonstrate that the performance also depends on the actual topology.
{"title":"Topology Attack Detection in Natural Gas Delivery Networks","authors":"Zisheng Wang, Rick S. Blum","doi":"10.1109/CISS.2019.8692869","DOIUrl":"https://doi.org/10.1109/CISS.2019.8692869","url":null,"abstract":"Whenever a system operator is led to believe that the system topology is different from the actual system topology, we call this a topology attack. These attacks can cause tremendous damage from the result of improper network control. A number of algorithms to detect these attacks for electrical networks have been suggested in the literature, but such algorithms are lacking for gas networks. In this paper, we design a new detection algorithm to detect topology attacks in gas networks based on modifying the generalized likelihood ratio test in a novel way. Simulation results indicate the detection algorithm will provide good performance given a sufficient amount of data. Results also demonstrate that the performance also depends on the actual topology.","PeriodicalId":123696,"journal":{"name":"2019 53rd Annual Conference on Information Sciences and Systems (CISS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131455443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1109/CISS.2019.8692870
Dimitris Perdios, Adrien Besson, Florian Martinez, Manuel Vonlanthen, M. Arditi, J. Thiran
Recently, many pulse-echo ultrasound (US) imaging methods have relied on the transmission of unfocused wavefronts. Such a strategy allows for very high frame rates at the cost of a degraded image quality. In this work, we present a regularized inverse problem approach and a highly efficient modeling of the physical measurement process to reconstruct high-quality US images from unfocused wavefronts. We compare it against a deep neural network (DNN) approach on the plane wave imaging challenge in medical ultrasound (PICMUS) and show that the use of carefully designed and trained DNN can overcome the limitations of standard image processing priors, which fail at capturing the very specific nature of US images accurately.
{"title":"On Problem Formulation, Efficient Modeling and Deep Neural Networks for High-Quality Ultrasound Imaging : Invited Presentation","authors":"Dimitris Perdios, Adrien Besson, Florian Martinez, Manuel Vonlanthen, M. Arditi, J. Thiran","doi":"10.1109/CISS.2019.8692870","DOIUrl":"https://doi.org/10.1109/CISS.2019.8692870","url":null,"abstract":"Recently, many pulse-echo ultrasound (US) imaging methods have relied on the transmission of unfocused wavefronts. Such a strategy allows for very high frame rates at the cost of a degraded image quality. In this work, we present a regularized inverse problem approach and a highly efficient modeling of the physical measurement process to reconstruct high-quality US images from unfocused wavefronts. We compare it against a deep neural network (DNN) approach on the plane wave imaging challenge in medical ultrasound (PICMUS) and show that the use of carefully designed and trained DNN can overcome the limitations of standard image processing priors, which fail at capturing the very specific nature of US images accurately.","PeriodicalId":123696,"journal":{"name":"2019 53rd Annual Conference on Information Sciences and Systems (CISS)","volume":"233 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114099495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1109/CISS.2019.8693041
S. Dasgupta, S. Sadhu, T. Ghoshal
A novel method for designing Disturbance Observer (DOB), an essential component of Active Anti-Disturbance Control (AADC) schemes, for nonlinear Multiple Input Multiple Output (MIMO) systems has been proposed. In contrast to the prevailing observer gain based approach, the proposed DOB design methodology employs the MIMO “Hirschorn Inverse” technique. The overall architecture and the related design pragmatics have been discussed. Feasibility and disturbance estimation performance of the proposed approach have been illustrated by a Quadruple Tank process with two inputs and two outputs.
{"title":"Disturbance Observers for Nonlinear MIMO Systems- An Alternative Design Approach","authors":"S. Dasgupta, S. Sadhu, T. Ghoshal","doi":"10.1109/CISS.2019.8693041","DOIUrl":"https://doi.org/10.1109/CISS.2019.8693041","url":null,"abstract":"A novel method for designing Disturbance Observer (DOB), an essential component of Active Anti-Disturbance Control (AADC) schemes, for nonlinear Multiple Input Multiple Output (MIMO) systems has been proposed. In contrast to the prevailing observer gain based approach, the proposed DOB design methodology employs the MIMO “Hirschorn Inverse” technique. The overall architecture and the related design pragmatics have been discussed. Feasibility and disturbance estimation performance of the proposed approach have been illustrated by a Quadruple Tank process with two inputs and two outputs.","PeriodicalId":123696,"journal":{"name":"2019 53rd Annual Conference on Information Sciences and Systems (CISS)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116064665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1109/CISS.2019.8692909
A. Koochakzadeh, P. Pal
This paper considers canonical polyadic (CP) decomposition of symmetric tensors of arbitrary even order. In earlier work [1], we showed that decomposition of such tensors is equivalent to solving a system of quadratic equations. As part of ongoing work, we further show that for almost all tensors, singular value decomposition of a certain matrix can uniquely obtain the solution to the system of quadratic equations. Our proposed algorithm is able to find the CP-decomposition, even in the regime where the CP-rank far exceeds the dimensions of the tensor (overcomplete tensors). We further show that using the symmetry of the tensor, it is possible to only use a certain type of flattening to significantly reduce the number of quadratic equations. Also, we show that the computational complexity can be reduced by a sketching technique, without any performance loss.
{"title":"On flattening of symmetric tensors and identification of latent factors","authors":"A. Koochakzadeh, P. Pal","doi":"10.1109/CISS.2019.8692909","DOIUrl":"https://doi.org/10.1109/CISS.2019.8692909","url":null,"abstract":"This paper considers canonical polyadic (CP) decomposition of symmetric tensors of arbitrary even order. In earlier work [1], we showed that decomposition of such tensors is equivalent to solving a system of quadratic equations. As part of ongoing work, we further show that for almost all tensors, singular value decomposition of a certain matrix can uniquely obtain the solution to the system of quadratic equations. Our proposed algorithm is able to find the CP-decomposition, even in the regime where the CP-rank far exceeds the dimensions of the tensor (overcomplete tensors). We further show that using the symmetry of the tensor, it is possible to only use a certain type of flattening to significantly reduce the number of quadratic equations. Also, we show that the computational complexity can be reduced by a sketching technique, without any performance loss.","PeriodicalId":123696,"journal":{"name":"2019 53rd Annual Conference on Information Sciences and Systems (CISS)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123450681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1109/CISS.2019.8692913
W. Gray, G. Venkatesh, L. A. D. Espinosa
A formal power series over a set of noncommuting indeterminants using iterated integrals as the coefficients is called a Chen series, named after the mathematician K.-T. Chen. The first goal of this paper is to give a brief overview of Chen series and their algebraic structures as a kind of reference point. The second goal is to describe its discrete-time analogue in detail and then apply the concept in two problems, the time discretization problem for nonlinear control systems and the machine learning problem for dynamical systems.
{"title":"Discrete-time Chen Series for Time Discretization and Machine Learning","authors":"W. Gray, G. Venkatesh, L. A. D. Espinosa","doi":"10.1109/CISS.2019.8692913","DOIUrl":"https://doi.org/10.1109/CISS.2019.8692913","url":null,"abstract":"A formal power series over a set of noncommuting indeterminants using iterated integrals as the coefficients is called a Chen series, named after the mathematician K.-T. Chen. The first goal of this paper is to give a brief overview of Chen series and their algebraic structures as a kind of reference point. The second goal is to describe its discrete-time analogue in detail and then apply the concept in two problems, the time discretization problem for nonlinear control systems and the machine learning problem for dynamical systems.","PeriodicalId":123696,"journal":{"name":"2019 53rd Annual Conference on Information Sciences and Systems (CISS)","volume":"192 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123522429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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