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Danil V. Prokhorov
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- affiliation: Toyota Technical Center, Ann Arbor, USA
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2020 – today
- 2024
- [i22]Navid Hashemi, Bardh Hoxha, Danil V. Prokhorov
, Georgios Fainekos, Jyotirmoy Deshmukh:
Scaling Learning based Policy Optimization for Temporal Tasks via Dropout. CoRR abs/2403.15826 (2024) - [i21]Mitchell Black, Georgios Fainekos, Bardh Hoxha, Hideki Okamoto, Danil V. Prokhorov
CBFKIT: A Control Barrier Function Toolbox for Robotics Applications. CoRR abs/2404.07158 (2024) - [i20]Hardik Parwana, Mitchell Black, Georgios Fainekos, Bardh Hoxha, Hideki Okamoto, Danil V. Prokhorov:
Model Predictive Path Integral Methods with Reach-Avoid Tasks and Control Barrier Functions. CoRR abs/2407.13693 (2024) - 2023
- [c74]Kandai Watanabe, Georgios Fainekos, Bardh Hoxha, Morteza Lahijanian, Danil V. Prokhorov
Timed Partial Order Inference Algorithm. ICAPS 2023: 639-647 - [c73]Navid Hashemi, Xin Qin, Jyotirmoy V. Deshmukh, Georgios Fainekos, Bardh Hoxha, Danil V. Prokhorov
Risk-Awareness in Learning Neural Controllers for Temporal Logic Objectives. ACC 2023: 4096-4103 - [c72]Hoang-Dung Tran
Quantitative Verification for Neural Networks using ProbStars. HSCC 2023: 4:1-4:12 - [c71]Hoang-Dung Tran
Verification of Recurrent Neural Networks with Star Reachability. HSCC 2023: 6:1-6:13 - [c70]Alexander Bastounis
The Boundaries of Verifiable Accuracy, Robustness, and Generalisation in Deep Learning. ICANN (1) 2023: 530-541 - [c69]Navid Hashemi
A Neurosymbolic Approach to the Verification of Temporal Logic Properties of Learning-enabled Control Systems. ICCPS 2023: 98-109 - [c68]Mitchell Black, Georgios Fainekos, Bardh Hoxha, Danil V. Prokhorov
Safety Under Uncertainty: Tight Bounds with Risk-Aware Control Barrier Functions. ICRA 2023: 12686-12692 - [c67]Yuyang Song, Umesh Gandhi, Danil V. Prokhorov
Design and fabrication of multi-pouch inflatable holding structure with higher payload. RoboSoft 2023: 1-6 - [c66]Jacob Anderson
Pattern Matching for Perception Streams. RV 2023: 251-270 - [i19]Kandai Watanabe, Bardh Hoxha, Danil V. Prokhorov
Timed Partial Order Inference Algorithm. CoRR abs/2302.02501 (2023) - [i18]Navid Hashemi, Bardh Hoxha, Tomoya Yamaguchi, Danil V. Prokhorov
A Neurosymbolic Approach to the Verification of Temporal Logic Properties of Learning enabled Control Systems. CoRR abs/2303.05394 (2023) - [i17]Mitchell Black, Georgios Fainekos, Bardh Hoxha, Danil V. Prokhorov
Safety Under Uncertainty: Tight Bounds with Risk-Aware Control Barrier Functions. CoRR abs/2304.01040 (2023) - [i16]Alexander Bastounis, Alexander N. Gorban, Anders C. Hansen, Desmond J. Higham, Danil V. Prokhorov
The Boundaries of Verifiable Accuracy, Robustness, and Generalisation in Deep Learning. CoRR abs/2309.07072 (2023) - [i15]Hardik Parwana, Mitchell Black, Bardh Hoxha, Hideki Okamoto, Georgios Fainekos, Danil V. Prokhorov
Feasible Space Monitoring for Multiple Control Barrier Functions with application to Large Scale Indoor Navigation. CoRR abs/2312.07803 (2023) - 2022
- [c65]Xiaodong Yang, Tom Yamaguchi, Hoang-Dung Tran, Bardh Hoxha, Taylor T. Johnson, Danil V. Prokhorov
Neural Network Repair with Reachability Analysis. FORMATS 2022: 221-236 - [i14]Navid Hashemi, Xin Qin, Jyotirmoy V. Deshmukh, Georgios Fainekos, Bardh Hoxha, Danil V. Prokhorov
Risk-Awareness in Learning Neural Controllers for Temporal Logic Objectives. CoRR abs/2210.07439 (2022) - [i13]Kenechukwu C. Mbanisi, Hideyuki Kimpara, Zhi Li, Danil V. Prokhorov
Model-based Evaluation of Driver Control Workloads in Haptic-based Driver Assistance Systems. CoRR abs/2210.13609 (2022) - 2021
- [j50]Shakiba Yaghoubi, Keyvan Majd, Georgios Fainekos
Risk-Bounded Control Using Stochastic Barrier Functions. IEEE Control. Syst. Lett. 5(5): 1831-1836 (2021) - [j49]Hideyuki Kimpara
Force Anticipation and Its Potential Implications on Feedforward and Feedback Human Motor Control. Hum. Factors 63(4) (2021) - [c64]Shakiba Yaghoubi, Keyvan Majd, Georgios Fainekos, Tomoya Yamaguchi, Danil V. Prokhorov
Risk-bounded Control using Stochastic Barrier Functions. ACC 2021: 1131-1136 - [c63]Shakiba Yaghoubi, Georgios Fainekos, Tomoya Yamaguchi, Danil V. Prokhorov
Risk-Bounded Control with Kalman Filtering and Stochastic Barrier Functions. CDC 2021: 5213-5219 - [c62]Xiaodong Yang, Taylor T. Johnson, Hoang-Dung Tran, Tomoya Yamaguchi, Bardh Hoxha, Danil V. Prokhorov
Reachability analysis of deep ReLU neural networks using facet-vertex incidence. HSCC 2021: 18:1-18:7 - [c61]Keyvan Majd, Shakiba Yaghoubi, Tomoya Yamaguchi, Bardh Hoxha, Danil V. Prokhorov
Safe Navigation in Human Occupied Environments Using Sampling and Control Barrier Functions. IROS 2021: 5794-5800 - [i12]Keyvan Majd, Shakiba Yaghoubi, Tomoya Yamaguchi, Bardh Hoxha, Danil V. Prokhorov, Georgios Fainekos:
Safe Navigation in Human Occupied Environments Using Sampling and Control Barrier Functions. CoRR abs/2105.01204 (2021) - [i11]Xiaodong Yang, Tomoya Yamaguchi, Hoang-Dung Tran, Bardh Hoxha, Taylor T. Johnson, Danil V. Prokhorov:
Reachability Analysis of Convolutional Neural Networks. CoRR abs/2106.12074 (2021) - [i10]Xiaodong Yang, Tom Yamaguchi, Hoang-Dung Tran, Bardh Hoxha, Taylor T. Johnson, Danil V. Prokhorov:
Neural Network Repair with Reachability Analysis. CoRR abs/2108.04214 (2021) - [i9]Shakiba Yaghoubi, Georgios Fainekos, Tomoya Yamaguchi, Danil V. Prokhorov, Bardh Hoxha:
Risk-Bounded Control with Kalman Filtering and Stochastic Barrier Functions. CoRR abs/2112.14912 (2021) - 2020
- [j48]Fan Yang
Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection. IEEE Trans. Intell. Transp. Syst. 21(4): 1525-1535 (2020) - [j47]Hideyuki Kimpara
Human Model-Based Active Driving System in Vehicular Dynamic Simulation. IEEE Trans. Intell. Transp. Syst. 21(5): 1903-1914 (2020) - [j46]Cumhur Erkan Tuncali
Requirements-Driven Test Generation for Autonomous Vehicles With Machine Learning Components. IEEE Trans. Intell. Veh. 5(2): 265-280 (2020) - [c60]Yuji Date, Takeshi Baba, Bardh Hoxha, Tomoya Yamaguchi, Danil V. Prokhorov
Application of Simulation-Based Methods on Autonomous Vehicle Control with Deep Neural Network: Work-in-Progress. EMSOFT 2020: 1-3 - [c59]Tomoya Yamaguchi, Bardh Hoxha, Danil V. Prokhorov
Specification-guided Software Fault Localization for Autonomous Mobile Systems. MEMOCODE 2020: 1-12
2010 – 2019
- 2019
- [j45]Ivan Yu. Tyukin
Fast construction of correcting ensembles for legacy Artificial Intelligence systems: Algorithms and a case study. Inf. Sci. 485: 230-247 (2019) - [j44]Jifeng Shen
Differential Features for Pedestrian Detection: A Taylor Series Perspective. IEEE Trans. Intell. Transp. Syst. 20(8): 2913-2922 (2019) - [j43]Tomoki Nishi
Merging in Congested Freeway Traffic Using Multipolicy Decision Making and Passive Actor-Critic Learning. IEEE Trans. Intell. Veh. 4(2): 287-297 (2019) - [j42]Wangmeng Zuo
Guest Editorial Special Issue on Discriminative Learning for Model Optimization and Statistical Inference. IEEE Trans. Neural Networks Learn. Syst. 30(10): 2894-2897 (2019) - [j41]Zhiqiang Wan
Model-Free Real-Time EV Charging Scheduling Based on Deep Reinforcement Learning. IEEE Trans. Smart Grid 10(5): 5246-5257 (2019) - [c58]Jyotirmoy V. Deshmukh, James Kapinski, Tomoya Yamaguchi, Danil V. Prokhorov
Learning Deep Neural Network Controllers for Dynamical Systems with Safety Guarantees: Invited Paper. ICCAD 2019: 1-7 - [c57]Danil V. Prokhorov
Toward Next Generation of Autonomous Systems with AI. IJCNN 2019: 1-5 - [i8]Fan Yang, Lei Zhang, Sijia Yu, Danil V. Prokhorov, Xue Mei, Haibin Ling:
Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection. CoRR abs/1901.06340 (2019) - [i7]Cumhur Erkan Tuncali, Georgios Fainekos, Danil V. Prokhorov, Hisahiro Ito, James Kapinski:
Requirements-driven Test Generation for Autonomous Vehicles with Machine Learning Components. CoRR abs/1908.01094 (2019) - 2018
- [j40]Shuai Di
Cross-Domain Traffic Scene Understanding: A Dense Correspondence-Based Transfer Learning Approach. IEEE Trans. Intell. Transp. Syst. 19(3): 745-757 (2018) - [j39]Heng Fan
Multi-Level Contextual RNNs With Attention Model for Scene Labeling. IEEE Trans. Intell. Transp. Syst. 19(11): 3475-3485 (2018) - [c56]He Jiang
Neural Network Based Distributed Consensus Control for Heterogeneous Multi-agent Systems. ACC 2018: 5175-5180 - [c55]Ivan Yu. Tyukin
Efficiency of Shallow Cascades for Improving Deep Learning AI Systems. IJCNN 2018: 1-8 - [i6]Ivan Yu. Tyukin, Alexander N. Gorban, Stephen Green, Danil V. Prokhorov:
Fast Construction of Correcting Ensembles for Legacy Artificial Intelligence Systems: Algorithms and a Case Study. CoRR abs/1810.05593 (2018) - 2017
- [j38]Yong-Duan Song, Frank L. Lewis, Marios M. Polycarpou, Danil V. Prokhorov
Guest Editorial Special Issue on New Developments in Neural Network Structures for Signal Processing, Autonomous Decision, and Adaptive Control. IEEE Trans. Neural Networks Learn. Syst. 28(3): 494-499 (2017) - [j37]Jun Li
Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene. IEEE Trans. Neural Networks Learn. Syst. 28(3): 690-703 (2017) - [c54]Heng Fan, Xue Mei, Danil V. Prokhorov, Haibin Ling:
RGB-D Scene Labeling with Multimodal Recurrent Neural Networks. CVPR Workshops 2017: 203-211 - [c53]Han-Kai Hsu, Yi-Hsuan Tsai, Xue Mei, Kuan-Hui Lee, Naoki Nagasaka, Danil V. Prokhorov
Learning to tell brake and turn signals in videos using CNN-LSTM structure. ITSC 2017: 1-6 - [i5]Tomoki Nishi, Prashant Doshi, Michael R. James, Danil V. Prokhorov:
Actor-Critic for Linearly-Solvable Continuous MDP with Partially Known Dynamics. CoRR abs/1706.01077 (2017) - [i4]Tomoki Nishi, Prashant Doshi, Danil V. Prokhorov:
Freeway Merging in Congested Traffic based on Multipolicy Decision Making with Passive Actor Critic. CoRR abs/1707.04489 (2017) - 2016
- [j36]Alexander N. Gorban
Approximation with random bases: Pro et Contra. Inf. Sci. 364-365: 129-145 (2016) - [j35]Danil V. Prokhorov, Sadayuki Tsugawa, Christoph Stiller, Christian Laugier, Emilio Frazzoli, Mohan M. Trivedi, Dimitar P. Filev:
IEEE Transactions on Intelligent Vehicles Senior Associate Editors. IEEE Trans. Intell. Veh. 1(1): 3-5 (2016) - [j34]Bunyo Okumura, Michael R. James, Yusuke Kanzawa, Matthew Derry, Katsuhiro Sakai, Tomoki Nishi
Challenges in Perception and Decision Making for Intelligent Automotive Vehicles: A Case Study. IEEE Trans. Intell. Veh. 1(1): 20-32 (2016) - [c52]Guangyu Zhong, Yi-Hsuan Tsai, Yi-Ting Chen, Xue Mei, Danil V. Prokhorov
Learning to tell brake lights with convolutional features. ITSC 2016: 1558-1563 - [c51]Shuai Di, Honggang Zhang, Xue Mei, Danil V. Prokhorov
A benchmark for cross-weather traffic scene understanding. ITSC 2016: 2150-2156 - [c50]Heng Fan
Cross datasets vegetation detection with spatial prior and local context. Intelligent Vehicles Symposium 2016: 735-740 - [c49]Danil V. Prokhorov:
Toward Highly Intelligent Automobiles. VEHITS 2016: 7 - [i3]Heng Fan, Xue Mei, Danil V. Prokhorov, Haibin Ling:
Multi-level Contextual RNNs with Attention Model for Scene Labeling. CoRR abs/1607.02537 (2016) - 2015
- [j33]Zhen Ni, Haibo He, Dongbin Zhao, Xin Xu
GrDHP: A General Utility Function Representation for Dual Heuristic Dynamic Programming. IEEE Trans. Neural Networks Learn. Syst. 26(3): 614-627 (2015) - [j32]Zhen Ni, Haibo He, Xiangnan Zhong, Danil V. Prokhorov
Model-Free Dual Heuristic Dynamic Programming. IEEE Trans. Neural Networks Learn. Syst. 26(8): 1834-1839 (2015) - [j31]Xue Mei, Zhibin Hong, Danil V. Prokhorov
Robust Multitask Multiview Tracking in Videos. IEEE Trans. Neural Networks Learn. Syst. 26(11): 2874-2890 (2015) - [c48]Zhibin Hong, Zhe Chen
MUlti-Store Tracker (MUSTer): A cognitive psychology inspired approach to object tracking. CVPR 2015: 749-758 - [c47]Shuai Di, Honggang Zhang, Xue Mei, Danil V. Prokhorov
Spatial Prior for Nonparametric Road Scene Parsing. ITSC 2015: 1209-1214 - [c46]Xue Mei, Naoki Nagasaka, Bunyo Okumura, Danil V. Prokhorov
Detection and motion planning for roadside parked vehicles at long distance. Intelligent Vehicles Symposium 2015: 412-418 - [i2]Alexander N. Gorban, Ivan Yu. Tyukin, Danil V. Prokhorov, Konstantin I. Sofeikov:
Approximation with Random Bases: Pro et Contra. CoRR abs/1506.04631 (2015) - 2014
- [j30]Michael Fairbank, Danil V. Prokhorov
Clipping in Neurocontrol by Adaptive Dynamic Programming. IEEE Trans. Neural Networks Learn. Syst. 25(10): 1909-1920 (2014) - [c45]Pengpeng Liang, Yi Wu, Xue Mei, Jingyi Yu, Erik Blasch, Danil V. Prokhorov
Blur-Resilient Tracking Using Group Sparsity. ACCV (5) 2014: 131-145 - [c44]Zhibin Hong, Chaohui Wang, Xue Mei, Danil V. Prokhorov
Tracking Using Multilevel Quantizations. ECCV (6) 2014: 155-171 - [c43]Danil V. Prokhorov:
Computational Intelligence in Automotive R & D. IJCCI (ECTA) 2014: IS-7 - [c42]Konstantin I. Sofeikov, Ivan Tyukin
Learning optimization for decision tree classification of non-categorical data with information gain impurity criterion. IJCNN 2014: 3548-3555 - 2013
- [j29]Michael Fairbank, Eduardo Alonso
An Equivalence Between Adaptive Dynamic Programming With a Critic and Backpropagation Through Time. IEEE Trans. Neural Networks Learn. Syst. 24(12): 2088-2100 (2013) - [c41]Zhibin Hong, Xue Mei, Danil V. Prokhorov
Tracking via Robust Multi-task Multi-view Joint Sparse Representation. ICCV 2013: 649-656 - [c40]Xiangnan Zhong, Haibo He, Danil V. Prokhorov
Robust controller design of continuous-time nonlinear system using neural network. IJCNN 2013: 1-8 - 2012
- [j28]Michael Fairbank, Eduardo Alonso
Simple and Fast Calculation of the Second-Order Gradients for Globalized Dual Heuristic Dynamic Programming in Neural Networks. IEEE Trans. Neural Networks Learn. Syst. 23(10): 1671-1676 (2012) - [c39]Zhen Ni, Haibo He, Dongbin Zhao, Danil V. Prokhorov
Reinforcement learning control based on multi-goal representation using hierarchical heuristic dynamic programming. IJCNN 2012: 1-8 - [c38]Jing Wang, Haibo He, Danil V. Prokhorov
A Folded Neural Network Autoencoder for Dimensionality Reduction. INNS-WC 2012: 120-127 - 2011
- [j27]Marco Baglietto
Editorial: One Year as EiC, and Editorial-Board Changes at TNN. IEEE Trans. Neural Networks 22(1): 1-7 (2011) - [c37]Danil V. Prokhorov
Overview of CI research in automotive ITS. CIVTS 2011: 1-7 - [c36]Zhen Ni, Haibo He, Danil V. Prokhorov
An online actor-critic learning approach with Levenberg-Marquardt algorithm. IJCNN 2011: 2333-2340 - 2010
- [j26]Danil V. Prokhorov
A convolutional learning system for object classification in 3-D lidar data. IEEE Trans. Neural Networks 21(5): 858-863 (2010) - [c35]Danil V. Prokhorov
Multi-agent framework for remote diagnostics. IEEE Congress on Evolutionary Computation 2010: 1-8 - [c34]Danil V. Prokhorov
Road obstacle classification with attention windows. Intelligent Vehicles Symposium 2010: 889-895 - [p2]Vladimir G. Red'ko, Danil V. Prokhorov
Learning and Evolution of Autonomous Adaptive Agents. Advances in Machine Learning I 2010: 491-500
2000 – 2009
- 2009
- [j25]Kihoon Choi, Satnam Singh, Anuradha Kodali, Krishna R. Pattipati, John W. Sheppard
Novel Classifier Fusion Approaches for Fault Diagnosis in Automotive Systems. IEEE Trans. Instrum. Meas. 58(3): 602-611 (2009) - [j24]Satnam Singh, Anuradha Kodali, Kihoon Choi, Krishna R. Pattipati, Setu Madhavi Namburu, S. C. Sean, Danil V. Prokhorov
Dynamic Multiple Fault Diagnosis: Mathematical Formulations and Solution Techniques. IEEE Trans. Syst. Man Cybern. Part A 39(1): 160-176 (2009) - [c33]Ivan Tyukin
Feasibility of random basis function approximators for modeling and control. CCA/ISIC 2009: 1391-1396 - [c32]Danil V. Prokhorov
Risk estimator for control in intelligent transportation system. CCA/ISIC 2009: 1403-1408 - [c31]Danil V. Prokhorov
A self-learning sensor fusion system for object classification. CIVVS 2009: 1-7 - [c30]Danil V. Prokhorov:
Object recognition in 3D lidar data with recurrent neural network. CVPR Workshops 2009: 9-15 - [c29]Danil V. Prokhorov
A Self-learning System for Object Categorization. ICEIS 2009: 265-274 - [c28]Danil V. Prokhorov, Yasuo Uehara:
Performance measurement and its role in advancement for intelligent systems: discussion points. PerMIS 2009: 265-267 - [i1]Ivan Tyukin, Danil V. Prokhorov:
Feasibility of random basis function approximators for modeling and control. CoRR abs/0905.0677 (2009) - 2008
- [j23]Ivan Tyukin
Adaptive Classification of Temporal Signals in Fixed-Weight Recurrent Neural Networks: An Existence Proof. Neural Comput. 20(10): 2564-2596 (2008) - [j22]Danil V. Prokhorov
Toyota Prius HEV neurocontrol and diagnostics. Neural Networks 21(2-3): 458-465 (2008) - [c27]Danil V. Prokhorov, Johann Schumann:
Intelligent Systems for Modeling and Control: Advances in Design and Validation. ISIC 2008: 18 - [c26]Setu Madhavi Namburu, Danil V. Prokhorov, Shunsuke Chigusa, Liu Qiao, Krishna R. Pattipati:
KDD and Its Applications in Automotive Sector - A Brief Survey. DMIN 2008: 335-341 - [c25]Zhengping Ji, Danil V. Prokhorov:
Radar-vision fusion for object classification. FUSION 2008: 1-7 - [c24]Dhafar S. Mohammed, Saeid R. Habibi, Danil V. Prokhorov
Adaptive parameter robust estimation. IJCNN 2008: 2948-2955 - [p1]Danil V. Prokhorov
Neural Networks in Automotive Applications. Computational Intelligence in Automotive Applications 2008: 101-123 - [e1]Danil V. Prokhorov:
Computational Intelligence in Automotive Applications. Studies in Computational Intelligence 132, Springer 2008, ISBN 978-3-540-79256-7 [contents] - 2007
- [j21]Xiao Hu, Danil V. Prokhorov
Time series prediction with a weighted bidirectional multi-stream extended Kalman filter. Neurocomputing 70(13-15): 2392-2399 (2007) - [j20]Ivan Tyukin
Adaptation and Parameter Estimation in Systems With Unstable Target Dynamics and Nonlinear Parametrization. IEEE Trans. Autom. Control. 52(9): 1543-1559 (2007) - [j19]Danil V. Prokhorov:
Intelligent Control Systems Using Computational Intelligence [book review]. IEEE Trans. Neural Networks 18(2): 611-612 (2007) - [j18]Xindi Cai, Danil V. Prokhorov
Training Winner-Take-All Simultaneous Recurrent Neural Networks. IEEE Trans. Neural Networks 18(3): 674-684 (2007) - [j17]Frank L. Lewis, Jie Huang, Thomas Parisini, Danil V. Prokhorov
Guest Editorial Special Issue on Neural Networks for Feedback Control Systems. IEEE Trans. Neural Networks 18(4): 969-972 (2007) - [j16]Danil V. Prokhorov
Training Recurrent Neurocontrollers for Real-Time Applications. IEEE Trans. Neural Networks 18(4): 1003-1015 (2007) - [c23]Danil V. Prokhorov:
Prius control with a hybrid method. ICINCO-RA (1) 2007: 372-376 - [c22]Danil V. Prokhorov
Toyota Prius HEV neurocontrol. IJCNN 2007: 2129-2134 - [c21]Steven F. Kalik, Danil V. Prokhorov
Automotive Turing Test. PerMIS 2007: 152-158 - [c20]Satnam Singh, Kihoon Choi, Anuradha Kodali, Krishna R. Pattipati, Setu Madhavi Namburu, Shunsuke Chigusa, Danil V. Prokhorov
Dynamic fusion of classifiers for fault diagnosis. SMC 2007: 2467-2472 - 2006
- [j15]Danil V. Prokhorov
Training Recurrent Neurocontrollers for Robustness With Derivative-Free Kalman Filter. IEEE Trans. Neural Networks 17(6): 1606-1616 (2006) - [c19]Danil V. Prokhorov
Feedback Neurocontrol of a Disease. IJCNN 2006: 2345-2348 - [c18]Vladimir G. Red'ko, Konstantin V. Anokhin, Mikhail S. Burtsev
Project "Animat Brain": Designing the Animat Control System on the Basis of the Functional Systems Theory. SAB ABiALS 2006: 94-107 - 2005
- [j14]Danil V. Prokhorov
Welcome to the special issue. Neural Networks 18(5-6): 457- (2005) - [j13]Vladimir G. Red'ko, Oleg P. Mosalov
A model of evolution and learning. Neural Networks 18(5-6): 738-745 (2005) - [c17]Danil V. Prokhorov:
Training neurocontrollers for robustness via nprKF. ACC 2005: 1337-1342 - [c16]Ivan Tyukin, Danil V. Prokhorov, Cees van Leeuwen:
A new method for adaptive brake control. ACC 2005: 2194-2199 - [c15]Vladimir G. Red'ko, Oleg P. Mosalov
Investigation of Evolving Populations of Adaptive Agents. ICANN (1) 2005: 337-342 - 2004
- [c14]Nikita E. Barabanov, Danil V. Prokhorov
Stability analysis of discrete-time recurrent multilayer neural networks. CDC 2004: 4958-4963 - 2003
- [j12]Ivan Tyukin
Parameter Estimation of Sigmoid Superpositions: Dynamical System Approach. Neural Comput. 15(10): 2419-2455 (2003) - [j11]Lee A. Feldkamp, Danil V. Prokhorov
Simple and conditioned adaptive behavior from Kalman filter trained recurrent networks. Neural Networks 16(5-6): 683-689 (2003) - [j10]Ivan Tyukin
Adaptive control with nonconvex parameterization. IEEE Trans. Autom. Control. 48(4): 554-567 (2003) - [j9]Nikita Barabanov, Danil V. Prokhorov
A new method for stability analysis of nonlinear discrete-time systems. IEEE Trans. Autom. Control. 48(12): 2250-2255 (2003) - [c13]Danil V. Prokhorov:
Optimal neurocontrollers for discretized distributed parameter systems. ACC 2003: 549-554 - [c12]Ivan Tyukin, Danil V. Prokhorov
Finite form realizations of adaptive control algorithms. ECC 2003: 264-269 - 2002
- [j8]Nikita Barabanov, Danil V. Prokhorov
Stability analysis of discrete-time recurrent neural networks. IEEE Trans. Neural Networks 13(2): 292-303 (2002) - [c11]Nikita E. Barabanov, Danil V. Prokhorov:
Two alternative stability criteria for discrete-time RMLP. CDC 2002: 1776-1779 - [c10]Ivan Tyukin, Cees van Leeuwen, Danil V. Prokhorov, Valery A. Terekhov:
On a problem of time-varying learning rate influence on the adaptive system dynamics. CDC 2002: 4718-4721 - 2001
- [c9]Nikita E. Barabanov, Danil V. Prokhorov
Global stability analysis of discrete-time recurrent neural networks. ACC 2001: 4550-4555 - 2000
- [j7]Arthur Petrosian, Danil V. Prokhorov
Recurrent neural network based prediction of epileptic seizures in intra- and extracranial EEG. Neurocomputing 30(1-4): 201-218 (2000) - [j6]Paul H. Eaton, Danil V. Prokhorov
Neurocontroller alternatives for "fuzzy" ball-and-beam systems with nonuniform nonlinear friction. IEEE Trans. Neural Networks Learn. Syst. 11(2): 423-435 (2000) - [c8]Valeri A. Terekhov, Ivan Yu. Tyukin
Adaptive control on manifolds with RBF neural networks. CDC 2000: 3831-3836
1990 – 1999
- 1999
- [c7]Danil V. Prokhorov, Lee A. Feldkamp:
Application of SVM to Lyapunov function approximation. IJCNN 1999: 383-387 - 1998
- [j5]Emad W. Saad, Danil V. Prokhorov
Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks. IEEE Trans. Neural Networks 9(6): 1456-1470 (1998) - [c6]Danil V. Prokhorov, Lee A. Feldkamp:
Analyzing for Lyapunov stability with adaptive critics. SMC 1998: 1658-1661 - 1997
- [j4]Danil V. Prokhorov
Adaptive critic designs. IEEE Trans. Neural Networks 8(5): 997-1007 (1997) - [j3]Wang Song, Shaowei Xia, Jianchang Mao, Anil K. Jain, Danil V. Prokhorov, Donald C. Wunsch II:
Comments on "A self-organizing network for hyperellipsoidal clustering (HEC)" [and reply]. IEEE Trans. Neural Networks 8(6): 1561-1563 (1997) - [j2]Danil V. Prokhorov
Corrections To "Adaptive Critic Designs". IEEE Trans. Neural Networks 8(6): 1563 (1997) - [c5]Raonak Zaman, Danil V. Prokhorov
Adaptive critic design in learning to play game of Go. ICNN 1997: 1-4 - [c4]Danil V. Prokhorov
Primitive adaptive critics. ICNN 1997: 2263-2267 - [c3]Lee A. Feldkamp, Gintaras V. Puskorius, Danil V. Prokhorov
Unified formulation for training recurrent networks with derivative adaptive critics. ICNN 1997: 2268-2272 - 1996
- [c2]Emad W. Saad, Danil V. Prokhorov, Donald C. Wunsch II:
Advanced neural network training methods for low false alarm stock trend prediction. ICNN 1996: 2021-2026 - 1995
- [j1]Danil V. Prokhorov
Adaptive critic designs: A case study for neurocontrol. Neural Networks 8(9): 1367-1372 (1995) - [c1]Hong Tan, Danil V. Prokhorov, Donald C. Wunsch:
Conservative thirty calendar day stock prediction using a probabilistic neural network. CIFEr 1995: 113-117
Coauthor Index
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Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-23 20:32 CEST by the dblp team
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