Modelling framework for artificial hybrid dynamical systems

[1]  Michael G. Epitropakis,et al.  Hardware-friendly Higher-Order Neural Network Training using Distributed Evolutionary Algorithms , 2010, Appl. Soft Comput..

[2]  Stephan Hoyer,et al.  Learning data-driven discretizations for partial differential equations , 2018, Proceedings of the National Academy of Sciences.

[3]  Zheng Xu,et al.  Training Neural Networks Without Gradients: A Scalable ADMM Approach , 2016, ICML.

[4]  Manfred Morari,et al.  A clustering technique for the identification of piecewise affine systems , 2001, Autom..

[5]  Wenjie Lu,et al.  A Hybrid-Adaptive Dynamic Programming Approach for the Model-Free Control of Nonlinear Switched Systems , 2016, IEEE Transactions on Automatic Control.

[6]  Kim Hua Tan,et al.  Leveraging the supply chain flexibility of third party logistics - Hybrid knowledge-based system approach , 2008, Expert Syst. Appl..

[7]  Stephen A. Billings,et al.  Properties of neural networks with applications to modelling non-linear dynamical systems , 1992 .

[8]  Deepak Shukla,et al.  Computationally Efficient Control of Nonlinear Systems Using Orthonormal Activation Function Based Neural Networks , 1996 .

[9]  Yongji Wang,et al.  Legendre Cooperative PSO Strategies for Trajectory Optimization , 2018, Complex..

[10]  Lyle H. Ungar,et al.  A hybrid neural network‐first principles approach to process modeling , 1992 .

[11]  Niels Kjølstad Poulsen,et al.  NNSYSID-Toolbox for System Identification with Neural Networks , 2002 .

[12]  Alberto Bemporad,et al.  HYSDEL-a tool for generating computational hybrid models for analysis and synthesis problems , 2004, IEEE Transactions on Control Systems Technology.

[13]  Anirban Roy,et al.  Development of a knowledge based hybrid neural network (KBHNN) for studying the effect of diafiltration during ultrafiltration of whey , 2011 .

[14]  Min-Yuan Cheng,et al.  Evolutionary fuzzy hybrid neural network for dynamic project success assessment in construction industry , 2012 .

[15]  Felix Breitenecker,et al.  State Events and Structural-dynamic Systems: Definition of ARGESIM Benchmark C21 , 2016, Simul. Notes Eur..

[16]  Ming Lu,et al.  Hybrid partial least squares and neural network approach for short-term electrical load forecasting , 2008 .

[17]  M. Kvasnica,et al.  Two steps piecewise affine identification of nonlinear systems , 2012 .

[18]  Felix Breitenecker,et al.  Possibilities in State Event Modelling of Hybrid Systems , 2018, Simul. Notes Eur..

[19]  Alberto Bemporad,et al.  Optimal control of continuous-time switched affine systems , 2006, IEEE Transactions on Automatic Control.

[20]  Dimitrios I. Fotiadis,et al.  Artificial neural networks for solving ordinary and partial differential equations , 1997, IEEE Trans. Neural Networks.

[21]  S. Kozák,et al.  Improved Piecewise Linear Approximation of Nonlinear Functions in Hybrid Control , 2011 .

[22]  Niel Canty,et al.  An output error algorithm for piecewise affine system identification , 2012 .

[23]  Lu Liu,et al.  Extended-State-Observer-Based Collision-Free Guidance Law for Target Tracking of Autonomous Surface Vehicles with Unknown Target Dynamics , 2018, Complex..

[24]  W. Fred Ramirez,et al.  Dynamic hybrid neural network model of an industrial fed-batch fermentation process to produce foreign protein , 2007, Comput. Chem. Eng..

[25]  Lennart Ljung,et al.  Nonlinear black-box modeling in system identification: a unified overview , 1995, Autom..

[26]  Colin Giles,et al.  Learning, invariance, and generalization in high-order neural networks. , 1987, Applied optics.

[27]  T. Henzinger.  The theory of hybrid automata , 1996, LICS 1996.