Results 51 to 60 of about 2,023 (88)

Energy prediction using spatiotemporal pattern networks

, 2017
This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems.
Akintayo, Adedotun, Henze, Gregor P., Jiang, Zhanhong, Liu, Chao, Sarkar, Soumik   +4 more
core  

Selective Evolutionary Generation Systems: Theory and Applications. [PDF]

This dissertation is devoted to the problem of behavior design, which is a generalization of the standard global optimization problem: instead of generating the optimizer, the generalization produces, on the space of candidate optimizers, a probability ...
Menezes, Amor A.
core  

On-line identification of language measure parameters for discrete-event supervisory control

, 2005
Xi Wang, A. Ray, A. Khatkhate
semanticscholar   +1 more source

Pulse, Parse, and Ponder: Using Invisible XML to Dissect a Scientific Domain Specific Language. [PDF]

Balisage Ser Markup Technol
Courtney JM, Gryk MR.
europepmc   +1 more source

Transforming BPEL into annotated deterministic finite state automata for service discovery

Proceedings. IEEE International Conference on Web Services, 2004., 2004
A. Wombacher, Péter Fankhauser, E. Neuhold   +2 more
semanticscholar   +1 more source

Surat Tugas, Bukti Presensi, Realisasi Dosen, Nilai Matkul TBO [PDF]

Yasir, Muhammad
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Optimal supervisory control of finite state automata

International Journal of Control, 2004
A. Ray *, Jinbo Fu, C. Lagoa
semanticscholar   +3 more sources

Unconstrained optimal control of regular languages

Automatica, 2004
Jinbo Fu, A. Ray, C. Lagoa
semanticscholar   +3 more sources

Deterministic chaotic finite-state automata

Nonlinear Dynamics, 2019
Moatsum Alawida, Azman Samsudin, J. Teh, Wafa’ Hamdan Alshoura   +3 more
semanticscholar   +3 more sources

Neural Networks as Universal Finite-State Machines: A Constructive Deterministic Finite Automaton Theory

arXiv.org
We present a complete theoretical and empirical framework establishing feedforward neural networks as universal finite-state machines (N-FSMs). Our results prove that finite-depth ReLU and threshold networks can exactly simulate deterministic finite ...
Sahil Rajesh Dhayalkar
semanticscholar   +1 more source