In this paper, we propose a method via hybrid spectral conjugate gradient to solve nonlinear monotone equations and signal recovery problems. This has been done using the hybrid conjugate gradient (CG) parameters of D...
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We discuss how to build nonlinear input-output models of low-dimensional deterministic systems for both static and dynamic (feedback) systems with \fading memory." Radial basis functions are used to build the mod...
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We discuss how to build nonlinear input-output models of low-dimensional deterministic systems for both static and dynamic (feedback) systems with \fading memory." Radial basis functions are used to build the models. The utility of these models is illustrated by building accurate and stable models for electronic circuits with dynamic (memory) effects.
A modeling method that allows one to rapidly build data driven models for nonlinear components is discussed. The models are constructed from input/output time domain data and their "embeddings". The notion o...
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A modeling method that allows one to rapidly build data driven models for nonlinear components is discussed. The models are constructed from input/output time domain data and their "embeddings". The notion o...
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A modeling method that allows one to rapidly build data driven models for nonlinear components is discussed. The models are constructed from input/output time domain data and their "embeddings". The notion of models built from embeded data is described in the Taken's Embedding Theorem and has been extensively explored for modeling autonomous systems in the physics liteature. The authors extend these results to nonautonomous systems by creating tools that allow engineers to rapidly build models for driven nonlinear components. These models can be used in simulation, process control, diagonostics, and sensor calibration. We present the results of applying nonlinear modelling techniques to the modelling of measurements taken from a Nonlinear Network Measurement Systems (NNMS). A comparison is made between reconstructed polynomial models and radial basis models.
This volume contains the proceedings of the US-Australia workshop on Control and Chaos held in Honolulu, Hawaii from 29 June to 1 July, 1995. The workshop was jointly sponsored by the National Science Foundation (USA)...
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ISBN:
(数字)9781461224464
ISBN:
(纸本)9780817638672;9781461275404
This volume contains the proceedings of the US-Australia workshop on Control and Chaos held in Honolulu, Hawaii from 29 June to 1 July, 1995. The workshop was jointly sponsored by the National Science Foundation (USA) and the Department of Industry, Science and Technology (Australia) under the US-Australia agreement. Control and Chaos-it brings back memories of the endless reruns of "Get Smart" where the good guys worked for Control and the bad guys were associated with Chaos. In keeping with current events, Control and Chaos are no longer adversaries but are now working together. In fact, bringing together workers in the two areas was the focus of the workshop. The objective of the workshop was to bring together experts in dynamical systems theory and control theory, and applications workers in both fields, to focus on the problem of controlling nonlinear and potentially chaotic systems using limited control effort. This involves finding and using orbits in nonlinear systems which can take a system from one region of state space to other regions where we wish to stabilize the system. Control is used to generate useful chaotic trajectories where they do not exist, and to identify and take advantage of useful ones where they do exist. A controller must be able to nudge a system into a proper chaotic orbit and know when to come off that orbit. Also, it must be able to identify regions of state space where feedback control will be effective.
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