NDLI: Discussion of neural network controllers from the point of view of inverse dynamics and folding behavior

Content Provider	IEEE Xplore Digital Library
Author	Yamada, T.
Copyright Year	2011
Description	Author affiliation: Department of Computer and Information Sciences, Faculty of Engineering, Ibaraki University 4-12-1 Nakanarusawa, Hitachi, Ibaraki, 316-8511, Japan (Yamada, T.)
Abstract	Most of neural network controllers are designed so as to learn the inverse dynamics of the control object plant. However, the plant of the practical application usually has nonlinear characteristics and it is expressed as many to one function (whose one output corresponds to many input) mathematically. It is well known that there is no inverse function of the many to one function mathematically. What dose the neural network of above controllers learn? This paper discusses several neural network controllers from this point of view and their problem for realization of the neural network learning. On the other hand, it was confirmed that the neural network has the folding behavior in the neural network direct controller. This behavior is that the neural network learns only one part of the inverse of the object plant in order to obtain whole plant output. This means that we can realize the learning of the neural network direct controller without the direct inverse modeling. The complex structure of other neural network controllers is not necessary with regard to realize the neural network learning. This paper describes the details of above discussion and shows the simulation results of the neural network direct controller without the direct inverse modeling.
Starting Page	2210
Ending Page	2215
File Size	435998
Page Count	6
File Format	PDF
ISBN	9781457707148
e-ISBN	9784907764395
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-13
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	SICE (The Society of Instrument and Cont
Subject Keyword	Biological neural networks Inverse problems Simulation Feedback loop Jacobian matrices Education Control systems Folding behavior Neural network controller Inverse dynamics
Content Type	Text
Resource Type	Article

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