neural-network-based RLS algorithm in control of nonlinear systems?
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Neural Networks have the capability of learning from a just a few training samples. Solution space for neural network based reasoning is not restricted to linearity. i.e. a neural network automatically forms a complex formula(non-linear) fitting the inputs and the previous well known results. \nSolving non-linear systems using mathematical models is very complex and sometimes impossible. That's why we go for NN, Fuzzy, Genetic algorithms.
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http://www.willamette.edu/~fthompso/MgmtCon/Control_Systems.html\n\n\nhttp://www.isd.mel.nist.gov/documents/falco/ITSecurityProcess.pdf#search='applications%20of%20digital%20control%20systems%20on%20different%20industries'\n\nhttp://www.cs.uidaho.edu/~krings/publications/SACT-2002-D.pdf#search='digital%20control%20systems%20in%20pipelines'\n\nTry them...
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not that I know...you might have to wright your own algorithm!\n\nouch...have fun!\n\nbut check links!!
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Convex Hull: Quick Hull Algorithm\nhttp://nereida.deioc.ull.es/html/casiano/qhull.html\n\nVoronoi: Boundary Extraction\nhttp://cgm.cs.mcgill.ca/~godfried/teaching/projects.pr.98/sergei/project.html
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Its based on Artificial Neural Networks, which are programmatical methods of trying to duplicate the activity of the human brain. It comprises of a learning stage, where the paths to different methods are chalked out. Then, when the input comes in, the program tries to track the path of the successive answers to ones that match the user's input closest (one method of achieving this is finding the least difference between each pair of input and entry in the database). This is done stage-wise, until a good guess is made on the possible solution to the user's responses.
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Since no-one else has answered the question yet, I'll take a stab at it -\n\nI think the term you're looking for is Hysteresis
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http://en.wikipedia.org/wiki/Gradient
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If your matrix is not square, then you can compute a generalized inverse matrix.  There are some links below.  There are many generalized inverses for matrices (other than those that are square and invertible; those only have one generalized inverse, which is its inverse).  But there is only one Moore-Penrose inverse for any given matrix.\n\nedit: reetz, I have already reported you for copying.  I suggest you back off.
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The best answer you are going to get for that can be found here: http://en.wikipedia.org/wiki/Artificial_intelligence
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The best controller in my opinion would be a dc chopper that smoothly reduces input voltage to the motor thus slowing it smoothly.
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You could try TNTSMS: http://www.tntsms.com/funstuff/
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fully computerised mechanics with sensors to detect objects in the driving lane
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Fuzzy Logic is very "stick it" powerful and effective control, but it often requires serious top-down engineering.\n\nWhat teaches itself is "Neural Networks", and they are very fast and effective.\n\nWhat the controls guys do is combine them to make "Neural-Fuzzy" Control.  It is self learning, very fast, and "sticks it" when nearly nothing else will.  Its awesome stuff.
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Here you go! Hope this helps.\n\nhttp://web.uvic.ca/~rburke/burkelab/neuraldevelopment.htm
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In this contribution, we suggest an efficient hierarchical hybrid control scheme for the start-up of a distillation plant consisting of two coupled distillation columns. The overall control task is decomposed into a set of low-level and high-level subtasks. Each of these subtasks can be solved with much less effort than the original specifications. Low-level tasks are solved by means of continuous feedback, whereas the high-level task is solved by a discrete supervisory controller operating on quantised measurement information and switching between continuous low-level controllers. The approach presented is set within a behavioural framework; it guarantees that the chosen interaction of low-level and high-level control does indeed enforce the specifications.
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would this be acceptable: \n\nhttp://www.fi.edu/pieces/knox/automaton/newtoncradle.htm
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Try this website. Scroll down to "The Circuit" ---\n\nhttp://www.play-hookey.com/ac_theory/ac_rc_series.html