Neural Network based controller

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The project focuses on using a neural network for precise temperature control in a complex, non-linear, and dynamic room environment. The neural network, trained with high accuracy (99.5%), takes five inputs: three spatial coordinates (x, y, z), fan speed (A), and dissipated heat (B), to predict the temperature (T). The user seeks recommendations for integrating this neural network into a control architecture, as traditional system inversion methods have proven ineffective. The implementation is being conducted in MATLAB. Suggestions for suitable control strategies are needed to effectively utilize the trained neural network.
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I'm working on a project which deals with temperature control of a room. the idea is to control temperature within a limit.

I have prior data which i used to train a neural network which is 99.5% accurate. there are 5 inputs (x , y , z , A , B) and there is one output (T).

Now I want to use this network in a control architecture of some kind. Any suggestions? which should I use?

P.S. The system is highly complex, non linear and dynamic in nature. I have already tried standard inversion of system but it doesn't work as outputs are lesser than inputs.
I'm doing this in Matlab.
 
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What are your 5 inputs?
 
3 coordinates of a point.. the fan speed at that point.. and the dissipated heat at that point
XYZ,S,H
 

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