Neural Network based controller

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SUMMARY

The discussion centers on implementing a neural network-based controller for temperature regulation in a room. The neural network, trained with prior data, achieves 99.5% accuracy with five inputs (x, y, z, A, B) and one output (T). The user seeks suggestions for integrating this neural network into a control architecture, noting the system's complexity, non-linearity, and dynamic characteristics. Previous attempts at standard system inversion were unsuccessful due to output limitations.

PREREQUISITES
  • Understanding of neural networks and their training processes
  • Familiarity with control systems and architectures
  • Proficiency in MATLAB for implementing algorithms
  • Knowledge of non-linear dynamic systems
NEXT STEPS
  • Research advanced control strategies such as Model Predictive Control (MPC)
  • Explore reinforcement learning techniques for adaptive control
  • Learn about MATLAB's Control System Toolbox for system modeling
  • Investigate the implementation of PID controllers in non-linear systems
USEFUL FOR

Control engineers, data scientists working on predictive modeling, and anyone involved in developing advanced temperature control systems using neural networks.

date.chinmay
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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.
 
Engineering news on Phys.org
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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