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Journal of Applied Sciences
  Year: 2011 | Volume: 11 | Issue: 15 | Page No.: 2754-2763
DOI: 10.3923/jas.2011.2754.2763
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Optimal Design of Neural Fuzzy Inference Network for Temperature Controller

Nisha Jha, Udaibir Singh, T.K. Saxena and Avinashi Kapoor

In this study, a Neural Fuzzy Inference Network (NFIN) for controlling the temperature of the system has been proposed. The NFIN is inherently a modified fuzzy rule based model possessing neural network’s learning ability using hybrid learning algorithm which combines gradient descent and least mean square algorithm. In contrast to the general adaptive neural fuzzy networks where the rules should be decided in advance before parameter learning is performed, there are no rules initially in the NFIN. The rules in the NFIN are created and adapted as on-line learning proceeds via simultaneous structure and parameter identification. The NFIN has been applied to a practical water bath temperature control system, designed and developed around Atmel’s 89C51 microcontroller. In the above system, four experiments were conducted on water bath each for 250 and 500 mL min-1 flow of water for different volume of water and power of heater. The performance of NFIN has been compared with Fuzzy Logic Controller (FLC) and conventional Proportional Integral Derivative (PID) controller. The three control schemes are compared through experimental studies with respect to set point regulation. It is found that the proposed NFIN control scheme has the best control performance of the three control schemes.
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How to cite this article:

Nisha Jha, Udaibir Singh, T.K. Saxena and Avinashi Kapoor, 2011. Optimal Design of Neural Fuzzy Inference Network for Temperature Controller. Journal of Applied Sciences, 11: 2754-2763.

DOI: 10.3923/jas.2011.2754.2763






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