CHICKEN BANDA PERFORMANCE IMPROVEMENT UTILIZING NEURO-FUZZY LOGIC TECHNIQUE

This study is on improvement of performance of the chicken Banda, using indoor change in environmental conditions for temperature control. The differential change in climatic conditions is technically used to put on the fan and the Banda so as to realize the right comfortable indoor conditions. The chicken chicks’ Banda Mathematical model is created, prototype designed, temperature controller to depict a two systems simulation of neuro fuzzy logic and fuzzy logic .The performance is analyzed by the use of Matlab Simulink latest edition. To monitor the temperature of the Chicken cage the neural fuzzy logic technique is utilized. As far as the prototype is concerned the chicks’ cage set temperature is fixed at 26.50C. The study will show that the reference input can be kept on track by the process controller hence proving the principle that the neural fuzzy control is much superior in optimizing performance compared to the fuzzy only controllers. The Back propagation (BP) and least square estimator (LSE) are the hybrid optimization methods which are used. For data training the gradient descent method (GDM) is used. The research reveal that  there is drastic performance improvement in the behavior response where  result show that there settling time is reduced from 0.75 to 0.48 seconds while the percentage  overshoot is also reduced down  from 29.9% to 0.9345%.