Intelligent Control of Heating and Cooling Systems Using Emotional Learning

In recent years، due to the high flexibility of Fuzzy inference and suitable learning ability of Neural Networks، Neuro Fuzzy model has become a suitable model facing uncertainty and complexity in decision making and control. Meanwhile، emotional learning can be used as a suitable learning algorithm for cases that system is not fully rational. In this paper، emotional learning method based on Neuro Fuzzy model of Takagi Sugeno has been implemented which has a higher level of efficiency and flexibility comparing with other methods. The application being considered in this work was the control of heating، cooling and greenhouse system which is used in industry and agriculture and has an important effect on quality improvement of products. Such systems cannot be modeled properly using classic optimization and control methods because of complexity in dynamic structures and high level of change ratio with time and also uncertainty in their nature.