Intelligent control of cooling-heating systems by using emotional learning

Abstract

In this paper emotional learning based on neuro fuzzy system by Takagi sugeno is used. High efficiency and proper flexibility of emotional learning are advantages of this learning pattern compare with other learning patterns. Use of this learning algorithm not only decreases the cost of control and makes control of cooling-heating system easier, but it can also readily be applied to cases where the control systems are very large and have a highly complex structure. This paper is about the control of cooling - heating and greenhouse system; which they could be used in agriculture and industry. To use emotional learning causes the quality of products improves. Cooling - heating and greenhouse system because of complicated dynamic structure, high time sensitivity and their natural uncertainty mostly con not be controlled and optimized by classic method. Some disadvantages about classic system are conditional stability, the need to performing sophisticated mathematical operations and complication in implementation. Implementation of cooling heating systems by using emotional learning (based on fuzzy inference system) can be considered as a suitable solution. Ill. 6, bibl. 12 (in English; abstracts in English and Lithuanian).