A personal computer cooling system based on adaptive neuro-fuzzy inference systems

To improve the cooling efficiency of personal computers, many cooling methods are developed, such as the manual mode, speed cruise mode, thermal cruise mode and multi-point fan control mode, and so on. But it is difficult to achieve a mathematical model of the fan motor, so that it is unable to timely control the fan speed, resulting in poor thermal performance. In this paper, we propose an adaptive neuro-fuzzy inference system (ANFIS) approach to a PC cooling system for reducing the time of searching the optimum speed of the cooling fan. ANFIS combines both fuzzy inference system and neural network, so it can be applied to uncertainty system fully. Even more, it has self-study and ability of the organization at the same time. The experiment results show that in fan speed control comparative analysis, the fan speed of the proposed ANFIS can be reduced an average of 7.67% that compares with other cooling methods.