Extreme learning machine and back propagation neural network comparison for temperature and humidity control of oyster mushroom based on microcontroller

This paper presents design and experimental studies of Extreme Learning Machine (ELM) to control temperature and humidity of oyster mushroom farm house. The ideal temperature to optimize the growth of oyster mushroom in low lying areas is for about 28° Celsius and 80% of humidity, while the current method for controlling temperature and humidity is done by conventional manner using manual sprayer. Given these problems, a Single Layer Feed Forward Neural Network (SLFN's) with modification of H inverse matrix versus target matrix or also known as ELM can control the temperature and humidity of oyster mushroom farm house more faster and effectively than previous research. DHT11 sensor is used to read the temperature and humidity value. Exhaust fan and mist maker are used for conditioning the control variables. Several beginning conditions were built to compare ELM with previous methods such back propagation neural network and zero order FLC in term to find the suitable methodfor this problem.