LSTM_thermistor | LSTM predictor for measuring temperature

LSTM_thermistor is a C++ library typically used in Internet of Things (IoT) applications. LSTM_thermistor has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

LSTM predictor for measuring temperature by gpt3. In this code, the loop() function waits for the software trigger input (TRIGGER_PIN) to go low, indicating a new resistance measurement is requested. Once triggered, the code charges the capacitor with a series of pulses and counts the number of pulses required to exceed the threshold voltage (THRESHOLD). The pulse count is averaged over AVERAGE_READINGS readings to improve accuracy. The code then calculates the resistance of the unknown resistor using the capacitance of the known capacitor (CAPACITANCE), the pulse count, and the supply voltage (supplyVoltage). It then trains the LSTM with inputs consisting of the analog input (analogInput), the average analog input over the number of readings (analogInput / AVERAGE_READINGS), the pulse count, the supply voltage, and a software trigger input set to zero to avoid interference from previous measurements. The LSTM is then used to predict the error in the resistance calculation (predictedError) and the measured error is computed as the difference between the actual resistance and the predicted error (measuredError). The measureAnalogInput() function measures the analog input on A0 and returns the result, and the measureSupplyVoltage() function measures the supply voltage using a voltage divider and the internal reference voltage of the Arduino. The chargeCapacitor() function charges the capacitor with discrete pulses until the threshold voltage is exceeded, and returns the number of pulses required. Finally, the calculateResistance() function calculates the resistance of the unknown resistor from the capacitance, pulse count, and supply voltage. Overall, this code uses a combination of pulse counting, averaging, and LSTM prediction to improve the precision and accuracy of the resistance measurement. To train LSTM use the dstaset gathering sketch to collect the data. then use python script for training using tensor flow. This script loads the dataset generated by the Arduino sketch, splits the data into features and targets, scales the data between 0 and 1, defines an LSTM model, trains the model, and saves the trained model and feature and target scalers to disk. To run this script, save it as a Python file (e.g. train_lstm.py) on a Linux machine with TensorFlow installed, and run it from the terminal with the command python train_lstm.py. Make sure that the data.csv file generated by the Arduino sketch is in the same directory as the script.