Temperant Measurement is a Critical Aspect in Various Industries, and THERMOCOUPLES Are Widly Used as Sensing Devices Due to their Affordability, RuggEdness S, and Stability. However, Their main drawback lies in the nonlinearity that adversects measures across a wider temporar. Numerous hardware andSoftware Techniques have been proposed to address this back, but they often suffer from pooms, High Costs, and increased memory requirements. In this p APER, We Propose the Application of Artificial Intelligence Dependent System to Linearize the Nonlinearity Characteristics of thermocouples.ASSESS The Performance of Tradingal Techniques with The Proposed Deep Neural Network with Levenberg –marquardt (LM) Algorithm, for Nonlinearity CompensationIndore Investment. We ConduCTED Experiments on K Type of thermocouples and Evaluated the Effectiveness of the Proposed Method.Network is Created with Multiple Layers and TrainedSurat Investment. The Trained DNN Model Predicts Voltage Values Based on TempentsGuoabong Stock. The Lm Algorithm is then applic The DNN-Predict Voltage Values to Achieve a Linearized Representation of the thermocouple Data.Percentage are Calculated to Evaluate The Performance of the Linearization Method. Our Results Demonstrate that the Deep Neurforms The ALL-OTH Er Conventional Approaches in Terms of Accuracy. Moreover, The Compensation USINGNIFICANTLY Reduces Nonlinearity by 61.4% Compared to theUncompensated Characteristics of the thermocouple. This Circuit Does Not Require Any Pre Continging Circuits. UTE of Standards and Technology (Nist) Standard.
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