@inproceedings{a1c99bdc41a74d0a894a3ac7e461ee35,
title = "Evaluation of the stirling heat engine performance prediction using ANN-PSO and ANFIS models",
abstract = "The work presents the prediction performance results of three algorithms, namely Artificial Neural Network (ANN), Artificial Neural Network trained with Particle Swarm Optimization (PSO) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models. ANFIS and ANN trained by PSO are applied to predict the power and torque values of a Stirling heat engine with a level controlled displacer driving mechanism. Data from experimental work done by Karabulut et al. is used to train and assess the algorithms. MATLAB is used to develop, implement and train the algorithms. The Root Mean Square Error (RMSE, Coefficient of determination (R2) and computational time are used to assess the performance of the algorithms.",
keywords = "Adaptive neurofuzzy inference system (ANFIS), Artificial neural network (ANN), Particle swarm optimisation (PSO), Stirling engine",
author = "MacHesa, {M. G.K.} and Tartibu, {L. K.} and Tekweme, {F. K.} and Okwu, {M. O.}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 6th International Conference on Soft Computing and Machine Intelligence, ISCMI 2019 ; Conference date: 19-11-2019 Through 20-11-2019",
year = "2019",
month = nov,
doi = "10.1109/ISCMI47871.2019.9004406",
language = "English",
series = "2019 6th International Conference on Soft Computing and Machine Intelligence, ISCMI 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "217--222",
booktitle = "2019 6th International Conference on Soft Computing and Machine Intelligence, ISCMI 2019",
address = "United States",
}