Abstract: With the ever increasing computational ability of modern machines and their ability to learn by themselves has led to the sustained growth of Artificial Intelligence. When measuring few parameters from the ether (dynamic), huge amount of data is obtained. The amount of knowledge available is too large for explicit encoding by humans. Machines that learn this knowledge can reproduce more accurate results/ predictions at specified instants. Environments change over time. Machines that can adapt to a changing environment would reduce the need for constant redesign. In this paper we obtain motion values of objects in a lab environment and make analysis of the dynamics using fuzzy logic. The machine is trained using machine learning algorithms to make predictions on motion. These predictions are further fed to the machine to increase the intelligence of the predicting model and to establish a controlled loopback model. Use of fuzzy logic, further refines the accuracy of the approach and rules out the present incompatible two logic level approach.
Keywords: Fuzzy Logic, Data Analytics and Machine Learning, Artificial Intelligence, Logistic regression, accuracy, precision, F1 score, categorical values, visualization, Heatmap analysis is done for knowledge of correlation
| DOI: 10.17148/IJARCCE.2019.81101