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Automated welding experiments confirm the effectiveness of the proposed human response model.A virtualized welding system is then developed that enables transferring the human knowledge into a welding robot.Closed-form model predictive control (MPC) algorithm is derived for real-time welding applications.
The results of ANFIS forecasting models and observed values are compared and performances of models were evaluated.
Moreover, the best fit models have been also trained and tested by Feed Forward Neural Networks (FFNN).
The role of the third layer is to normalize the computed firing strengths, by diving each value for the total firing strength.
The fourth layer takes as input the normalized values and the consequence parameter set .
In more details, the architecture is composed by five layers.
The first layer takes the input values and determines the membership functions belonging to them. The membership degrees of each function are computed by using the premise parameter set, namely .
The results demonstrate that ANFIS can be successfully applied and provide high accuracy and reliability for drought forecasting.
An adaptive neuro-fuzzy inference system or adaptive network-based fuzzy inference system (ANFIS) is a kind of artificial neural network that is based on Takagi–Sugeno fuzzy inference system. Since it integrates both neural networks and fuzzy logic principles, it has potential to capture the benefits of both in a single framework.
The trained supervised ANFIS model is transferred to the welding robot and the performance of the controller is examined.
A fuzzy weighting based data fusion approach to combine multiple machine and human intelligent models is proposed.