Tags: Microsoft Lync 2010 Case StudiesAstronomy Coursework ListBrave New World Ap Essay PromptsCritical And Creative Thinking Skills ListEasy Rubric 5 Paragraph EssayStart Creative WritingProblems In Everyday Life That Need SolvingTerm Paper On Child Labour In BangladeshBooks To Use In Sat EssayCan Someone Do My Homework
The second layer is responsible of generating the firing strengths for the rules.Due to its task, the second layer is denoted as "rule layer".In this thesis, an active vibration control system was developed.
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 data fusion model can outperform individual machine-based control algorithm and welder intelligence-based models (with and without VR enhancement).
Finally a data-driven approach is proposed to model human welder adjustments in 3D (including welding speed, arc length, and torch orientations).
ANFIS model is then proposed to correlate the 3D weld pool characteristic parameters and welder’s torch movements.
A foundation is thus established to rapidly extract human intelligence and transfer such intelligence into welding robots.
The learning of human welder movement (i.e., welding speed) is first realized with Virtual Reality (VR) enhancement using iterative K-means based local ANFIS modeling.
As a separate effort, the learning is performed without VR enhancement utilizing a fuzzy classifier to rank the data and only preserve the high ranking “correct” response.
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.