<p>Machine learning and artificial intelligence are becoming a major influence in various research and commercial fields. This review attempts to explain machine learning techniques and applications in various fields. Challenges and future directions are also proposed, including data analysis suggestions, effective algorithms based on the situation, industrial implementation, organization’s risk tolerance, cost-benefit comparisons and the future of machine learning techniques. Applications discussed in this paper range from technological development and health care to financial issues and sports analytics.</p>
Robotic grippers are commonly required to grasp and manipulate loads under a wide range of operating conditions, without the load slipping from the end-effector and avoiding damage to the load. The increasing demand on robotic gripper performance leads to the use of advanced control strategies. Literature shows that many fuzzy controllers have peculiar and interesting structures i.e., nonlinear controllers with variable gains. Most fuzzy controllers have been treated and used as black-box controllers in the sense that their analytical structures are unknown. Knowing the explicit structure information will enable one to insightfully understand how a fuzzy control works. In the present paper a novel technique is presented for deriving input-out put relations for the fuzzy controller that uses Zadeh AND operator for symmetrical and unsymmetrical triangular input fuzzy sets. The efficacy of the controller is tested on Robot gripper. It is observed that the symmetrical fuzzy sets are resulting to better response than the unsymmetrical fuzzy sets.
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