The Optimal Adaptive-Based Neurofuzzy Control of the 3-DOF Musculoskeletal System of Human Arm in a 2D Plane

Each individual performs different daily activities such as reaching and lifting with his hand that shows the important role of robots designed to estimate the position of the objects or the muscle forces. Understanding the body’s musculoskeletal system’s learning control mechanism can lead us to develop a robust control technique that can be applied to rehabilitation robotics. The musculoskeletal model of the human arm used in this study is a 3-link robot coupled with 6 muscles which a neurofuzzy controller of TSK type along multicritic agents is used for training and learning fuzzy rules. The adaptive critic agents based on reinforcement learning oversees the controller’s parameters and avoids overtraining. The simulation results show that in both states of with/without optimization, the controller can well track the desired trajectory smoothly and with acceptable accuracy. The magnitude of forces in the optimized model is significantly lower, implying the controller’s correct operation. Also, links take the same trajectory with a lower overall displacement than that of the nonoptimized mode, which is consistent with the hand’s natural motion, seeking the most optimum trajectory.

Application of Adaptive Neurofuzzy Control in the Field of Credit Insurance

Credit insurance is of vital importance for the trade sector and almost every related business. Moreover, every policy in credit insurance is tailor-made in order to suit in the best available way the unique needs and demands of the insured business. Thus, pricing of such service can be tricky for an insurance company. In the present chapter, this pricing problem in the field of credit insurance will be addressed through the use of intelligent control mechanisms. More specifically, a way of calculating the price of insurance policies that has to be paid by a prospective client of an insurance company will be suggested. The model will be created and implemented with the use of fuzzy logic, and more specifically, through the implementation of an adaptive neurofuzzy inference system. The training data that will be used for the tuning of the system will be derived from real anonymous insurance policies of the Greek insurance market.