Bird swarm algorithm (BSA) is a new heuristic intelligent algorithm, which has been successfully applied in many fields. In view of the shortcomings of bird swarm algorithm which is easy to fall into local optimum and premature convergence. I propose an improved bird swarm algorithm (IBSA). Firstly, the initial population is constructed by chaos optimization algorithm, so that the initial solution is uniformly distributed in the solution space, thus improving the diversity of the population. Secondly, by introducing inertial weights, nonlinear adjustment of cognitive and social coefficients can be trade-off bird local and global search ability. Finally, a disturbance strategy is added to the forging position of the birds. Thus, the diversity of population in the late iteration period is enhanced, and the ability to jump out of the local optimum is improved. Through the simulation experiments of several benchmark functions and compared with other intelligent algorithms, the results show that the improved bird swarm algorithm (IBSA) has better convergence speed and optimization precision, which proves its superiority.
Based on the principles of neuromechanics, human arm movements result from the dynamic interaction between the nervous, muscular, and skeletal systems. To develop an effective neural feedback controller for neuro-rehabilitation training, it is important to consider both the effects of muscles and skeletons. In this study, we designed a neuromechanics-based neural feedback controller for arm reaching movements. To achieve this, we first constructed a musculoskeletal arm model based on the actual biomechanical structure of the human arm. Subsequently, a hybrid neural feedback controller was developed that mimics the multifunctional areas of the human arm. The performance of this controller was then validated through numerical simulation experiments. The simulation results demonstrated a bell-shaped movement trajectory, consistent with the natural motion of human arm movements. Furthermore, the experiment testing the tracking ability of the controller revealed real-time errors within one millimeter, with the tensile force generated by the controller’s muscles being stable and maintained at a low value, thereby avoiding the issue of muscle strain that can occur due to excessive excitation during the neurorehabilitation process.
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