When the quality of liquid ammonia is measured by volumetric flowmeter, the traditional quadratic expression method can't meet the accuracy of temperature compensation in modern coal chemical industry. So the temperature compensation method by support vector machine (SVM) regression is presented, and kernel function parameter σ of SVM is optimized by variable weight particle swarm optimization (PSO). After the performance analysis and comparison in PSO, the suitable linear inertia weight method is selected. Experimental results show that the temperature compensation accuracy of the SVM method based on the variable weight PSO is significantly higher than that of the traditional quadratic expression method.

With the development of smart healthcare, surgical and rehabilitation robots have permeated into daily medical operations. This raises concerns over the trajectory planning of medical manipulators. Based on particle swarm optimization (PSO) algorithm and fuzzy neural network (FNN), this paper puts forward a trajectory planning algorithm for medical manipulators, which ensures that the target medical manipulator can suppress the residual jitter at the end, while meeting the requirements of high precision, flexible operation, and disturbance resistance. Specifically, a kinetic model was constructed for a medical manipulator of multi-degrees-of-freedoms (DOFs) through position and posture transforms, and used to construct an FNN for trajectory planning. To suppress the jitter at the end, an adaptive PSO algorithm was designed, and combined with the FNN into a trajectory planning algorithm called PSO neural network (PSONN) algorithm. Finally, the proposed algorithm was proved effective through experiments. The research results provide the reference for applying PSO algorithm and FNN in other fields.

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