Terminal zeroing neural network for time-varying matrix computing under bounded noise
To improve the convergence performance of zeroing neural network(ZNN)for time-varying matrix computa-tion problems solving,a terminal zeroing neural network(TZNN)with noise resistance and its logarithmically acceler-ated form(LA-TZNN)were proposed.The terminal attraction of the error dynamic equation were analyzed,and the re-sults showed that the neural state of the proposed networks can converge to the theoretical solution within a fixed time when subjected to bounded noises.In addition,the LA-TZNN could achieve logarithmical settling-time stability,and its convergence speed was faster than the TZNN.Considering that the initial error was bounded in actual situations,an up-per bound of the settling-time in a semi-global sense was given,and an adjustable parameter was set to enable the net-work to converge within a predefined time.The two proposed models were applied to solve the time-varying matrix in-version and trajectory planning of redundant manipulators PUMA560.The simulation results further verified that com-pared with the conventional ZNN design,the proposed methods have shorter settling-time,higher convergence accuracy,and can effectively suppress bounded noise interference.
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