![]() Practical application to robot manipulator and computer simulation results with using different activation functions and design parameters have verified the effectiveness, stability, and reliability of the proposed IRNN. Furthermore, by using the discretization method, the discrete IRNN model and its convergence analysis are also presented. I’ve not read ‘The Hitchhiker’s Guide to the Galaxy’. To obtain faster convergence performance and finite-time convergence property, an SP-AF is designed. If we included the Identity Activation function this list would contain 42 activation functions, although you could say with the inclusion of the bipolar sigmoid that it is indeed 42. The IRNN has finite-time convergence property by using power activation function. Specifically, if linear or bipolar-sigmoid activation functions are applied, the IRNN possess exponential convergence performance. ![]() The IRNN can achieve global convergence performance and strong robustness if odd-monotonically increasing activation functions are applied. The Bipolar Sigmoid Function is similar to the sigmoid function but this activation function takes the input (which may have any value between plus infinity. This kind of recurrent neural networks is based on an error-integral design equation and does not need training in advance. To solve a general time-varying Sylvester equation, a novel integral recurrent neural network (IRNN) is designed and analyzed. ![]()
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