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Stability of the particle swarm optimization algorithm is analyzed without any simplifying assumptions made in the previous works. To evaluate the convergence speed of the algorithm, the decay rate is introduced, and a method for finding the largest lower bound of the decay rate is presented. Moreover, it is pointed out that the l2 gain of the algorithm can be used to measure exploration ability of...
This paper studies a property of neural network architecture for non-linear modeling. This method was proposed in our previous work and has three improvements; 1) the design of a sigmoidal function with localized derivative, 2) a deterministic scheme for weight initialization, and 3) an updating rule for weight parameters. We discuss its robustness against noise based on simulation results.
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