A particle swarm optimization approach to nonlinear rational filter modeling
Expert Systems with Applications: An International Journal
A novel adaptive bilinear filter based on pipelined architecture
Digital Signal Processing
Computational intelligence approach to PID controller design using the universal model
Information Sciences: an International Journal
A new modified particle swarm optimization algorithm for adaptive equalization
Digital Signal Processing
IEEE Transactions on Signal Processing
Volterra series representation of time-frequency distributions
IEEE Transactions on Signal Processing
Minimum Mean-Square Error Equalization for Second-Order Volterra Systems
IEEE Transactions on Signal Processing - Part I
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This paper focuses on the identification problem of nonlinear discrete-time systems using Volterra filter series model. Generally, to update the kernels of Volterra model, the most commonly used method is the gradient adaptive algorithm. However, this method probably traps at the local minimum for searching parameter solutions. In this study, a new intelligence swarm computation of the global search is considered. We utilize an improved particle swarm optimization (IPSO) algorithm to design the Volterra kernel parameters. It is somewhat different from the original algorithm due to modifying its velocity updating formula and this can promote the algorithm@?s searching ability for solving the optimization problem. Using the IPSO algorithm to minimize the mean square error (MSE) between the actual output and model output, the identification problem for nonlinear discrete-time systems can be fulfilled. Finally, two different kinds of examples are provided to demonstrate the efficiency of the proposed method. Moreover, some examinations including the Volterra model memory size and algorithm initial condition are further considered.