Abstract
This paper describes an improved global harmony search (IGHS) algorithm for identifying the nonlinear discrete-time systems based on second-order Volterra model. The IGHS is an improved version of the novel global harmony search (NGHS) algorithm, and it makes two significant improvements on the NGHS. First, the genetic mutation operation is modified by combining normal distribution and Cauchy distribution, which enables the IGHS to fully explore and exploit the solution space. Second, an opposition-based learning (OBL) is introduced and modified to improve the quality of harmony vectors. The IGHS algorithm is implemented on two numerical examples, and they are nonlinear discrete-time rational system and the real heat exchanger, respectively. The results of the IGHS are compared with those of the other three methods, and it has been verified to be more effective than the other three methods on solving the above two problems with different input signals and system memory sizes.
Highlights
The Volterra model is a kind of nonlinear filter model, which is usually employed to track and identify plenty of complex nonlinear systems
The novel global harmony search (NGHS) parameters consist of harmony memory size HMS, the number of improvisations NI, and mutation rate pm
To verify the validity of the improved global harmony search (IGHS) on identifying nonlinear system based on second-order Volterra filter model, two examples including the highly nonlinear discrete-time rational system and the real heat exchanger are considered
Summary
The Volterra model is a kind of nonlinear filter model, which is usually employed to track and identify plenty of complex nonlinear systems. Gruber et al [3] presented a nonlinear model predictive control (NMPC) method based on a secondorder Volterra series model for greenhouse temperature control using natural ventilation. These models, denoting the simple and logical extension of convolution models, are capable of describing the nonlinear dynamic feature of the ventilation and other environmental conditions on the greenhouse temperature. We develop an improved global harmony search (IGHS) algorithm and try the IGHS as an efficient candidate for system identification based on Volterra filter model.
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