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Abstract: In this paper we first introduce a new approach to optimise the cascaded spline adaptive filter (CSAF) for identifying unknown nonlinear systems by using a meta-heuristic optimisation algorithm (MOA). The CSAF architecture combines Hammerstein and Wiener systems, where the nonlinear blocks are implemented with the spline network. The algorithms used optimise the weights of the spline interpolation function and linear filter by using an adequately weighted cost function, leading to improved filter stability, steady state performance, and guaranteed convergence to globally optimal solutions. In this study we investigated two CSAF architectures: Hammerstein-Wiener SAF (HW-SAF) and Wiener-Hammerstein SAF (WH-SAF) structures. These architectures have been designed using gradient-based approaches which are inefficient due to poor convergence speed, and produce suboptimal solutions in a Gaussian noise environment. To avert these difficulties, we estimated the design parameters of CSAF architecture using four independent MOAs: differential evolution (DE), brain storm optimisation (BSO), multi-verse optimiser (MVO) and a recently proposed remora optimisation algorithm (ROA). In ROA, the remora factor’s control parameters produce near-global optimal parameters with a faster convergence speed. ROA also ensures the most passably balanced exploration and exploitation phases compared to DE, GSA and SSA-based design approaches. Finally, the identification results of three numerical and industry-specific benchmark systems, including coupled electric drives, a thermic wall and a continuous stirred tank reactor, are presented to emphasise the effectiveness of the ROA-based CSAF design.