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The paper investigates a novel technique of designing 2-dimensional IIR digital filters using a modified version of Differential Evolution (DE) where the scalar factor used for weighing the difference vector is made to vary randomly. This approach makes the classical DE more stochastic and provides it with additional exploration capability over the search space. The task of the design has been reformulated as a constrained minimization problem and is solved by the convergence of the proposed algorithm. Numerical example has been provided in support of the theoretical results. The paper also attempts to demonstrate the superiority of the proposed design method by comparing with a few previous design methods based on GA and neural networks.

Design of Two-Dimensional IIR Filters Using an Improved DE Algorithm