In this paper, fuzzy networked systems with a randomly varying delay and quantization errors are considered to represent signal transmission systems of nonlinearly interactive sources. A source estimation scheme is proposed by using a multiobjective approach, addressing the concerns of estimation errors and transmission power consumption. A mixed <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex Notation="TeX">$H_{2}/H_{\infty }$</tex></formula> design is employed to enhance the estimation performance, while the number of quantized bits is optimized to reduce the power consumption. A Pareto front representation is adopted so that the proposed estimation scheme is designed from a broader perspective in contrast with the conventional single-objective approach. It turns out that the proposed source estimator parameters can serve as decision variables of a multiobjective optimization problem (MOP) with linear matrix inequality constraints. This MOP can be solved by using deterministic algorithms, such as interior-point methods, for solutions of internal variables and using stochastic algorithms, such as multiobjective evolutionary algorithms, for the global optimality. Numerical examples are provided to illustrate the proposed methodology.
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