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In this research, three different time-varying mean-variance portfolio optimization (MVPO) problems are addressed using the zeroing neural network (ZNN) approach. The first two MVPO problems are defined as time-varying quadratic programming (TVQP) problems, while the third MVPO problem is defined as a time-varying nonlinear programming (TVNLP) problem. Then, utilizing real-world datasets, the time-varying MVPO problems are addressed by this alternative neural network (NN) solver and conventional MATLAB solvers, and their performances are compared in three various portfolio configurations. The results of the experiments show that the ZNN approach is a magnificent alternative to the conventional methods. To publicize and explore the findings of this study, a MATLAB repository has been established and is freely available on GitHub for any user who is interested.

The proportional–integral–derivative (PID) control systems, which have become a standard for technical and industrial applications, are the fundamental building blocks of classical and modern control systems. In this paper, a three-layer feed-forward neural network (NN) model trained to replicate the behavior of a PID controller is employed to stabilize control systems through a NN feedback controller. A novel bio-inspired weights-and-structure-determination (BIWASD) algorithm, which incorporates a metaheuristic optimization algorithm dubbed beetle antennae search (BAS), is used to train the NN model. More presicely, the BIWASD algorithm identifies the ideal weights and structure of the BIWASD-based NN (BIWASDNN) model utilizing a power sigmoid activation function while handling model fitting and validation. The results of three simulated trials on stabilizing feedback control systems validate and demonstrate the BIWASDNN model’s exceptional learning and prediction capabilities, while achieving similar or better performance than the corresponding PID controller. The BIWASDNN model is compared to three other high-performing NN models, and a MATLAB repository is accessible in public through GitHub to encourage and enhance this work.

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Realizations of the nonlinear energy operator (NEO), using the concept of companding filtering, are introduced and compared in this work. For this purpose, the Log-Domain and Sinh-Domain filtering techniques have been followed. Both topologies are constructed from differentiator and multiplier blocks which have been realized through the utilization of nonlinear transconductor cells. Both of the proposed topologies offer the capability of ultra-low voltage operation, thanks to the employment of MOS transistors in the weak inversion. Considering a single power supply voltage of 0.5 V, the behavior of the proposed NEO realizations has been simulated using the Analog Design Environment of the Cadence software and the design kit of the TSMC 130 nm process. Comparison results show that the Sinh-Domain realization offers a more power efficient design than that offered by the Log-Domain realization.