Complex network science is an interdisciplinary field of study based on graph theory, statistical mechanics, and data science. With the powerful tools now available in complex network theory for the study of network topology, it is obvious that complex network topology models can be applied to enhance artificial neural network models. In this paper, we provide an overview of the most important works published within the past 10 years on the topic of complex network theory-based optimization methods. This review of the most up-to-date optimized neural network systems reveals that the fusion of complex and neural networks improves both accuracy and robustness. By setting out our review findings here, we seek to promote a better understanding of basic concepts and offer a deeper insight into the various research efforts that have led to the use of complex network theory in the optimized neural networks of today.

The bulk-heterojunction (BHJ) system that uses a $\pi$-conjugated polymer as an electron donor, and a fullerene derivative as an electron acceptor, is widely used in organic solar cells (OSCs) to facilitate efficient charge separation and extraction. However, the conventional BHJ system still suffers from unwanted phase segregation caused by the existence of significant differences in surface energy between the two BHJ components and the charge extraction layer during film formation. In the present work, we demonstrate a sophisticated control of fast film-growth kinetics that can be used to achieve a uniform distribution of donor and acceptor materials in the BHJ layer of OSCs without undesirable phase separation. Our approach involves depositing the BHJ solution onto a spinning substrate, thus inducing rapid evaporation of the solvent during BHJ film formation. The fast-growth process prevents the fullerene derivative from migrating toward the charge extraction layer, thereby enabling a homogeneous distribution of the fullerene derivative within the BHJ film. The OSCs based on the fast-growth BHJ thin film are found to exhibit substantial increases in J_SC, fill factor, and a PCE up to 11.27 mA/cm², 66%, and 4.68%, respectively; this last value represents a remarkable 17% increase in PCE compared to that of conventional OSCs.

Maximally flat (MAXFLAT) half-band filters usually have wider transition-band than other filters although the frequency response is maximally flat (i.e., no ripples) in the passband and stopband. This is due to fact that the maximum possible number of zeros at z = ±1 is imposed in half-band close form solution, which leaves no degree of freedom, and thus no independent parameters for direct control of the frequency response. This paper describes a novel method for the design of FIR half-band filters with an explicit control of the transition bandwidth. The proposed method is based on a generalized Lagrange half-band polynomial ( g-LHBP) with coefficients parameterizing a 0-th coefficient h ₀ and allows the frequency response of this filter type to be controllable by adjusting h ₀ . Then, h ₀ is modeled as a steepness parameter of the transition-band and this is accomplished through theoretically analyzing a polynomial recurrence relation of the g-LHBP. This method also provides explicit formulas for direct computation of design parameters related to choosing a desired filter characteristic (by a reasonable trade-off between the transition-band sharpness and passband & stopband flatness). The examples are shown to provide a complete and accurate solution for the design of such filters with relatively sharper transition-band steepness than other existing half-band filters.

Maximally-flat (MAXFLAT) finite impulse response (FIR) filters often face a problem of the cutoff-frequency error due to approximation of the desired frequency response by some closed-form solution. So far, there have been plenty of efforts to design such a filter with an arbitrarily specified cut off-frequency, but this filter type requires extensive computation and is not MAXFLAT anymore. Thus, a computationally efficient and effective design is needed for highly accurate filters with desired frequency characteristics. This paper describes a new method for designing cutoff-frequency-fixing FIR filters through the cutoff-frequency error compensation of MAXFLAT FIR filters. The proposed method provides a closed-form Chebyshev polynomial containing a cutoff-error compensation function, which can characterize the “cutoff-error-free” filters in terms of the degree of flatness for a given order of filter and cut off-frequency. This method also allows a computationally efficient and accurate formula to directly determine the degree of flatness, so that this filter type has a flat magnitude characteristic both in the passband and the stopband. The remarkable effectiveness of the proposed method in design efficiency and accuracy is clearly demonstrated through various examples, indicating that the cutoff-fixing filters exhibit amplitude distortion error of less than 10⁻¹⁴ and no cut off-frequency error. This new approach is shown to provide significant advantages over the previous works in design flexibility and accuracy.