Honeycomb core sandwich plates are widely used as a lightweight, high-strength sound insulation material. However, they do not perform well in specific frequency bands. Acoustic metamaterials can break the law of mass in specific frequency bands and have high sound transmission loss (STL); however, the resonance frequency is difficult to regulate. To solve this problem, this paper first proposes an infinitely large metamaterial honeycomb core sandwich plate, which can generate newly tuned piezoelectric resonance frequencies, and we study its STL. The structure has piezoelectric patches arranged in sub-wavelength arrays with inductance shunting circuits that are elastically connected to both sides of the honeycomb core sandwich plate. The effective dynamic mass density and effective dynamic bending stiffness of the metamaterial plates were obtained using the effective medium (EM) method. A theoretical model for the numerical calculation of oblique STL and diffuse-field STL was established by the structural bending wave method. The finite element simulation method was used to verify that the metamaterial plates can generate three peaks at 1147 Hz, 1481 Hz and 1849 Hz in oblique or diffuse-field STL curves, which reached 57 dB, 86 dB and 63 dB, respectively, and are significantly better than the plate rigidly connected with piezoelectric sheets and the bare plate with the same mass. In order to better understand the characteristics of STL, the explicit functions of the resonance frequencies were derived. Key influencing factors were analyzed, and the regulation law of new piezoelectric resonance frequencies was clarified.

When dealing with specific tasks, the hidden layer output matrix of an extreme learning machine (ELM) may change, largely due to the random assigned weight matrix of the input layer and the threshold matrix of the hidden layer, which sequentially leads to the corresponding change to output weights. The unstable fluctuations of the output weights increase the structural risk and the empirical risk of ELM. This paper proposed a fuzzy adaptive particle swarm optimization (PSO) algorithm to solve this problem, which could nonlinearly control the inertia factor during the iteration by fuzzy control. Based on the fuzzy adaptive PSO-ELM algorithm, a sound quality prediction model was developed. The prediction results of this model were compared with the other three sound quality prediction models. The results showed that the fuzzy adaptive PSO-ELM model was more precise. In addition, in comparison with two other adaptive inertia factor algorithms, the fuzzy adaptive PSO-ELM model was the fastest model to reach goal accuracy.