An improved maximum spread algorithm with application to complex-valued RBF neural networks

Decanoic acid-palmitic acid/SiO2@TiO2 phase change microcapsules based on RBF model with excellent photocatalysis performance and humidity control property

The decanoic acid-palmitic acid composite phase change material compounds with SiO₂ and TiO₂ to prepare decanoic acid-palmitic acid/SiO₂@TiO₂ phase change microcapsules (D-P-SiO₂@TiO₂ PCM). The D-P-SiO₂@TiO₂ PCM could show efficient temperature regulation, remove pollutants through photocatalysis, and control air humidity. However, it is difficult to obtain the best experimental scheme directly using the traditional experimental setup due to the complicated photocatalytic-humidity performance. The radial basis function (RBF) model optimized the uniform experimental design parameters, and the D-P-SiO₂@TiO₂ PCM showed enhanced photocatalytic-humidity performance. The RBF-calculated preparation parameters were as follows: the molar ratio of decanoic acid-palmitic acid to tetraethyl silicate was 0.42, pH was 1.83, the molar ratio of deionized water to tetraethyl silicate was 98.15, while the molar rate of tetrabutyl titanate to tetraethyl silicate was 0.76. The degradation rate of gaseous formaldehyde by the RBF-optimized D-P-SiO₂@TiO₂ PCM was 69.57% after 6 h, and the moisture content was between 0.0923 and 0.0940 g·g^-1 at 43.16-75.29% relative humidity (RH). The comparison between model optimization and the experiment sample prepared using the optimized parameters showed that the theoretical photocatalytic-humidity performance target value was 2.0502, and the tested target value was 2.0757. The error calculated from these two values was only 1.24%, and both were better than the best value of uniform experimental calculation. RBF mathematical model was proved to be an effective, convenient, and economic-saving method to simulate and predict D-P-SiO₂@TiO₂ PCM experimental design parameters. SEM and TEM analyses of the RBF-optimized D-P-SiO₂@TiO₂ PCM showed a uniform spherical structure, and the particle size analysis analyses was about 200 nm. The DSC analysis showed the phase transition temperature range was between 16.97 and 28.94 °C, within the comfort range of the human body. The UV-Vis investigations showed the absorption edge of the RBF-optimized D-P-SiO₂@TiO₂ PCM was 380 nm, in line with the band gap structure of the TiO₂ anatase phase. The thermogravimetric investigations showed that this composite was stable at normal temperature and pressure. After a 100 times hot-cold cycle, the quality of the RBF-optimized D-P-SiO₂@TiO₂ PCM maintained its stability, as the photocatalytic-humidity performance was almost the same. The N₂-adsorption analysis showed it had a high specific surface area and irregular pore structures, which could help it regulate air humidity. Considering these results, the D-P-SiO₂@TiO₂ PCM, a new ecological functional material, would be used in the construction industry to improve the architectural ecological environment.

A Machine Learning Method for the Detection of Brown Core in the Chinese Pear Variety Huangguan Using a MOS-Based E-Nose

The brown core is an internal disorder that significantly affects the palatability and economic value of Chinese pears. In this study, a framework that includes a back-propagation neural network (BPNN) and extreme learning machine (ELM) (BP-ELMNN) was proposed for the detection of brown core in the Chinese pear variety Huangguan. The odor data of pear were collected using a metal oxide semiconductor (MOS) electronic nose (E-nose). Principal component analysis was used to analyze the complexity of the odor emitted by pears with brown cores. The performances of several machine learning algorithms, i.e., radial basis function neural network (RBFNN), BPNN, and ELM, were compared with that of the BP-ELMNN. The experimental results showed that the proposed framework provided the best results for the test samples, with an accuracy of 0.9683, a macro-precision of 0.9688, a macro-recall of 0.9683, and a macro-F1 score of 0.9685. The results demonstrate that the use of machine learning algorithms for the analysis of E-nose data is a feasible and non-destructive method to detect brown core in pears.

On-line prediction of ferrous ion concentration in goethite process based on self-adjusting structure RBF neural network

Outlet ferrous ion concentration is an essential indicator to manipulate the goethite process in the zinc hydrometallurgy plant. However, it cannot be measured on-line, which leads to the delay of this feedback information. In this study, a self-adjusting structure radial basis function neural network (SAS-RBFNN) is developed to predict the outlet ferrous ion concentration on-line. First, a supervised cluster algorithm is proposed to initialize the RBFNN. Then, the network structure is adjusted by the developed self-adjusting structure mechanism. This mechanism can merge or divide the hidden neurons according to the distance of the clusters to achieve the adaptability of the RBFNN. Finally, the connection weights are determined by the gradient-based algorithm. The convergence of the SAS-RBFNN is analyzed by the Lyapunov criterion. A simulation for a benchmark problem shows the effectiveness of the proposed network. The SAS-RBFNN is then applied to predict the outlet ferrous ion concentration in the goethite process. The results demonstrate that this network can provide a more accurate prediction than the mathematical model, even under the fluctuating production condition.