To overcome the effect of temperature on laser gyro zero bias and to stabilize the laser gyro output, this study proposes a modified radial basis function neural network (RBFNN) based on a Kohonen network and an orthogonal least squares (OLS) algorithm. The modified method, which combines the pattern classification capability of the Kohonen network and the optimal choice capacity of OLS, avoids the random selection of RBFNN centers and improves the compensation accuracy of the RBFNN. It can quickly and accurately identify the effect of temperature on laser gyro zero bias. A number of comparable identification and compensation tests on a variety of temperature-changing situations are completed using the multiple linear regression (MLR), RBFNN and modified RBFNN methods. The test results based on several sets of gyro output in constant and changing temperature conditions demonstrate that the proposed method is able to overcome the effect of randomly selected RBFNN centers. The running time of the method is about 60 s shorter than that of traditional RBFNN under the same test conditions, which suggests that the calculations are reduced. Meanwhile, the compensated gyro output accuracy using the modified method is about 7.0 × 10⁻⁴ °/h; comparatively, the traditional RBFNN is about 9.0 × 10⁻⁴ °/h and the MLR is about 1.4 × 10⁻³ °/h.

Carbon capture systems and the utilization of renewable energy are key waysto reduce carbon emissions, but their uncertainty seriously affects the stableoperation and economic efficiency of power systems. To tackle this challenge, alow-carbon economic scheduling model for microgrid electric-thermal integratedenergy systems(IES) considering uncertainty is proposed in this paper. The modelsuggests an ideal dispatch of the IES system that accounts for the disruption ofthe carbon capture system’s integration into the grid belt and plans for the carbon capture plant’s low carbon features. Coupling of wind energy with carboncapture power plants employing incentives for system operation and dispatch toincorporate load-side demand response resources. The system’s low carbon performance is increased through cooperative source-load resource optimization. Awell-established industrial automation optimal dispatch model is developed usingYALMIP and CPLEX. The results reveal that the method contributes to the resolution of the system adaptability problem and encourages a balance between lowcarbon and economics for large loads, taking into account that uncertainty raisesthe cost by around 3%, thereby boosting system robustness to an acceptabledegree.