To solve the problem that subnetwork output cannot be optimally integrated in a modular neural network (MNN), this paper proposes an adaptive particle swarm optimization algorithm for dynamic MNN (APSO-DMNN). First, t...
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To solve the problem that subnetwork output cannot be optimally integrated in a modular neural network (MNN), this paper proposes an adaptive particle swarm optimization algorithm for dynamic MNN (APSO-DMNN). First, the method identifies the distribution of samples and updates the training parameters based on data potential. Second, the MNN activates the corresponding subnetworks according to the input data. Calculate the weights based on an APSO algorithm, which can dynamically optimize the contribution of the output. Then, the inertia weights in the APSO algorithm are adjusted by a nonlinear function in order to avoid being trapped into local optimal values. Finally, the proposed APSO-DMNN can be obtained based on the optimal integration and dynamic adjustment. Comparisons with other algorithms indicate that the proposed method is more effective in modeling and predicting.
To eliminate the noise interference caused by continuous external environmental disturbances on the rotor signals of a maglev gyroscope, this study proposes a noise reduction method that integrates an adaptive particl...
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To eliminate the noise interference caused by continuous external environmental disturbances on the rotor signals of a maglev gyroscope, this study proposes a noise reduction method that integrates an adaptiveparticleswarmoptimization variational modal decomposition algorithm with a strategy for error compensation of the trend term in reconstructed signals, significantly improving the azimuth measurement accuracy of the gyroscope torque sensor. The optimal parameters for the variational modal decomposition algorithm were determined using the adaptive particle swarm optimization algorithm, allowing for the accurate decomposition of noisy rotor signals. Additionally, using multi-scale permutation entropy as a criterion for discriminant, the signal components were filtered and summed to obtain the denoised reconstructed signal. Furthermore, an empirical mode decomposition algorithm was employed to extract the trend term of the reconstructed signal, which was then used to compensate for the errors in the reconstructed signal, achieving significant noise reduction. On-site experiments were conducted on the high-precision GNSS baseline of the Xianyang Yuan Tunnel in the second phase of the project to divert water from the Han River to the Wei River, where this method was applied to process and analyze multiple sets of rotor signals. The experimental results show that this method effectively suppresses continuous external environmental interference, reducing the average standard deviation of the compensated signals by 46.10% and the average measurement error of the north azimuth by 45.63%. Its noise reduction performance surpasses that of the other four algorithms.
Traditional greenhouse management often suffers from slow responsiveness and limited adaptability due to its reliance on manual operations. This study proposes a greenhouse environment monitoring and control system th...
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Traditional greenhouse management often suffers from slow responsiveness and limited adaptability due to its reliance on manual operations. This study proposes a greenhouse environment monitoring and control system that integrates Internet of Things (IoT) technologies with a fuzzy PID controller optimized through an adaptiveparticleswarmoptimization (APSO) algorithm. A real-time monitoring platform was developed based on a WebSocket-enabled front-end/back-end separation architecture. Environmental parameters, such as temperature and humidity, were collected by sensors and transmitted in real time to the platform via the MQTT protocol, enabling data visualization and anomaly detection. The APSO algorithm was employed offline to optimize the fuzzy PID parameters, and the resulting controller was implemented on a microcontroller to achieve real-time control. Compared with conventional PID control, the APSO-optimized controller reduced overshoot by 72.1% and shortened the settling time by 20%. Experimental results demonstrated that the system was less susceptible to external environmental disturbances, maintaining temperature fluctuations within 0.3 degrees C. This study provides a robust and effective solution for smart greenhouse management.
The complex nonlinear and strongly coupled dynamics of small unmanned helicopters make mathematical modeling challenging. Traditional approaches often rely on least squares support vector machine (LSSVM) algorithms us...
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The complex nonlinear and strongly coupled dynamics of small unmanned helicopters make mathematical modeling challenging. Traditional approaches often rely on least squares support vector machine (LSSVM) algorithms using standard kernel functions, which are limited in their learning and generalization capabilities, resulting in insufficient accuracy for flight control systems. This paper proposes an adaptiveparticleswarmoptimization-based LSSVM (APSO-LSSVM) method, incorporating a self-constructed kernel function. Using Mercer's theorem, the custom kernel addresses the limitations of conventional kernels and is integrated into the LSSVM framework. An adaptive particle swarm optimization algorithm, capable of dynamically adjusting inertia weights and learning factors, optimizes the model parameters, overcoming the standard particleswarmoptimization's tendency to get trapped in local optima. The model is trained using flight test data from a self-developed small unmanned helicopter, and its identification performance is cross-validated in the time domain against traditional models. Experimental results show that the proposed method significantly improves the modeling accuracy of small unmanned helicopters.
Along with the further integration of demand management and renewable energy technology, making optimal use of energy storage devices and coordinating operation with other devices are key. The present study takes into...
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Along with the further integration of demand management and renewable energy technology, making optimal use of energy storage devices and coordinating operation with other devices are key. The present study takes into account the current situation of power storage equipment. Based on one year of measured data, four cases are designed for a composite energy storage system (ESS). In this paper, a two-tiered optimization model is proposed and is used to optimizing the capacity of power storage devices and the yearly production of the system. Furthermore, this paper performs a comparative analysis of the performance of the four cases from the energy, environmental and economic perspectives. It is concluded that this kind of energy storage equipment can enhance the economics and environment of residential energy systems. The thermal energy storage system (TESS) has the shortest payback period (7.84 years), and the CO2 emissions are the lowest. Coupled with future price volatility and the carbon tax, the electrothermal hybrid energy storage system (HESS) has good development potential. However, the current investment cost is very high, and it will not be possible to recover this cost in 10 years. Finally, it is recommended that the cost of equipment be reduced in combination with subsidies and incentives for further promotion. The research results not only fill a gap in the study area, but also provide some suggestions for further development of industry and research on user-side energy storage.
Accurately predicting the surface finish of fused deposition modeling (FDM) parts is an important task for the engineering application of FDM technology. So far, many prediction models have been proposed by establishi...
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Accurately predicting the surface finish of fused deposition modeling (FDM) parts is an important task for the engineering application of FDM technology. So far, many prediction models have been proposed by establishing a mapping relationship between printing parameters and surface roughness. Each model can work well in its specific context;however, existing prediction models cannot meet the requirements of multi-factor and multi-category prediction of surface finish and cope with imbalanced data. Aiming at these issues, a prediction method based on a combination of the adaptiveparticleswarmoptimization and K-nearest neighbor (APSO-KNN) algorithms is proposed in this paper. Seven input variables, including nozzle diameter, layer thickness, number of perimeters, flow rate, print speed, nozzle temperature, and build orientation, are considered. The printing values of each specimen are determined using an L27 Taguchi experimental design. A total of 27 specimens are printed and experimental data for the 27 specimens are used for model training and validation. The results indicate that the proposed method can achieve a minimum classification error of 0.01 after two iterations, with a maximum accuracy of 99.0%, and high model training efficiency. It can meet the requirements of predicting surface finish for FDM parts with multiple factors and categories and can handle imbalanced data. In addition, the high accuracy demonstrates the potential of this method for predicting surface finish, and its application in actual industrial manufacturing.
Semi-active control is one of the most effective methods for damage reduction in offshore platforms subjected to intense environmental forces. Despite its advantages, probable device time delays may drastically decrea...
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Semi-active control is one of the most effective methods for damage reduction in offshore platforms subjected to intense environmental forces. Despite its advantages, probable device time delays may drastically decrease the real performance of the control algorithm. Thus, the uncertainty could make the control process non-optimal. In this paper, a Kalman Filter is used to ponder previous responses and the history of the measured errors in order to estimate the real state variables of an offshore structure equipped with Magneto-Rheological (MR) dampers. In the current article, the amount of applied voltage to the MR damper is optimized via the adaptiveparticleswarmoptimization (APSO) method. Furthermore, the structure is controlled by the Linear-Quadratic Regulator (LQR) algorithm. A newly installed offshore platform, located in the Persian Gulf at a depth of 64 m is considered as an example to demonstrate the performance of the controller. The offshore structure is assumed to be excited by near- and far-field earthquakes. The results of the parametric studies indicate that all the earthquake-induced vibrations of the platform can be effectively suppressed by the designed control system. Moreover, the life spans of the dampers may increase with the predicting-optimizing algorithm.
The dynamic vibration absorber, which is adopted to suppress the unbalanced vibration of rotor, is optimized for the optimal parameters in this paper. This paper proposes a parameter optimization method for dynamic vi...
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The dynamic vibration absorber, which is adopted to suppress the unbalanced vibration of rotor, is optimized for the optimal parameters in this paper. This paper proposes a parameter optimization method for dynamic vibration absorbers and seeks parameters of a dynamic vibration absorber with better vibration suppression performance. Firstly, the frequency response function of the dynamic vibration absorber-rotor coupling system is obtained by using the finite element method. Then, basing on the optimal mathematical model, the optimal design variables are solved with the adaptive particle swarm optimization algorithm. Also, an example is used to prove the validity of the optimization design method mentioned in this paper. Further, in order to master the influence of deviation from the optimal value on the suppressing vibration effect, the vibration suppression performance changes of the dynamic vibration absorber whose parameters deviate from the optimal value are analyzed. The results show that: compared with conventional design method, this method is more superior;The dynamic vibration absorber with optimal parameters has better vibration suppression performance;At the same degree deviated from the optimal value, the stiffness has a more remarkable influence on the vibration suppression performance than damping for suppressing the first resonance;For the dynamic vibration absorber which is adopted to suppress the fixed-frequency vibration, the influence of stiffness deviation on the vibration suppression performance appears an obvious interval which is related to working speed.
An inverted pendulum system (IPS) is a highly nonlinear dynamical open loop unstable system, typically used as a benchmark to verify the performance of controllers. The IPS emulates the behaviour of an altitude contro...
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An inverted pendulum system (IPS) is a highly nonlinear dynamical open loop unstable system, typically used as a benchmark to verify the performance of controllers. The IPS emulates the behaviour of an altitude control of a space booster or rocket on take-off. The problem is to develop suitable controllers to maintain the stabilization and swing up of an inverted pendulum on a cart. This paper presents the evolutionary tuning methods of nonlinear PID (NL-PID) controller for IPS with the multi-objective genetic algorithm (MOGA) and adaptiveparticleswarmoptimization (APSO) algorithm. The function of NL-PID controllers is to keep the pendulum in an upright position by maintaining the pendulum at same state and angle at zero degrees. The comparison of responses and performance of MOGA tuned NL-PID and APSO tuned NL-PID controllers for an IPS are described. The mathematical modeling and simulation analysis of the IPS is presented in detail to test the effectiveness of controller tuning algorithm. The APSO based tuning of the NL-PID controller has lesser chattering, noise and fast settling time than MOGA based tuning of the controller.
In this paper, a new particleswarmoptimizationalgorithm, adaptiveparticleswarmoptimization (APSO) algorithm, is proposed to optimize the stability and setting time of the fourth generation grate cooler scraper d...
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ISBN:
(纸本)9781509061266
In this paper, a new particleswarmoptimizationalgorithm, adaptiveparticleswarmoptimization (APSO) algorithm, is proposed to optimize the stability and setting time of the fourth generation grate cooler scraper during speed regulation. This algorithm replaces the compression factor with the time varying parameter by using the dynamic variation of the time varying parameters of the traditional PSO algorithm. At the same time, it is proved that the new PSO algorithm in this paper has faster convergence speed and higher efficiency than traditional PSO algorithm. Finally, algorithm is applied to the simulation of electro-hydraulic servo system of grate cooler, and good control effect is achieved.
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