Thefilter-x least mean square(FxLMS)algorithm is widely used in active noise control(ANC)systems.However,because the algorithm is a feedback control algorithm based on the minimization of the error signal variance to ...Thefilter-x least mean square(FxLMS)algorithm is widely used in active noise control(ANC)systems.However,because the algorithm is a feedback control algorithm based on the minimization of the error signal variance to update thefilter coefficients,it has a certain delay,usually has a slow convergence speed,and the system response time is long and easily affected by the learning rate leading to the lack of system stability,which often fails to achieve the desired control effect in practice.In this paper,we propose an active control algorithm with near-est-neighbor trap structure and neural network feedback mechanism to reduce the coefficient update time of the FxLMS algorithm and use the neural network feedback mechanism to realize the parameter update,which is called NNR-BPFxLMS algorithm.In the paper,the schematic diagram of the feedback control is given,and the performance of the algorithm is analyzed.Under various noise conditions,it is shown by simulation and experiment that the NNR-BPFxLMS algorithm has the following three advantages:in terms of performance,it has higher noise reduction under the same number of sampling points,i.e.,it has faster convergence speed,and by computer simulation and sound pipe experiment,for simple ideal line spectrum noise,compared with the convergence speed of NNR-BPFxLMS is improved by more than 95%compared with FxLMS algorithm,and the convergence speed of real noise is also improved by more than 70%.In terms of stability,NNR-BPFxLMS is insensitive to step size changes.In terms of tracking performance,its algorithm responds quickly to sudden changes in the noise spectrum and can cope with the complex control requirements of sudden changes in the noise spectrum.展开更多
To design a multi-population adaptive genetic BP algorithm, crossover probability and mutation probability are self-adjusted according to the standard deviation of population fitness in this paper. Then a hybrid model...To design a multi-population adaptive genetic BP algorithm, crossover probability and mutation probability are self-adjusted according to the standard deviation of population fitness in this paper. Then a hybrid model combining Fuzzy Neural Network and multi-population adaptive genetic BP algorithm—Adaptive Genetic Fuzzy Neural Network (AGFNN) is proposed to overcome Neural Network’s drawbacks. Furthermore, the new model has been applied to financial distress prediction and the effectiveness of the proposed model is performed on the data collected from a set of Chinese listed corporations using cross validation approach. A comparative result indicates that the performance of AGFNN model is much better than the ones of other neural network models.展开更多
With the rapid development of China’s economy,the scale of the city has been continuously expanding,industrial enterprises have been increasing,the discharge of multiple pollutants has reached the top of the world,an...With the rapid development of China’s economy,the scale of the city has been continuously expanding,industrial enterprises have been increasing,the discharge of multiple pollutants has reached the top of the world,and the environmental problems become more and more serious.The air pollution problem is particularly prominent.Air quality has become a daily concern for people.In order to control air pollution,it is necessary to grasp the air quality situation in an all-round way.It is necessary to evaluate air quality.Accurate results of air quality evaluation can help people know more about air quality.In this paper,refers to previous research results and different evaluation methods,combined with artificial neural network,fuzzy theory,genetic algorithm,GA-BP hybrid algorithm based on fuzzy theory is proposed to evaluate air quality.At the same time,for the problem that the two-grade standard of air quality annual evaluation is not suitable for practical application,the four-grade standard for annual air quality evaluation has been proposed,and its practicality has been verified through experiments.By setting contrast experiments and comparing the air quality evaluation model based on standard BP algorithm,it is proved that the fuzzy GA-BP evaluation model is better than the standard BP model,both in efficiency and accuracy.展开更多
在风电场风速预测方法中,BP神经网络是常用的方法之一。针对BP神经网络相关参数选取不当影响预测结果的问题,提出一种基于麻雀搜索算法(sparrow search algorithm,SSA)对BP神经网络的权值W及阈值B的优化方法,构建SSA-BP超短期风速预测...在风电场风速预测方法中,BP神经网络是常用的方法之一。针对BP神经网络相关参数选取不当影响预测结果的问题,提出一种基于麻雀搜索算法(sparrow search algorithm,SSA)对BP神经网络的权值W及阈值B的优化方法,构建SSA-BP超短期风速预测模型。从数据采集与监视控制(supervisory control and data acquisition,SCADA)系统中收集得到的风速数据,预处理后及分类之后,使用该模型进行预测。实验表明,与BP、遗传算法(genetic algorithm,GA)-BP、粒子群算法(particle swarm optimization,PSO)-BP预测模型相比,SSA-BP预测模型可有效提高风电场风速预测的准确率,且收敛速度快、寻优能力强。展开更多
近年来,风力发电装机规模在全球范围内迅速增长。风力发电功率取决于风速及其所携带的能量,而风速是一个不断变化的随机变量。为了使风电场高效有序运行,减少因波动性、随机性对电网的冲击,降低弃风限电率,准确的短期风功率预测是必不...近年来,风力发电装机规模在全球范围内迅速增长。风力发电功率取决于风速及其所携带的能量,而风速是一个不断变化的随机变量。为了使风电场高效有序运行,减少因波动性、随机性对电网的冲击,降低弃风限电率,准确的短期风功率预测是必不可少的。基于灰狼优化算法(Grey Wolf Optimizer,GWO)-反向传播(Back Propagation,BP)神经网络模型预测风电场风功率,结果表明,它对短期风力发电的预测准确可靠。应用的GWO-BP模型所得结果与标准BP模型和遗传算法优化的BP模型进行比较,证明此模型预测精度更高。展开更多
基金This work was supported by the National Key R&D Program of China(Grant No.2020YFA040070).
文摘Thefilter-x least mean square(FxLMS)algorithm is widely used in active noise control(ANC)systems.However,because the algorithm is a feedback control algorithm based on the minimization of the error signal variance to update thefilter coefficients,it has a certain delay,usually has a slow convergence speed,and the system response time is long and easily affected by the learning rate leading to the lack of system stability,which often fails to achieve the desired control effect in practice.In this paper,we propose an active control algorithm with near-est-neighbor trap structure and neural network feedback mechanism to reduce the coefficient update time of the FxLMS algorithm and use the neural network feedback mechanism to realize the parameter update,which is called NNR-BPFxLMS algorithm.In the paper,the schematic diagram of the feedback control is given,and the performance of the algorithm is analyzed.Under various noise conditions,it is shown by simulation and experiment that the NNR-BPFxLMS algorithm has the following three advantages:in terms of performance,it has higher noise reduction under the same number of sampling points,i.e.,it has faster convergence speed,and by computer simulation and sound pipe experiment,for simple ideal line spectrum noise,compared with the convergence speed of NNR-BPFxLMS is improved by more than 95%compared with FxLMS algorithm,and the convergence speed of real noise is also improved by more than 70%.In terms of stability,NNR-BPFxLMS is insensitive to step size changes.In terms of tracking performance,its algorithm responds quickly to sudden changes in the noise spectrum and can cope with the complex control requirements of sudden changes in the noise spectrum.
文摘To design a multi-population adaptive genetic BP algorithm, crossover probability and mutation probability are self-adjusted according to the standard deviation of population fitness in this paper. Then a hybrid model combining Fuzzy Neural Network and multi-population adaptive genetic BP algorithm—Adaptive Genetic Fuzzy Neural Network (AGFNN) is proposed to overcome Neural Network’s drawbacks. Furthermore, the new model has been applied to financial distress prediction and the effectiveness of the proposed model is performed on the data collected from a set of Chinese listed corporations using cross validation approach. A comparative result indicates that the performance of AGFNN model is much better than the ones of other neural network models.
文摘With the rapid development of China’s economy,the scale of the city has been continuously expanding,industrial enterprises have been increasing,the discharge of multiple pollutants has reached the top of the world,and the environmental problems become more and more serious.The air pollution problem is particularly prominent.Air quality has become a daily concern for people.In order to control air pollution,it is necessary to grasp the air quality situation in an all-round way.It is necessary to evaluate air quality.Accurate results of air quality evaluation can help people know more about air quality.In this paper,refers to previous research results and different evaluation methods,combined with artificial neural network,fuzzy theory,genetic algorithm,GA-BP hybrid algorithm based on fuzzy theory is proposed to evaluate air quality.At the same time,for the problem that the two-grade standard of air quality annual evaluation is not suitable for practical application,the four-grade standard for annual air quality evaluation has been proposed,and its practicality has been verified through experiments.By setting contrast experiments and comparing the air quality evaluation model based on standard BP algorithm,it is proved that the fuzzy GA-BP evaluation model is better than the standard BP model,both in efficiency and accuracy.
文摘在风电场风速预测方法中,BP神经网络是常用的方法之一。针对BP神经网络相关参数选取不当影响预测结果的问题,提出一种基于麻雀搜索算法(sparrow search algorithm,SSA)对BP神经网络的权值W及阈值B的优化方法,构建SSA-BP超短期风速预测模型。从数据采集与监视控制(supervisory control and data acquisition,SCADA)系统中收集得到的风速数据,预处理后及分类之后,使用该模型进行预测。实验表明,与BP、遗传算法(genetic algorithm,GA)-BP、粒子群算法(particle swarm optimization,PSO)-BP预测模型相比,SSA-BP预测模型可有效提高风电场风速预测的准确率,且收敛速度快、寻优能力强。
文摘近年来,风力发电装机规模在全球范围内迅速增长。风力发电功率取决于风速及其所携带的能量,而风速是一个不断变化的随机变量。为了使风电场高效有序运行,减少因波动性、随机性对电网的冲击,降低弃风限电率,准确的短期风功率预测是必不可少的。基于灰狼优化算法(Grey Wolf Optimizer,GWO)-反向传播(Back Propagation,BP)神经网络模型预测风电场风功率,结果表明,它对短期风力发电的预测准确可靠。应用的GWO-BP模型所得结果与标准BP模型和遗传算法优化的BP模型进行比较,证明此模型预测精度更高。
