In the classification problem,deep kernel extreme learning machine(DKELM)has the characteristics of efficient processing and superior performance,but its parameters optimization is difficult.To improve the classificat...In the classification problem,deep kernel extreme learning machine(DKELM)has the characteristics of efficient processing and superior performance,but its parameters optimization is difficult.To improve the classification accuracy of DKELM,a DKELM algorithm optimized by the improved sparrow search algorithm(ISSA),named as ISSA-DKELM,is proposed in this paper.Aiming at the parameter selection problem of DKELM,the DKELM classifier is constructed by using the optimal parameters obtained by ISSA optimization.In order to make up for the shortcomings of the basic sparrow search algorithm(SSA),the chaotic transformation is first applied to initialize the sparrow position.Then,the position of the discoverer sparrow population is dynamically adjusted.A learning operator in the teaching-learning-based algorithm is fused to improve the position update operation of the joiners.Finally,the Gaussian mutation strategy is added in the later iteration of the algorithm to make the sparrow jump out of local optimum.The experimental results show that the proposed DKELM classifier is feasible and effective,and compared with other classification algorithms,the proposed DKELM algorithm aciheves better test accuracy.展开更多
Pipeline isolation plugging robot (PIPR) is an important tool in pipeline maintenance operation. During the plugging process, the violent vibration will occur by the flow field, which can cause serious damage to the p...Pipeline isolation plugging robot (PIPR) is an important tool in pipeline maintenance operation. During the plugging process, the violent vibration will occur by the flow field, which can cause serious damage to the pipeline and PIPR. In this paper, we propose a dynamic regulating strategy to reduce the plugging-induced vibration by regulating the spoiler angle and plugging velocity. Firstly, the dynamic plugging simulation and experiment are performed to study the flow field changes during dynamic plugging. And the pressure difference is proposed to evaluate the degree of flow field vibration. Secondly, the mathematical models of pressure difference with plugging states and spoiler angles are established based on the extreme learning machine (ELM) optimized by improved sparrow search algorithm (ISSA). Finally, a modified Q-learning algorithm based on simulated annealing is applied to determine the optimal strategy for the spoiler angle and plugging velocity in real time. The results show that the proposed method can reduce the plugging-induced vibration by 19.9% and 32.7% on average, compared with single-regulating methods. This study can effectively ensure the stability of the plugging process.展开更多
This paper delves into the parameter tuning of fractional-order PID(FOPID)controllers.FOPID controllers,with additional integral and derivative orders compared to traditional PID controllers,possess enhanced capabilit...This paper delves into the parameter tuning of fractional-order PID(FOPID)controllers.FOPID controllers,with additional integral and derivative orders compared to traditional PID controllers,possess enhanced capabilities in handling complex systems.However,effective tuning of its five parameters is challenging.To address this,multiple intelligent algorithms are investigated.The improved sparrow search algorithm(ISSA)utilizes Chebyshev chaotic mapping initialization,adaptive t-distribution,and the firefly algorithm to overcome the limitations of the basic algorithm,showing high accuracy,speed,and robustness in multi-modal problems.The grey wolf optimizer(GWO),inspired by the hunting behavior of grey wolves,has procedures for encircling,hunting,and attacking but may encounter local optima,and several improvement methods have been proposed.The genetic algorithm,based on the survival of the fittest principle,involves encoding,decoding,and other operations.Taking vehicle ABS control as an example,the genetic algorithm-based FOPID controller outperforms the traditional PID controller.In conclusion,different algorithms have their own advantages in FOPID parameter tuning,and the selection depends on system characteristics and control requirements.Future research can focus on further algorithm improvement and hybrid methods to achieve better control performance,providing a valuable reference for FOPID applications in industry.展开更多
Depression is one of the most severe mental health illnesses among senior citizens.Aiming at the low accuracy and poor interpretability of traditional prediction models,a novel interpretable depression predictive mode...Depression is one of the most severe mental health illnesses among senior citizens.Aiming at the low accuracy and poor interpretability of traditional prediction models,a novel interpretable depression predictive model for the elderly based on the improved sparrow search algorithm(ISSA)optimized light gradient boosting machine(LightGBM)and Shapley Additive exPlainations(SHAP)is proposed.First of all,to achieve better optimization ability and convergence speed,various strategies are used to improve SSA,including initialization population by Halton sequence,generating elite population by reverse learning and multi-sample learning strategy with linear control of step size.Then,the ISSA is applied to optimize the hyper-parameters of light gradient boosting machine(LightGBM)to improve the prediction accuracy when facing massive high-dimensional data.Finally,SHAP is used to provide global and local interpretation of the prediction model.The effectiveness of the proposed method is validated by a series of comparative experiments based on a real-world dataset.展开更多
Battery life prediction is of great significance to the safe operation,and reduces the maintenance costs.This paper proposes a hybrid framework considering feature extraction to achieve more accurate and stable life p...Battery life prediction is of great significance to the safe operation,and reduces the maintenance costs.This paper proposes a hybrid framework considering feature extraction to achieve more accurate and stable life prediction performance of the battery.By feature extraction,eight features are obtained to fed into the life prediction model.The hybrid framework combines variational mode decomposition,the multi-kernel support vector regression model and the improved sparrow search algorithm to solve the problem of data backward,uneven distribution of high-dimensional feature space and the local escape ability,respectively.Better parameters of the estimation model are obtained by introducing the elite chaotic opposition-learning strategy and adaptive weights to optimize the sparrow search algorithm.The algorithm can improve the local escape ability and convergence performance and find the global optimum.The comparison is conducted by dataset from National Aeronautics and Space Administration which shows that the proposed framework has a more accurate and stable prediction performance.Compared with other algorithms,the SOH estimation accuracy of the proposed algorithm is improved by 0.16%–1.67%.With the advance of the start point,the RUL prediction accuracy of the proposed algorithm does not change much.展开更多
The existing traditional dam seepage pressure prediction models have problems such as falling into local optimum.The sparrow search algorithm(SSA)was improved as ISSA using both methods of nonlinear Sine Cosine optimi...The existing traditional dam seepage pressure prediction models have problems such as falling into local optimum.The sparrow search algorithm(SSA)was improved as ISSA using both methods of nonlinear Sine Cosine optimization algorithm and adaptive producer and scrounger ratio.We combined the Bidirectional Long Short-Term Memory(BiLSTM)neural network model with ISSA to develop the ISSA-BiLSTM seepage pressure prediction model.The critical feature factors were extracted based on LightGBM to construct the input layer for seepage pressure prediction.The results show that the ISSA-BiLSTM model's fitting outcomes are generally consistent with the observed changes in seepage pressure observations,achieving an R^(2) of 0.987.In comparison to SSABiLSTM and BiLSTM,the model exhibits a substantial reduction in errors,decreasing by approximately 20%and 30%,respectively.This model can provide technical support and insights for accurately predicting dam seepage,contributing to the advancement of this field.展开更多
文摘In the classification problem,deep kernel extreme learning machine(DKELM)has the characteristics of efficient processing and superior performance,but its parameters optimization is difficult.To improve the classification accuracy of DKELM,a DKELM algorithm optimized by the improved sparrow search algorithm(ISSA),named as ISSA-DKELM,is proposed in this paper.Aiming at the parameter selection problem of DKELM,the DKELM classifier is constructed by using the optimal parameters obtained by ISSA optimization.In order to make up for the shortcomings of the basic sparrow search algorithm(SSA),the chaotic transformation is first applied to initialize the sparrow position.Then,the position of the discoverer sparrow population is dynamically adjusted.A learning operator in the teaching-learning-based algorithm is fused to improve the position update operation of the joiners.Finally,the Gaussian mutation strategy is added in the later iteration of the algorithm to make the sparrow jump out of local optimum.The experimental results show that the proposed DKELM classifier is feasible and effective,and compared with other classification algorithms,the proposed DKELM algorithm aciheves better test accuracy.
基金This work was financially supported by the National Natural Science Foundation of China(Grant No.51575528)the Science Foundation of China University of Petroleum,Beijing(No.2462022QEDX011).
文摘Pipeline isolation plugging robot (PIPR) is an important tool in pipeline maintenance operation. During the plugging process, the violent vibration will occur by the flow field, which can cause serious damage to the pipeline and PIPR. In this paper, we propose a dynamic regulating strategy to reduce the plugging-induced vibration by regulating the spoiler angle and plugging velocity. Firstly, the dynamic plugging simulation and experiment are performed to study the flow field changes during dynamic plugging. And the pressure difference is proposed to evaluate the degree of flow field vibration. Secondly, the mathematical models of pressure difference with plugging states and spoiler angles are established based on the extreme learning machine (ELM) optimized by improved sparrow search algorithm (ISSA). Finally, a modified Q-learning algorithm based on simulated annealing is applied to determine the optimal strategy for the spoiler angle and plugging velocity in real time. The results show that the proposed method can reduce the plugging-induced vibration by 19.9% and 32.7% on average, compared with single-regulating methods. This study can effectively ensure the stability of the plugging process.
文摘This paper delves into the parameter tuning of fractional-order PID(FOPID)controllers.FOPID controllers,with additional integral and derivative orders compared to traditional PID controllers,possess enhanced capabilities in handling complex systems.However,effective tuning of its five parameters is challenging.To address this,multiple intelligent algorithms are investigated.The improved sparrow search algorithm(ISSA)utilizes Chebyshev chaotic mapping initialization,adaptive t-distribution,and the firefly algorithm to overcome the limitations of the basic algorithm,showing high accuracy,speed,and robustness in multi-modal problems.The grey wolf optimizer(GWO),inspired by the hunting behavior of grey wolves,has procedures for encircling,hunting,and attacking but may encounter local optima,and several improvement methods have been proposed.The genetic algorithm,based on the survival of the fittest principle,involves encoding,decoding,and other operations.Taking vehicle ABS control as an example,the genetic algorithm-based FOPID controller outperforms the traditional PID controller.In conclusion,different algorithms have their own advantages in FOPID parameter tuning,and the selection depends on system characteristics and control requirements.Future research can focus on further algorithm improvement and hybrid methods to achieve better control performance,providing a valuable reference for FOPID applications in industry.
基金supported by the National Natural Science Foundation of China(Nos.62172287,62102273)。
文摘Depression is one of the most severe mental health illnesses among senior citizens.Aiming at the low accuracy and poor interpretability of traditional prediction models,a novel interpretable depression predictive model for the elderly based on the improved sparrow search algorithm(ISSA)optimized light gradient boosting machine(LightGBM)and Shapley Additive exPlainations(SHAP)is proposed.First of all,to achieve better optimization ability and convergence speed,various strategies are used to improve SSA,including initialization population by Halton sequence,generating elite population by reverse learning and multi-sample learning strategy with linear control of step size.Then,the ISSA is applied to optimize the hyper-parameters of light gradient boosting machine(LightGBM)to improve the prediction accuracy when facing massive high-dimensional data.Finally,SHAP is used to provide global and local interpretation of the prediction model.The effectiveness of the proposed method is validated by a series of comparative experiments based on a real-world dataset.
基金This work was supported by the National Natural Science Foundation of China(Grant number 51577046)the State Key Program of the National Natural Science Foundation of China(Grant number 51637004)the National Key Research and Development Plan“Important Scientific Instruments and Equipment Development”(Grant number 2016YFF0102200).
文摘Battery life prediction is of great significance to the safe operation,and reduces the maintenance costs.This paper proposes a hybrid framework considering feature extraction to achieve more accurate and stable life prediction performance of the battery.By feature extraction,eight features are obtained to fed into the life prediction model.The hybrid framework combines variational mode decomposition,the multi-kernel support vector regression model and the improved sparrow search algorithm to solve the problem of data backward,uneven distribution of high-dimensional feature space and the local escape ability,respectively.Better parameters of the estimation model are obtained by introducing the elite chaotic opposition-learning strategy and adaptive weights to optimize the sparrow search algorithm.The algorithm can improve the local escape ability and convergence performance and find the global optimum.The comparison is conducted by dataset from National Aeronautics and Space Administration which shows that the proposed framework has a more accurate and stable prediction performance.Compared with other algorithms,the SOH estimation accuracy of the proposed algorithm is improved by 0.16%–1.67%.With the advance of the start point,the RUL prediction accuracy of the proposed algorithm does not change much.
基金supported by the North China University of Water Resources and Electric Powersupport of The National Natural Science Foundation of China(51979169)Henan Province Innovation Talent Support Plan(24HASTIT017).
文摘The existing traditional dam seepage pressure prediction models have problems such as falling into local optimum.The sparrow search algorithm(SSA)was improved as ISSA using both methods of nonlinear Sine Cosine optimization algorithm and adaptive producer and scrounger ratio.We combined the Bidirectional Long Short-Term Memory(BiLSTM)neural network model with ISSA to develop the ISSA-BiLSTM seepage pressure prediction model.The critical feature factors were extracted based on LightGBM to construct the input layer for seepage pressure prediction.The results show that the ISSA-BiLSTM model's fitting outcomes are generally consistent with the observed changes in seepage pressure observations,achieving an R^(2) of 0.987.In comparison to SSABiLSTM and BiLSTM,the model exhibits a substantial reduction in errors,decreasing by approximately 20%and 30%,respectively.This model can provide technical support and insights for accurately predicting dam seepage,contributing to the advancement of this field.
