Water quality is always one of the most important factors in human health. Artificial intelligence models are respected methods for modeling water quality. The evolutionary algorithm (EA) is a new technique for improv...Water quality is always one of the most important factors in human health. Artificial intelligence models are respected methods for modeling water quality. The evolutionary algorithm (EA) is a new technique for improving the performance of artificial intelligence models such as the adaptive neuro fuzzy inference system (ANFIS) and artificial neural networks (ANN). Attempts have been made to make the models more suitable and accurate with the replacement of other training methods that do not suffer from some shortcomings, including a tendency to being trapped in local optima or voluminous computations. This study investigated the applicability of ANFIS with particle swarm optimization (PSO) and ant colony optimization for continuous domains (ACOR) in estimating water quality parameters at three stations along the Zayandehrood River, in Iran. The ANFIS-PSO and ANFIS-ACOR methods were also compared with the classic ANFIS method, which uses least squares and gradient descent as training algorithms. The estimated water quality parameters in this study were electrical conductivity (EC), total dissolved solids (TDS), the sodium adsorption ratio (SAR), carbonate hardness (CH), and total hardness (TH). Correlation analysis was performed using SPSS software to determine the optimal inputs to the models. The analysis showed that ANFIS-PSO was the better model compared with ANFIS-ACOR. It is noteworthy that EA models can improve ANFIS' performance at all three stations for different water quality parameters.展开更多
Recently,as recognizing emotion has been one of the hallmarks of affective computing,more attention has been paid to physiological signals for emotion recognition.This paper presented an approach to emotion recognitio...Recently,as recognizing emotion has been one of the hallmarks of affective computing,more attention has been paid to physiological signals for emotion recognition.This paper presented an approach to emotion recognition using ElectroCardioGraphy(ECG) signals from multiple subjects.To collect reliable affective ECG data,we applied an arousal method by movie clips to make subjects experience specific emotions without external interference.Through precise location of P-QRS-T wave by continuous wavelet transform,an amount of ECG features was extracted sufficiently.Since feature selection is a combination optimization problem,Improved Binary Particle Swarm Optimization(IBPSO) based on neighborhood search was applied to search out effective features to improve classification results of emotion states with the help of fisher or K-Nearest Neighbor(KNN) classifier.In the experiment,it is shown that the approach is successful and the effective features got from ECG signals can express emotion states excellently.展开更多
The Indian Regional Navigation Satellite System provides accurate positioning service to the users within and around India,extending up to 1500 km.However,when a receiver encounters a Continuous Wave Interference,its ...The Indian Regional Navigation Satellite System provides accurate positioning service to the users within and around India,extending up to 1500 km.However,when a receiver encounters a Continuous Wave Interference,its positioning accuracy degrades,or sometimes it even fails to work.Wavelet Packet Transform(WPT)is the most widely used technique for anti-jamming in Global Navigation Satellite System receivers.But the conventional method suffers from threshold drifting and employs inflexible thresholding functions.So,to address these issues,an efficient approach using Improved Particle Swarm Optimization based Parametric Wavelet Packet Thresholding(IPSO-PWPT)is proposed.Firstly,a new parameter adaptive thresholding function is constructed.Then,a new form of inertia weight is presented to enhance the performance of PSO.Later,IPSO is used to optimize the key parameters of WPT.Finally,the implementation of the IPSO-PWPT anti-jamming algorithm is discussed.The performance of the proposed technique is evaluated for various performance metrics in four jamming environments.The evaluation results manifest the proposed method’s efficacy compared to the conventional WPT in terms of anti-jamming capability.Also,the results show the ability of the new thresholding function to process various signals effectively.Furthermore,the findings reveal that the improved PSO outperforms the variants of PSO.展开更多
Mechanical property prediction of hot rolled strip is one of the hotspots in material processing research. To avoid the local infinitesimal defect and slow constringency in pure BP algorithm, a kind of global optimiza...Mechanical property prediction of hot rolled strip is one of the hotspots in material processing research. To avoid the local infinitesimal defect and slow constringency in pure BP algorithm, a kind of global optimization algorithm-particle swarm optimization （PSO） is adopted. The algorithm is combined with the BP rapid training algorithm, and then, a kind of new neural network （NN） called PSO-BP NN is established. With the advantages of global optimization ability and the rapid constringency of the BP rapid training algorithm, the new algorithm fully shows the ability of nonlinear approach of multilayer feedforward network, improves the performance of NN, and provides a favorable basis for further online application of a comprehensive model.展开更多
文摘Water quality is always one of the most important factors in human health. Artificial intelligence models are respected methods for modeling water quality. The evolutionary algorithm (EA) is a new technique for improving the performance of artificial intelligence models such as the adaptive neuro fuzzy inference system (ANFIS) and artificial neural networks (ANN). Attempts have been made to make the models more suitable and accurate with the replacement of other training methods that do not suffer from some shortcomings, including a tendency to being trapped in local optima or voluminous computations. This study investigated the applicability of ANFIS with particle swarm optimization (PSO) and ant colony optimization for continuous domains (ACOR) in estimating water quality parameters at three stations along the Zayandehrood River, in Iran. The ANFIS-PSO and ANFIS-ACOR methods were also compared with the classic ANFIS method, which uses least squares and gradient descent as training algorithms. The estimated water quality parameters in this study were electrical conductivity (EC), total dissolved solids (TDS), the sodium adsorption ratio (SAR), carbonate hardness (CH), and total hardness (TH). Correlation analysis was performed using SPSS software to determine the optimal inputs to the models. The analysis showed that ANFIS-PSO was the better model compared with ANFIS-ACOR. It is noteworthy that EA models can improve ANFIS' performance at all three stations for different water quality parameters.
基金Supported by the National Natural Science Foundation of China (No.60873143)the National Key Subject Foundation for Basic Psychology (No.NKSF07003)
文摘Recently,as recognizing emotion has been one of the hallmarks of affective computing,more attention has been paid to physiological signals for emotion recognition.This paper presented an approach to emotion recognition using ElectroCardioGraphy(ECG) signals from multiple subjects.To collect reliable affective ECG data,we applied an arousal method by movie clips to make subjects experience specific emotions without external interference.Through precise location of P-QRS-T wave by continuous wavelet transform,an amount of ECG features was extracted sufficiently.Since feature selection is a combination optimization problem,Improved Binary Particle Swarm Optimization(IBPSO) based on neighborhood search was applied to search out effective features to improve classification results of emotion states with the help of fisher or K-Nearest Neighbor(KNN) classifier.In the experiment,it is shown that the approach is successful and the effective features got from ECG signals can express emotion states excellently.
文摘The Indian Regional Navigation Satellite System provides accurate positioning service to the users within and around India,extending up to 1500 km.However,when a receiver encounters a Continuous Wave Interference,its positioning accuracy degrades,or sometimes it even fails to work.Wavelet Packet Transform(WPT)is the most widely used technique for anti-jamming in Global Navigation Satellite System receivers.But the conventional method suffers from threshold drifting and employs inflexible thresholding functions.So,to address these issues,an efficient approach using Improved Particle Swarm Optimization based Parametric Wavelet Packet Thresholding(IPSO-PWPT)is proposed.Firstly,a new parameter adaptive thresholding function is constructed.Then,a new form of inertia weight is presented to enhance the performance of PSO.Later,IPSO is used to optimize the key parameters of WPT.Finally,the implementation of the IPSO-PWPT anti-jamming algorithm is discussed.The performance of the proposed technique is evaluated for various performance metrics in four jamming environments.The evaluation results manifest the proposed method’s efficacy compared to the conventional WPT in terms of anti-jamming capability.Also,the results show the ability of the new thresholding function to process various signals effectively.Furthermore,the findings reveal that the improved PSO outperforms the variants of PSO.
基金Natural Science Foundation of Anhui Provincial Education Depart ment of China (2006KJ080A)
文摘Mechanical property prediction of hot rolled strip is one of the hotspots in material processing research. To avoid the local infinitesimal defect and slow constringency in pure BP algorithm, a kind of global optimization algorithm-particle swarm optimization （PSO） is adopted. The algorithm is combined with the BP rapid training algorithm, and then, a kind of new neural network （NN） called PSO-BP NN is established. With the advantages of global optimization ability and the rapid constringency of the BP rapid training algorithm, the new algorithm fully shows the ability of nonlinear approach of multilayer feedforward network, improves the performance of NN, and provides a favorable basis for further online application of a comprehensive model.
