Pub Date : 2022-07-15DOI: 10.1109/icaci55529.2022.9837669
Fei Wei, Guici Chen, Lei Yu
This paper investigates the finite-time anti-synchronization problem for a class of memristor oscillation circuit systems. Firstly, a 4th order memristor chaotic circuit system is derived using a quadratic nonlinear activated magneto-controlled memristor instead of the Chua diode in the Chua circuit. Then, a suitable controller is designed utilizing Lyapunov stability theory to drive the drive-response systems to finite-time anti-synchronization. Furthermore, the derived synchronization criterion is related to the system parameters. Therefore, the results obtained are more general and extend previous work. Finally, a numerical example is given and simulated to verify the validity of the results obtained.
{"title":"Finite-time Anti-synchronization of Memristor Oscillation System","authors":"Fei Wei, Guici Chen, Lei Yu","doi":"10.1109/icaci55529.2022.9837669","DOIUrl":"https://doi.org/10.1109/icaci55529.2022.9837669","url":null,"abstract":"This paper investigates the finite-time anti-synchronization problem for a class of memristor oscillation circuit systems. Firstly, a 4th order memristor chaotic circuit system is derived using a quadratic nonlinear activated magneto-controlled memristor instead of the Chua diode in the Chua circuit. Then, a suitable controller is designed utilizing Lyapunov stability theory to drive the drive-response systems to finite-time anti-synchronization. Furthermore, the derived synchronization criterion is related to the system parameters. Therefore, the results obtained are more general and extend previous work. Finally, a numerical example is given and simulated to verify the validity of the results obtained.","PeriodicalId":412347,"journal":{"name":"2022 14th International Conference on Advanced Computational Intelligence (ICACI)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121718719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper is about a research in applying different neural networks for diabetic-retinopathy-detection. Respectively using basic CNNs, VGG16 and GoogLeNet trained on datasets from Aravind Eye Hospital in India including 8929 photos and validated on other 1114 photos. Experiment showed that GoogLeNet model could better identify diabetic retinopathy with a higher train accuracy around 97%, compared to the CNN model’s performance of 84% and VGG16’s 94%. Meanwhile, the test accuracy of GoogLeNet is 85%, relatively higher than other proposed models. The excellent performance of the GoogLeNet model shows its great potential and promises to be extended to replace ophthalmologists in the screening of patients in the future.
{"title":"GoogLeNet-based Diabetic-retinopathy-detection","authors":"Bojia Shi, Xiaoya Zhang, Zhuoyang Wang, Jiawei Song, Jiaxuan Han, Zaiye Zhang, Teoh Teik Toe","doi":"10.1109/icaci55529.2022.9837677","DOIUrl":"https://doi.org/10.1109/icaci55529.2022.9837677","url":null,"abstract":"This paper is about a research in applying different neural networks for diabetic-retinopathy-detection. Respectively using basic CNNs, VGG16 and GoogLeNet trained on datasets from Aravind Eye Hospital in India including 8929 photos and validated on other 1114 photos. Experiment showed that GoogLeNet model could better identify diabetic retinopathy with a higher train accuracy around 97%, compared to the CNN model’s performance of 84% and VGG16’s 94%. Meanwhile, the test accuracy of GoogLeNet is 85%, relatively higher than other proposed models. The excellent performance of the GoogLeNet model shows its great potential and promises to be extended to replace ophthalmologists in the screening of patients in the future.","PeriodicalId":412347,"journal":{"name":"2022 14th International Conference on Advanced Computational Intelligence (ICACI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128400806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-15DOI: 10.1109/icaci55529.2022.9837674
Hanwen Liu, Bingrong Xu, Yin Sheng, Zhigang Zeng
An interesting property of deep convolutional neural networks is their weakness to adversarial examples, which can deceive the models with subtle perturbations. Though adversarial attack algorithms have accomplished excellent performance in the white-box scenario, they frequently display a low attack success rate in the black-box scenario. Various transformation-based attack methods are shown to be powerful to enhance the transferability of adversarial examples. In this work, several novel transformation-based attack methods that integrate with the Random Block Shuffle (RBS) and Ensemble Random Block Shuffle (ERBS) mechanisms are come up with to boost adversarial transferability. First of all, the RBS calculates the gradient of the shuffled input instead of the original input. It increases the diversity of adversarial perturbation’s gradient and makes the original input’s information more invisible for the model. Based on the RBS, the ERBS is proposed to decrease gradient variance and stabilize the update direction further, which integrates the gradient of transformed inputs. Moreover, by incorporating various gradient-based attack methods with transformation-based methods, the adversarial transferability could be additionally improved fundamentally and relieve the overfitting problem. Our best attack method arrives an average success rate of 85.5% on two normally trained models and two adversarially trained models, which outperforms existing baselines.
{"title":"Boosting Adversarial Attack Transferability via Random Block Shuffle","authors":"Hanwen Liu, Bingrong Xu, Yin Sheng, Zhigang Zeng","doi":"10.1109/icaci55529.2022.9837674","DOIUrl":"https://doi.org/10.1109/icaci55529.2022.9837674","url":null,"abstract":"An interesting property of deep convolutional neural networks is their weakness to adversarial examples, which can deceive the models with subtle perturbations. Though adversarial attack algorithms have accomplished excellent performance in the white-box scenario, they frequently display a low attack success rate in the black-box scenario. Various transformation-based attack methods are shown to be powerful to enhance the transferability of adversarial examples. In this work, several novel transformation-based attack methods that integrate with the Random Block Shuffle (RBS) and Ensemble Random Block Shuffle (ERBS) mechanisms are come up with to boost adversarial transferability. First of all, the RBS calculates the gradient of the shuffled input instead of the original input. It increases the diversity of adversarial perturbation’s gradient and makes the original input’s information more invisible for the model. Based on the RBS, the ERBS is proposed to decrease gradient variance and stabilize the update direction further, which integrates the gradient of transformed inputs. Moreover, by incorporating various gradient-based attack methods with transformation-based methods, the adversarial transferability could be additionally improved fundamentally and relieve the overfitting problem. Our best attack method arrives an average success rate of 85.5% on two normally trained models and two adversarially trained models, which outperforms existing baselines.","PeriodicalId":412347,"journal":{"name":"2022 14th International Conference on Advanced Computational Intelligence (ICACI)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129441258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-15DOI: 10.1109/icaci55529.2022.9837581
Zhansong Ma, Bingrong Xu, Lei Wang, Hanwen Liu, Zhigang Zeng
Unsupervised domain adaptation (UDA) recognizes unlabeled domain data by using the classifier learned from another domain. Previous works mainly focus on domain-level alignment that usually ignores the class-level information, resulting in the samples of different classes being too close to be classified correctly. To tackle this challenge, we design a unified weighted maximum mean discrepancy (MMD) metric method, that measures the differences in empirical distributions of two domains by calculating the weights of different sample pairs adaptively. The unified weighted MMD method is proposed which combines the class-level alignment with domain-level alignment, making full use of intra-domain, inter-domain, intra-class, and inter-class information with adaptive weights, and it is easy to implement. Experiment results demonstrate that our method can obtain superior results from two standard UDA datasets Office-31 and ImageCLEF-DA, compared with other UDA approaches.
{"title":"A Unified Weighted MMD For Unsupervised Domain Adaptation","authors":"Zhansong Ma, Bingrong Xu, Lei Wang, Hanwen Liu, Zhigang Zeng","doi":"10.1109/icaci55529.2022.9837581","DOIUrl":"https://doi.org/10.1109/icaci55529.2022.9837581","url":null,"abstract":"Unsupervised domain adaptation (UDA) recognizes unlabeled domain data by using the classifier learned from another domain. Previous works mainly focus on domain-level alignment that usually ignores the class-level information, resulting in the samples of different classes being too close to be classified correctly. To tackle this challenge, we design a unified weighted maximum mean discrepancy (MMD) metric method, that measures the differences in empirical distributions of two domains by calculating the weights of different sample pairs adaptively. The unified weighted MMD method is proposed which combines the class-level alignment with domain-level alignment, making full use of intra-domain, inter-domain, intra-class, and inter-class information with adaptive weights, and it is easy to implement. Experiment results demonstrate that our method can obtain superior results from two standard UDA datasets Office-31 and ImageCLEF-DA, compared with other UDA approaches.","PeriodicalId":412347,"journal":{"name":"2022 14th International Conference on Advanced Computational Intelligence (ICACI)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122457655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jaya optimization algorithm is a simple but powerful intelligence optimization method which has several outstanding characteristics of both population-based algorithms and swarm intelligence-based algorithms. It has shown great potentials to solve various hard and complex optimization problems, but there still has much room to improve its performance, especially for solving high-dimensional and non-convex problems. Hence, this paper proposes an improved Jaya optimization algorithm with a novel hybrid logistic-sine-cosine chaotic map, which is named IJaya for short. The hybrid logisticsine-cosine chaotic map is applied to balance the exploration and the exploitation processes of Jaya optimization algorithm. Seven benchmark testing functions with different scale settings are used to evaluate the performance of our improved algorithm. Computational results indicate that our improved Jaya optimization algorithm outperforms greatly its original version on most testing functions with high-dimensions.
{"title":"An Improved Jaya Optimization Algorithm with Hybrid Logistic-Sine-Cosine Chaotic Map","authors":"Weidong Lei, Zhan Zhang, Jiawei Zhu, Yishuai Lin, Jing Hou, Ying Sun","doi":"10.1109/icaci55529.2022.9837758","DOIUrl":"https://doi.org/10.1109/icaci55529.2022.9837758","url":null,"abstract":"Jaya optimization algorithm is a simple but powerful intelligence optimization method which has several outstanding characteristics of both population-based algorithms and swarm intelligence-based algorithms. It has shown great potentials to solve various hard and complex optimization problems, but there still has much room to improve its performance, especially for solving high-dimensional and non-convex problems. Hence, this paper proposes an improved Jaya optimization algorithm with a novel hybrid logistic-sine-cosine chaotic map, which is named IJaya for short. The hybrid logisticsine-cosine chaotic map is applied to balance the exploration and the exploitation processes of Jaya optimization algorithm. Seven benchmark testing functions with different scale settings are used to evaluate the performance of our improved algorithm. Computational results indicate that our improved Jaya optimization algorithm outperforms greatly its original version on most testing functions with high-dimensions.","PeriodicalId":412347,"journal":{"name":"2022 14th International Conference on Advanced Computational Intelligence (ICACI)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133060124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-15DOI: 10.1109/icaci55529.2022.9837776
Zhongwen Wu, Fanghai Zhang
This article investigates multiple O(t-α) stability of fractional-order switched neural networks(FOSNNs) with time-varying delays. Under the framework of Filippov solution and geometric properties of activation function, some new results are established to ascertain the existence of equilibria. Besides, FOSNNs have more stable equilibria, which reveals that the effect of switching threshold in fractional-order neural networks. One example is provided to illustrate the effectiveness of the theoretical results.
{"title":"Multiple O(t-α) Stability of Fractional-order Switched Neural Networks with Time-varying Delays","authors":"Zhongwen Wu, Fanghai Zhang","doi":"10.1109/icaci55529.2022.9837776","DOIUrl":"https://doi.org/10.1109/icaci55529.2022.9837776","url":null,"abstract":"This article investigates multiple O(t-α) stability of fractional-order switched neural networks(FOSNNs) with time-varying delays. Under the framework of Filippov solution and geometric properties of activation function, some new results are established to ascertain the existence of equilibria. Besides, FOSNNs have more stable equilibria, which reveals that the effect of switching threshold in fractional-order neural networks. One example is provided to illustrate the effectiveness of the theoretical results.","PeriodicalId":412347,"journal":{"name":"2022 14th International Conference on Advanced Computational Intelligence (ICACI)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127962385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-15DOI: 10.1109/icaci55529.2022.9837664
Pengyong Wang, Feng Mao, Zhiheng Li
Intelligent traffic signal control plays a crucial role in alleviating traffic congestion. With increasingly available traffic data, there is a trend to use deep reinforcement learning (DRL) techniques for intelligent traffic signal control. However, a majority of existing DRL methods are based on Q-learning, where the optimal solution is always a deterministic policy, so they may fail to adapt to heterogeneous traffic flow and different environment settings. In this paper, we propose a method called SoftLight based on maximum entropy DRL. Through the regularization of maximum entropy, our method learns a stochastic policy that significantly reduces the queue length at the intersection. At the same time, our method keeps the policy as random as possible, which achieves better adaptability to heterogeneous traffic flow. By conducting comprehensive experiments, we demonstrate that our method outperforms existing DRL methods in both phase selection and phase shift settings. We also compare our method with the prevalent maximum entropy DRL method, soft actor-critic (SAC). The results show that our method can find better solutions than SAC under different model designs and hyper-parameters.
{"title":"SoftLight: A Maximum Entropy Deep Reinforcement Learning Approach for Intelligent Traffic Signal Control","authors":"Pengyong Wang, Feng Mao, Zhiheng Li","doi":"10.1109/icaci55529.2022.9837664","DOIUrl":"https://doi.org/10.1109/icaci55529.2022.9837664","url":null,"abstract":"Intelligent traffic signal control plays a crucial role in alleviating traffic congestion. With increasingly available traffic data, there is a trend to use deep reinforcement learning (DRL) techniques for intelligent traffic signal control. However, a majority of existing DRL methods are based on Q-learning, where the optimal solution is always a deterministic policy, so they may fail to adapt to heterogeneous traffic flow and different environment settings. In this paper, we propose a method called SoftLight based on maximum entropy DRL. Through the regularization of maximum entropy, our method learns a stochastic policy that significantly reduces the queue length at the intersection. At the same time, our method keeps the policy as random as possible, which achieves better adaptability to heterogeneous traffic flow. By conducting comprehensive experiments, we demonstrate that our method outperforms existing DRL methods in both phase selection and phase shift settings. We also compare our method with the prevalent maximum entropy DRL method, soft actor-critic (SAC). The results show that our method can find better solutions than SAC under different model designs and hyper-parameters.","PeriodicalId":412347,"journal":{"name":"2022 14th International Conference on Advanced Computational Intelligence (ICACI)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125603880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-15DOI: 10.1109/icaci55529.2022.9837498
J. Jia, Yuchi Cao, Tie-shan Li, Jiakun Xu, Xiuxian Yang
The Differential of Log-Sum-Exp $(DLSE_{T})$ neural network (NN) is combined with model predictive control (MPC) to perform course tracking control based on data. In the past, classical MPC was used to track a given ship reference course, but the ship model should be precisely known, and the cost of MPC online optimization calculation was high. To tackle these problems data driven DLSET NN is used in this paper to approximate the cost functionals based on course data. Off-line neural network training, and DLSET characteristics can reduce the cost of online optimization, and MPC can ensure that the rudder angle constraint is satisfied. According to the simulation results, the DLSET-based MPC is feasible in ship course tracking control.
{"title":"Ship Course Tracking Control Using Differential of Log-Sum-Exp Neural Network and Model Predictive Control","authors":"J. Jia, Yuchi Cao, Tie-shan Li, Jiakun Xu, Xiuxian Yang","doi":"10.1109/icaci55529.2022.9837498","DOIUrl":"https://doi.org/10.1109/icaci55529.2022.9837498","url":null,"abstract":"The Differential of Log-Sum-Exp $(DLSE_{T})$ neural network (NN) is combined with model predictive control (MPC) to perform course tracking control based on data. In the past, classical MPC was used to track a given ship reference course, but the ship model should be precisely known, and the cost of MPC online optimization calculation was high. To tackle these problems data driven DLSET NN is used in this paper to approximate the cost functionals based on course data. Off-line neural network training, and DLSET characteristics can reduce the cost of online optimization, and MPC can ensure that the rudder angle constraint is satisfied. According to the simulation results, the DLSET-based MPC is feasible in ship course tracking control.","PeriodicalId":412347,"journal":{"name":"2022 14th International Conference on Advanced Computational Intelligence (ICACI)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125117735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-15DOI: 10.1109/icaci55529.2022.9837495
Changyuan Wang, Xiao Liu, Yunong Zhang
It is very common and vital to solve linear equation system (LES) in numerical fields. Generally, LES problems mainly include two types, i.e., the time-dependent LES problem and the static (i.e., time-independent) LES problem. With the rapid development of artificial intelligence, neural network has rich application scenes in many fields. For example, Zhang neural net (ZNN) is an effective neural network when solving time-dependent problems. In this paper, we present a special ZNN model termed elegant-formula ZNN (EFZNN) model. In addition, the specific EFZNN model has close relation with the traditional algorithm, i.e., Jacobi iteration (JI) algorithm, after ingenious construction and discretization by Euler forward discretization formula. Especially, when we fix the step-size in the discretization EFZNN algorithm as 1, it is the same as the JI algorithm. Besides, the ZNN and EFZNN models including the corresponding discretization algorithms for solving the LES are introduced, and the feasibility and efficiency of them in solving the LES are verified by, more importantly, computer numerical experiments, being the main merit of the paper.
{"title":"Computer Numerical Experiment Results of Zhang Neural Net Connected to Jacobi Iteration Algorithm for Static Linear Equation System Solving","authors":"Changyuan Wang, Xiao Liu, Yunong Zhang","doi":"10.1109/icaci55529.2022.9837495","DOIUrl":"https://doi.org/10.1109/icaci55529.2022.9837495","url":null,"abstract":"It is very common and vital to solve linear equation system (LES) in numerical fields. Generally, LES problems mainly include two types, i.e., the time-dependent LES problem and the static (i.e., time-independent) LES problem. With the rapid development of artificial intelligence, neural network has rich application scenes in many fields. For example, Zhang neural net (ZNN) is an effective neural network when solving time-dependent problems. In this paper, we present a special ZNN model termed elegant-formula ZNN (EFZNN) model. In addition, the specific EFZNN model has close relation with the traditional algorithm, i.e., Jacobi iteration (JI) algorithm, after ingenious construction and discretization by Euler forward discretization formula. Especially, when we fix the step-size in the discretization EFZNN algorithm as 1, it is the same as the JI algorithm. Besides, the ZNN and EFZNN models including the corresponding discretization algorithms for solving the LES are introduced, and the feasibility and efficiency of them in solving the LES are verified by, more importantly, computer numerical experiments, being the main merit of the paper.","PeriodicalId":412347,"journal":{"name":"2022 14th International Conference on Advanced Computational Intelligence (ICACI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124439014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-15DOI: 10.1109/icaci55529.2022.9837506
Wen Wei, Pengcheng Liao, Jinzhan Xie, Bo Yu
This paper takes hybrid vehicles as the research object, research on the fuel economy of hybrid vehicles and their control strategies, train the data collected by the hybrid vehicle through the ANFIS toolbox, Takagi-Sugeno fuzzy inference algorithm was established, Fuzzy inference rules for torque allocation are generated. The optimized model of the ANFIS algorithm is imported into the vehicle model for simulation. Compared with the control strategy based on logic threshold, the simulation results show that the torque distribution of hybrid vehicles can be reasonably performed by ANFIS, and the hybrid vehicles optimized based on ANFIS algorithm can significantly improve the fuel economy of the whole vehicle, which verifies the effectiveness and practicability of the proposed control strategy.
{"title":"Research on Control Strategy of Hybrid Vehicle Based on ANFIS","authors":"Wen Wei, Pengcheng Liao, Jinzhan Xie, Bo Yu","doi":"10.1109/icaci55529.2022.9837506","DOIUrl":"https://doi.org/10.1109/icaci55529.2022.9837506","url":null,"abstract":"This paper takes hybrid vehicles as the research object, research on the fuel economy of hybrid vehicles and their control strategies, train the data collected by the hybrid vehicle through the ANFIS toolbox, Takagi-Sugeno fuzzy inference algorithm was established, Fuzzy inference rules for torque allocation are generated. The optimized model of the ANFIS algorithm is imported into the vehicle model for simulation. Compared with the control strategy based on logic threshold, the simulation results show that the torque distribution of hybrid vehicles can be reasonably performed by ANFIS, and the hybrid vehicles optimized based on ANFIS algorithm can significantly improve the fuel economy of the whole vehicle, which verifies the effectiveness and practicability of the proposed control strategy.","PeriodicalId":412347,"journal":{"name":"2022 14th International Conference on Advanced Computational Intelligence (ICACI)","volume":"313 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124452487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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