Pub Date : 2022-07-15DOI: 10.1109/icaci55529.2022.9837580
Xinrui Jiang, Zhaorui Xin, Sitian Qin, Jiqiang Feng, Guocheng Li
This article discusses the problem of Nash equilibrium seeking for noncooperative game with equality constraints. In the problem, each player desires to maximize its nonsmooth payoff function which depends on both its own strategy and the strategy of other players. Besides, the game-player is subjected to private local equality constraints. We use a l1 penalty function to deal with the equality constraints and a Nash equilibrium seeking strategy is designed on the basis of differential inclusions and subgradient methods. And we show that the strategy of player is exponentially convergent to the Nash equilibrium with Lyapunov methods. Finally, a numerical example is presented to illustrate the validity of our theoretical results.
{"title":"Generalized Nash Equilibrium Seeking Strategy for Nonsmooth Noncooperative Game with Equality Constraints","authors":"Xinrui Jiang, Zhaorui Xin, Sitian Qin, Jiqiang Feng, Guocheng Li","doi":"10.1109/icaci55529.2022.9837580","DOIUrl":"https://doi.org/10.1109/icaci55529.2022.9837580","url":null,"abstract":"This article discusses the problem of Nash equilibrium seeking for noncooperative game with equality constraints. In the problem, each player desires to maximize its nonsmooth payoff function which depends on both its own strategy and the strategy of other players. Besides, the game-player is subjected to private local equality constraints. We use a l1 penalty function to deal with the equality constraints and a Nash equilibrium seeking strategy is designed on the basis of differential inclusions and subgradient methods. And we show that the strategy of player is exponentially convergent to the Nash equilibrium with Lyapunov methods. Finally, a numerical example is presented to illustrate the validity of our theoretical results.","PeriodicalId":412347,"journal":{"name":"2022 14th International Conference on Advanced Computational Intelligence (ICACI)","volume":"38 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":"116689304","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/icaci55529.2022.9837654
Yaqian Hu, Leimin Wang, Xingxing Tan, Kan Zeng
In this paper, the finite-time synchronization (FTS) for inertial neural networks (INNs) is investigated based on periodically intermittent control. By utilizing the reduced order approach, INN system is transformed into two first-order systems. Then, proper periodically intermittent controllers are designed to obtain sufficient condition for FTS of INNs. An example is proposed to support the validity of the synchronization criterion.
{"title":"Finite-time Synchronization of Inertial Neural Networks via Periodically Intermittent Control","authors":"Yaqian Hu, Leimin Wang, Xingxing Tan, Kan Zeng","doi":"10.1109/icaci55529.2022.9837654","DOIUrl":"https://doi.org/10.1109/icaci55529.2022.9837654","url":null,"abstract":"In this paper, the finite-time synchronization (FTS) for inertial neural networks (INNs) is investigated based on periodically intermittent control. By utilizing the reduced order approach, INN system is transformed into two first-order systems. Then, proper periodically intermittent controllers are designed to obtain sufficient condition for FTS of INNs. An example is proposed to support the validity of the synchronization criterion.","PeriodicalId":412347,"journal":{"name":"2022 14th International Conference on Advanced Computational Intelligence (ICACI)","volume":"168 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":"116909399","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/icaci55529.2022.9837537
X. Li, Mingxin Kang
The rapid development of vehicle-to-everything (V2X) and intelligent control technologies brings new opportunities and challenges to the traditional automotive control architecture. More driving information about traffic scenarios and ambient events such as the road slope, the traffic light timing is possible to be obtained via V2X system. And then, those traffic information will be extracted by individual vehicle’s controller and be further utilized to design the optimal control strategy. Fuel economy performance and time losses for waiting for the traffic red light are the two main concerns by most drivers. In order to obtain a satisfactory fuel economy performance and lower traveling time loss, this paper investigates an eco-driving problem for road vehicles when assuming the information of the traffic light ahead is prior known. The optimization problem by balancing the fuel consumption and time loss is designed and meanwhile the time phase of the traffic light is also considered. The optimization problem is firstly solved with the dynamic programming (DP) algorithm. Preliminary simulations have been implemented and the simulation results demonstrate the potential ability in improvement of the fuel economy performance. Moreover, an equivalent problem is formulated under the switching control system framework, to guarantee the hard constraint of the red light. The equivalent problem provides an interesting topic for the open discussion.
{"title":"An Investigation on Vehicle Fuel Consumption Optimization Strategy Based on Scenario Information","authors":"X. Li, Mingxin Kang","doi":"10.1109/icaci55529.2022.9837537","DOIUrl":"https://doi.org/10.1109/icaci55529.2022.9837537","url":null,"abstract":"The rapid development of vehicle-to-everything (V2X) and intelligent control technologies brings new opportunities and challenges to the traditional automotive control architecture. More driving information about traffic scenarios and ambient events such as the road slope, the traffic light timing is possible to be obtained via V2X system. And then, those traffic information will be extracted by individual vehicle’s controller and be further utilized to design the optimal control strategy. Fuel economy performance and time losses for waiting for the traffic red light are the two main concerns by most drivers. In order to obtain a satisfactory fuel economy performance and lower traveling time loss, this paper investigates an eco-driving problem for road vehicles when assuming the information of the traffic light ahead is prior known. The optimization problem by balancing the fuel consumption and time loss is designed and meanwhile the time phase of the traffic light is also considered. The optimization problem is firstly solved with the dynamic programming (DP) algorithm. Preliminary simulations have been implemented and the simulation results demonstrate the potential ability in improvement of the fuel economy performance. Moreover, an equivalent problem is formulated under the switching control system framework, to guarantee the hard constraint of the red light. The equivalent problem provides an interesting topic for the open discussion.","PeriodicalId":412347,"journal":{"name":"2022 14th International Conference on Advanced Computational Intelligence (ICACI)","volume":"115 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":"131511273","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}
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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