Article
Computer Science, Artificial Intelligence
Shixi Hou, Cheng Wang, Yundi Chu, Juntao Fei
Summary: Based on the global fast terminal sliding mode control, this article proposes a recurrent probabilistic compensation fuzzy neural network control scheme for handling nonlinear systems with uncertainties. The developed RPCFNN controller possesses superior nonlinearity handling capability and robustness.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Xiaolong Xu, Qinting Jiang, Peiming Zhang, Xuefei Cao, Mohammad R. Khosravi, Linss T. Alex, Lianyong Qi, Wanchun Dou
Summary: This article introduces an edge computing method for the Internet of vehicles (IoV) based on a fuzzy-task-offloading-and-resource-allocation scheme. The method predicts traffic flow, balances loads, and determines optimal task offloading strategies using game theory, and allocates computing resources for the offloaded tasks using the Q-learning algorithm. Comparative experiments validate the robust performance of this method.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Information Systems
Fengyu Gao, Jer-Guang Hsieh, Ying-Sheng Kuo, Jyh-Horng Jeng
Summary: Novel resistant hierarchical fuzzy neural networks are proposed in this study to model complex controlled plants and serve as fuzzy controllers. The least trimmed squared error is used as the cost function to enhance the resistance of learning machines. Real-world datasets are used to compare the performances of the proposed networks with and without noise.
Article
Computer Science, Artificial Intelligence
Hong-Gui Han, Chen-Yang Wang, Hao-Yuan Sun, Hong-Yan Yang, Jun-Fei Qiao
Summary: In this article, a fuzzy neural network-based iterative learning model predictive control (FNN-ILMPC) is designed for complex nonlinear systems. The controller considers the impact of external disturbances, effectively eliminates their influence, and ensures system stability.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Information Systems
Phu Pham, Loan T. T. Nguyen, Ngoc Thanh Nguyen, Robert Kozma, Bay Vo
Summary: The integration of deep learning and fuzzy learning is a promising research direction in data embedding. It helps improve the performance of latent feature representation learning and multiple recommendation problem fine-tuning. However, existing deep learning-based recommendation techniques face major challenges related to data uncertainty and noise.
INFORMATION SCIENCES
(2023)
Article
Agronomy
Jianlei Zhao, Jun Zhou, Chenyang Sun, Xu Wang, Zian Liang, Zezhong Qi
Summary: This study determined the working parameters and soil physical parameters of plowing using a designed electric suspension platform and soil instrument. By classifying the soil conditions into three physical states and constructing a T-S fuzzy neural network classifier, the model achieved real-time and accurate identification of the soil's physical state.
Article
Chemistry, Multidisciplinary
Chun-Jung Lin, Cheng-Jian Lin, Xue-Qian Lin
Summary: In this study, a Taguchi-based multiscale convolutional compensatory fuzzy neural network (T-MCCFNN) model is proposed for automatic detection and classification of sleep stages. The experimental results show that the proposed model achieves a sleep stage classification accuracy of 85.3%, which is superior to methods proposed by other scholars.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Interdisciplinary Applications
Yonghao Li, Yiqing Shen, Jiadong Zhang, Shujie Song, Zhenhui Li, Jing Ke, Dinggang Shen
Summary: A novel hierarchical Graph V-Net approach is proposed for breast cancer classification, which integrates patch-level pre-training and context-based fine-tuning using a hierarchical graph network. Experimental results demonstrate the superiority of our proposed method in classifying breast cancer.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2023)
Article
Computer Science, Artificial Intelligence
Huimin Lu, Ming Zhang, Xing Xu, Yujie Li, Heng Tao Shen
Summary: Our proposed deep fuzzy hashing network (DFHN) combines fuzzy logic technique and DNN to learn effective binary codes that leverage fuzzy rules to model data uncertainties. The generalized hamming distance derived from fuzzy logic theory is utilized in the convolutional and fully connected layers to model outputs, resulting in competitive retrieval accuracy and efficient training speed on large-scale image datasets: CIFAR-10 and NUS-WIDE, compared to state-of-the-art deep hashing approaches.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Tuan-Linh Nguyen, Swathi Kavuri, Soo-Yeon Park, Minho Lee
Summary: This paper proposes an attentive hierarchical adaptive neuro-fuzzy inference system (AH-ANFIS) that combines fuzzy inference and attention mechanism for predicting clinical outcomes. The system improves interpretability by decomposing the input space and selecting important features.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Yang Wang, Fuqian Yang, Jun Zhang, Huidong Wang, Xianwen Yue, Shanshan Liu
Summary: This paper constructs breast cancer CT image detection model and breast cancer screening model based on convolution and deconvolution neural network, using fuzzy C-means clustering algorithm to optimize breast cancer images. The new deep learning model improves automatic classification performance of breast cancer.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Honggui Han, Chenxuan Sun, Xiaolong Wu, Hongyan Yang, Junfei Qiao
Summary: This article proposes an FNN with a multiobjective optimization algorithm (MOO-FNN) to improve the generalization performance. It utilizes multilevel learning objectives and multiple indicators for evaluating the generalization performance accurately. The MOO algorithm is applied to adjust both the structure and parameters of the FNN, resulting in significant improvements compared to other algorithms.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Honggui Han, Hongxu Liu, Zheng Liu, Junfei Qiao
Summary: This article introduces an interactive transfer learning (ITL) algorithm to improve the learning performance of fuzzy neural network (FNN). Through knowledge filtering, self-balancing mechanism, and structural competition algorithm, ITL-FNN can achieve effective knowledge transfer and optimize learning performance between different scenes.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Automation & Control Systems
Juntao Fei, Yun Chen, Lunhaojie Liu, Yunmei Fang
Summary: This study proposes a fuzzy double hidden layer recurrent neural network (FDHLRNN) controller for nonlinear systems using terminal sliding-mode control (TSMC). The FDHLRNN shows advantages in approximation capability and control performance, and its effectiveness is verified through simulation examples and hardware experiments.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Engy El-Shafeiy, Amr A. A. Abohany, Wael M. M. Elmessery, Amr A. Abd El-Mageed
Summary: This paper proposes an adaptive model, FNN-SWO, based on Fuzzy Neural Network and Sperm Whale Optimization, to estimate the maturity of coconut water. The model is trained and tested using fuzzy rules and the SWO algorithm. The FNN-SWO model outperforms other conventional techniques in terms of prediction outcomes.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Acoustics
Hamideh Sedigh Ziyabari, Mahdi Aliyari Shoorehdeli
JOURNAL OF VIBRATION AND CONTROL
(2018)
Article
Automation & Control Systems
J. Taheri-Kalani, G. Latif-Shabgahi, M. Alyari Shooredeli
JOURNAL OF PROCESS CONTROL
(2018)
Article
Computer Science, Artificial Intelligence
Mohammad Mandi Zabihi Shesh Poli, Mahdi Aliyari Shoorehdeli, Ali Moarefianpour
Article
Engineering, Electrical & Electronic
Fatemeh Nasri Rudsari, Ali Asghar Razi-Kazemi, Mahdi Aliyari Shoorehdeli
IEEE TRANSACTIONS ON POWER DELIVERY
(2019)
Article
Automation & Control Systems
Koorosh Aslansefat, Mandi Bahar Gogani, Sohag Kabir, Mandi Aliyari Shoorehdeli, Mostafa Yari
Article
Automation & Control Systems
Hossein Ali Ghiassirad, Mandi Aliyari Shoorehdeli, Faezeh Farivar
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2019)
Article
Engineering, Chemical
Mehran Haghparast, Mahdi Aliyari Shoorehdeli
Summary: This paper proposes a causality detection method based on distance correlation, utilizing causality measure index and REDC to reduce computational burden. The method provides acceptable performance for causality detection in linear and nonlinear systems, significantly reducing computational time.
Article
Automation & Control Systems
Ali Asghar Sheydaeian Arani, Mahdi Aliyari Shoorehdeli, Ali Moarefianpour, Mohammad Teshnehlab
Summary: This paper introduces a method for fault estimation in a nonlinear system using the unscented Kalman filter, augmented by a fault signal as a state variable. A filter combining Gaussian mixture model and augmented ensemble unscented Kalman filter is designed for estimating faults in nonlinear systems, with suitable conditions and assumptions for convergence. The proposed method is evaluated in simulating a bioreactor system, demonstrating better performance compared to traditional methods in the presence of non-Gaussian noise.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2021)
Article
Engineering, Chemical
Majid Ghaniee Zarch, Vicenc Puig, Javad Poshtan, Mahdi Aliyari Shoorehdeli
Summary: This paper investigates the application of viability theory in nonlinear processes, proposing verification algorithms based on invariance and viability kernels and capture basin. The use of Lagrangian method and zonotopes simplifies computation. Two new sets, SWA and RP, are defined and an algorithm is proposed for verification.
Article
Automation & Control Systems
Mohadese Jahanian, Amin Ramezani, Ali Moarefianpour, Mahdi Aliyari Shoorehdeli
Summary: This study presents a robust extended Kalman filter for discrete-time nonlinear systems, capable of estimating unknown inputs and system states simultaneously while ensuring an upper bound on estimation error covariance. The filter demonstrates robustness against noise, uncertainties, and unknown inputs. The effectiveness of the REKF is validated through simulated gas pipeline leakage detection.
OPTIMAL CONTROL APPLICATIONS & METHODS
(2022)
Article
Engineering, Electrical & Electronic
Ali Asghar Sheydaeian Arani, Mahdi Aliyari Shoorehdeli, Ali Moarefianpour, Mohammad Teshnehlab
Summary: This paper introduces a new filter, FAEnUKF, based on a Takagi-Sugeno fuzzy augmented ensemble unscented Kalman filter, for handling nonlinear stochastic systems with multiplicative fault and noise. By transforming the nonlinear system into several T-S fuzzy systems, the non-Gaussian noise can be effectively estimated. The filter algorithm's convergence conditions and the boundedness of the error covariance matrix are presented, and the effectiveness of FAEnUKF is evaluated through illustrative examples.
CIRCUITS SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Chemistry, Analytical
Marzieh Hosseini, Anna Kaasinen, Mahdi Aliyari Shoorehdeli, Guido Link, Timo Lahivaara, Marko Vauhkonen
Summary: The microwave drying process is widely used in industry, with a focus on drying polymer foams for sealings in construction. A state-space model is developed using system identification tools and electrical capacitance tomography (ECT) sensor for moisture measurement, allowing for accurate controller design. Multiple experiments validate the model's accuracy in controlling the microwave drying process.
Article
Computer Science, Artificial Intelligence
Abolfazl Hasanzadeh Shadiani, Mahdi Aliyari Shoorehdeli
Summary: This paper proposes an online approach for twin support vector machine, which utilizes recursive relation to avoid repetitive calculation of inverse matrices, resulting in improved training efficiency and maintained accuracy. Experimental results demonstrate the effectiveness of this method.
NEURAL PROCESSING LETTERS
(2023)
Article
Engineering, Electrical & Electronic
Milad Tahvilzadeh, Mahdi Aliyari-Shoorehdeli, Ali A. Razi-Kazemi
Summary: This paper presents a model that simulates a real case to solve the problem of data collection in fault detection and validates the effectiveness of the model. The results of the study indicate that support vector machines and decision trees are the most effective models for detecting operating mechanisms.
IEEE TRANSACTIONS ON POWER DELIVERY
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Seyed Mohammad Emad Oliaee, Mahdi Aliyari Shoorehdeli, Mohammad Teshnehlab
2018 6TH IRANIAN JOINT CONGRESS ON FUZZY AND INTELLIGENT SYSTEMS (CFIS)
(2018)