Review
Chemistry, Multidisciplinary
Yicen Liu, Ying Chen, Prithwineel Paul, Songhai Fan, Xiaomin Ma, Gexiang Zhang
Summary: This paper discusses the application of spiking neural P systems in fault diagnosis in power systems, and explores their efficiency in different power equipment systems as well as future research directions.
APPLIED SCIENCES-BASEL
(2021)
Article
Physics, Multidisciplinary
Xiaotian Chen, Tao Wang, Ruixuan Ying, Zhibo Cao
Summary: This paper proposes a fault diagnosis method for transmission networks considering meteorological factors. By designing a spiking neural P system and matrix reasoning algorithm, and establishing a diagnosis model for each suspicious fault transmission line, diagnosis results are obtained through parallel execution of the algorithm, showing the feasibility and effectiveness of the proposed method on the IEEE 39-bus system.
Article
Physics, Multidisciplinary
Xiu Yin, Xiyu Liu, Minghe Sun, Jianping Dong, Gexiang Zhang
Summary: This paper introduces the fuzzy reasoning numerical spiking neural P systems (FRNSN P systems) by incorporating interval-valued triangular fuzzy numbers into the numerical spiking neural P systems (NSN P systems). The FRNSN P systems are used for induction motor fault diagnosis, and can effectively identify incomplete and uncertain fault information through modeling and reasoning, facilitating timely repairs.
Article
Computer Science, Artificial Intelligence
Tingfang Wu, Qiang Lyu, Linqiang Pan
Summary: This study explores spiking neural P systems (SNP systems) and their variant evolution-communication SNP (ECSNP) systems, demonstrating the Turing universality of ECSNP systems as number-generating devices and highlighting the critical impact of the quantity of spikes in neurons on the computational power of ECSNP systems.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2021)
Article
Mathematics
Krassimir Atanassov, Sotir Sotirov, Tania Pencheva
Summary: This paper introduces the concept of an intuitionistic fuzzy deep neural network (IFDNN), which combines artificial neural networks and intuitionistic fuzzy sets to leverage the advantages of both methods. The study methodologically presents the entire development process of IFDNN, starting from the simplest form of an intuitionistic fuzzy neural network (IFNN) with one layer and a single-input neuron, and progressing to more complex structures with multi-input neurons. The formulas for estimating NN parameters, represented as intuitionistic fuzzy pairs, are provided for each of the presented IFNNs. An example of using IFDNN for biomedical data is also presented to demonstrate its feasibility.
Article
Computer Science, Information Systems
Tao Wang, Wei Liu, Peng Wang, Xiaoguang Wei, Tianlei Zang, Luis Valencia Cabrera
Summary: This paper proposes a fault diagnosis method based on memory spiking neural P systems to distinguish false faults caused by measurement tampering attacks. The method includes three modules for suspicious fault section detection, measurement tamper attack identification, and fault diagnosis. Case studies on IEEE 14 and IEEE 118 bus systems validate the feasibility and effectiveness of the proposed method.
INFORMATION SCIENCES
(2022)
Article
Mathematics
Shahzad Faizi, Heorhii Svitenko, Tabasam Rashid, Sohail Zafar, Wojciech Salabun
Summary: This paper proposes operations and properties on the cubic intuitionistic set, including the internal and external cubic intuitionistic sets, P-order, R-order, P-union, R-union, P-intersection, and R-intersection. The paper investigates properties of these operations and presents examples. Important theorems related to the internal and external cubic intuitionistic sets are also presented. The effectiveness and significance of the proposed operations are measured through solving a multi-criteria decision-making problem.
Article
Mathematics, Interdisciplinary Applications
Nitesh Dhiman, Madan M. Gupta, Dhan Pal Singh, Vandana, Vishnu Narayan Mishra, Mukesh K. Sharma
Summary: In this research, an intuitionistic fuzzy fractional knowledge-based expert system is proposed for the diagnosis of diseases in the medical field. This system is able to handle discrete data and has effective analysis capabilities.
FRACTAL AND FRACTIONAL
(2022)
Article
Computer Science, Artificial Intelligence
Lifan Long, Rikong Lugu, Xin Xiong, Qian Liu, Hong Peng, Jun Wang, David Orellana-Martin, Mario J. Perez-Jimenez
Summary: Nonlinear spiking neural P (NSNP) systems are distributed parallel neural-like computing models that abstract the nonlinear spiking mechanisms of biological neurons. Inspired by the structure of echo state network (ESN), this study proposes a new variant of NSNP systems called echo spiking neural P (ESNP) systems. The experimental results demonstrate the effectiveness of the proposed ESNP model for time-series forecasting.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Mathematics
Lilija Atanassova, Piotr Dworniczak
Summary: This study introduces a new operation increment over intuitionistic fuzzy sets and explores its properties, including analogues to De Morgan's Law, the Fixed Point Theorem, and connections to classical modal operators over IFS Necessity and Possibility. The operation increment can be used for de-fuzzification, and a geometrical interpretation of the construction process is provided.
Article
Mathematics, Applied
Atiqe Ur Rahman, Muhammad Saeed, Hamiden Abd El-Wahed Khalifa, Walaa Abdullah Afifi
Summary: This study generalizes the concept of possibility intuitionistic fuzzy hypersoft set and proposes algorithms based on AND/OR operations for decision making. The set-theoretic operations and similarity measure of possibility intuitionistic fuzzy hypersoft sets are investigated with numerical examples, matrix and graphical representations. The proposed structure and similarity formulation are compared with existing models to validate their effectiveness.
Editorial Material
Computer Science, Artificial Intelligence
Akansha Mishra, Amit Kumar, S. S. Appadoo
Summary: Li and Chen proposed the concept of D-intuitionistic hesitant fuzzy set and a method for comparing them, which was later found to be inadequate for distinguishing distinct sets.
COGNITIVE COMPUTATION
(2021)
Article
Mathematics
Krassimir Atanassov
Summary: This article introduces the concept and basic properties of Intuitionistic Fuzzy Modal Topological Structure (IFMTS), discusses the importance of the intuitionistic fuzzy modal and topological operators, and formulates ideas for the future development of IFMT theory.
Article
Computer Science, Information Systems
Zhang Sun, Luis Valencia -Cabrera, Guimin Ning, Xiaoxiao Song
Summary: Spiking neural P systems are an abstraction of the structure and function of nervous systems and neurons. SNP-WOD systems, a new class of these systems, remove the mechanism of duplication and allow for the amplification of pulses during the firing of spiking rules. These systems have computational properties and can generate numbers.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Xiaoxiao Song, Luis Valencia-Cabrera, Hong Peng, Jun Wang
Summary: This paper introduces a new neural computing model - spiking neural P systems with autapses (SNP-AU systems) and demonstrates their ability to generate Turing-computable numbers. By building an SNP-AU system with 53 neurons and providing a universal machine, the universality of its computing function is shown.
INFORMATION SCIENCES
(2021)
Article
Automation & Control Systems
Ting Shi, Peng Shi, Liping Zhang
Summary: This paper investigates the leader-following consensus problem for general linear multi-agent systems under external disturbances. The communication topologies are time-varying and switched from a finite set. A switched control system is introduced to model these topologies, and the weighted L-2 - L-infinity performance is analyzed. A topology-dependent controller is designed based on local information from the neighbors. Conditions are developed for the existence of a control protocol that achieves the leader-following consensus with a certain level of weighted L-2 - L-infinity performance. The design algorithm is formulated as a set of linear matrix inequalities (LMIs), and a numerical example is provided to demonstrate the effectiveness of the proposed consensus algorithm.
INTERNATIONAL JOURNAL OF CONTROL
(2022)
Article
Automation & Control Systems
Renjie Ma, Peng Shi
Summary: This paper presents defense strategies based on switched counteraction principle to protect the secure state estimation (SSE) of Cyber-Physical Systems (CPSs) from sparse data injection (DI) attacks. The physical layer is modeled using a hybrid mechanism and malicious injections are excluded through adaptively switched counteraction searching. The proposed design methods are demonstrated to be effective and promising through numerical examples.
INTERNATIONAL JOURNAL OF CONTROL
(2022)
Article
Engineering, Marine
Yuanjie Ren, Lanyong Zhang, Peng Shi, Ziqi Zhang
Summary: A hierarchical collaborative control energy management scheme is proposed for the propulsion system of hybrid electric ships. The scheme effectively solves the problems of steady-state oscillation and deviation from the tracking direction caused by volatility and uncertainty, achieving significant improvement.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Yanping Huang, Hong Peng, Qian Liu, Qian Yang, Jun Wang, David Orellana-Martin, Mario J. Perez-Jimenez
Summary: The study combines a modified GSNP with attention mechanism to develop a novel model called attention-enabled GSNP model or AGSNP model for sentiment classification. The AGSNP model has two channels for processing content words and aspect items, using modified GSNPs to capture dependencies between words. It also incorporates attention components to establish semantic correlation. Comparative experiments demonstrate the effectiveness of the AGSNP model for aspect-level sentiment classification tasks.
Article
Computer Science, Information Systems
Yuxiang Feng, Yao Huang, Bing Li, Hong Peng, Jian Wang, Weikai Zhou
Summary: In this paper, a QL-mRSU series artificial intelligence energy saving method is proposed to optimize the energy consumption of parked electric vehicles during communication. The method dynamically clusters electric vehicles parked in parking lots and selects suitable vehicles as mobile roadside units based on reinforcement learning. The method achieves self-learning and energy saving effects.
Article
Computer Science, Theory & Methods
Wenping Yu, Xiangquan Xiao, Jieping Wu, Fuwen Chen, Li Zheng, Huijie Zhang
Summary: To achieve carbon peaking and carbon neutrality goals, research on technologies related to the grid connection of high proportion renewable energy has become a hot topic. The use of multi-microgrid is a feasible solution, but its advantages can only be fully realized by ensuring the safe and stable operation of the system. This paper introduces fuzzy spiking neural dP systems (FSNdPS) into the multi-microgrid system to address uncertainty and inaccuracy issues caused by the integration of new energy sources, and proposes a coordinated control strategy for a typical multi-microgrid structure.
JOURNAL OF MEMBRANE COMPUTING
(2023)
Article
Computer Science, Theory & Methods
Qian Yang, Xin Xiong, Hong Peng, Jun Wang, Xiaoxiao Song
Summary: This paper investigates a new variant of spiking neural P systems (SN P systems), called nonlinear spiking neural P systems with multiple channels (NSN PMC systems). In this variant, each neuron can use its multiple channels to connect different successor neurons, and nonlinear spiking rules are introduced to control the spiking of neurons. The computational power of NSN P-MC systems is discussed, showing the Turing universality as number generating/accepting devices. Additionally, small universal NSN P-MC systems with 54 and 63 neurons are constructed to compute any Turing computable function and generate numbers.
THEORETICAL COMPUTER SCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Qian Liu, Lifan Long, Hong Peng, Jun Wang, Qian Yang, Xiaoxiao Song, Agustin Riscos-Nunez, Mario J. Perez-Jimenez
Summary: This article proposes a new variant of SNP systems, called GSNP systems, which are composed of gated neurons and introduce two gated mechanisms to control the updating of states in neurons. The GSNP model based on gated neurons is developed for time series prediction and is evaluated against benchmark models, demonstrating its availability and effectiveness.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Qian Liu, Hong Peng, Lifan Long, Jun Wang, Qian Yang, Mario J. Perez-Jimenez, David Orellana-Martin
Summary: SNP systems are neural-like computing models that are inspired by spiking neurons and have applications in chaotic time series forecasting. Nonlinear SNP systems with autapses (NSNP-AU systems) are proposed in this study, which have nonlinearity in spike consumption, generation, and gate functions. Based on NSNP-AU systems, a recurrent-type prediction model for chaotic time series, called the NSNP-AU model, is developed and implemented using a deep learning framework. Experimental results show the superiority of the NSNP-AU model in chaotic time series forecasting.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Theory & Methods
Wenping Yu, Jieping Wu, Yufeng Chen, Yubo Wu
Summary: In this paper, a fuzzy tissue-like P system with promoters (PFTPS) is proposed for power coordinated control of microgrid. The integration of promoters and a common language fuzzy system into the tissue-like P systems enables better characterization of uncertain and inaccurate information in the microgrid. The paper also discusses the modeling method, rules, and energy control strategy of the proposed PFTPS, and conducts reasoning demonstration. MATLAB simulation results demonstrate that the proposed strategy achieves better power coordination control of the microgrid.
JOURNAL OF MEMBRANE COMPUTING
(2023)
Article
Engineering, Electrical & Electronic
Huiyan Zhang, Hao Sun, Peng Shi, Luis Ismael Minchala
Summary: This article proposes a novel chip detection method that combines attentional feature fusion and cosine nonlocal attention to effectively handle chip images with multiple classes or complex backgrounds. Experimental results demonstrate that the proposed method outperforms the benchmark method on a medium-scale dataset.
Article
Computer Science, Theory & Methods
Shuwei Zhao, Li Zhang, Zhicai Liu, Hong Peng, Jun Wang
Summary: Inspired by spiking mechanisms in spiking neural P (SNP) systems, this paper proposes a new type of neurons, termed as SNP-like neurons. Based on SNP-like neurons, a new class of deep learning models called ConvSNP models are developed. Five ConvSNP models are designed by referring to the structures of existing convolutional neural networks (CNNs). The evaluation results on three benchmark data sets demonstrate the availability and effectiveness of ConvSNP models for classical classification tasks.
JOURNAL OF MEMBRANE COMPUTING
(2022)
Article
Remote Sensing
Yang Fei, Yuan Sun, Peng Shi
Summary: In this study, a hierarchical formation control strategy is used to address the robust formation control problem for a group of UAVs with system uncertainty. A sliding mode neural-based observer is constructed to estimate the nonlinear uncertainty in the UAV model, and sliding mode controllers and differentiators are designed to alleviate chattering in the control input. The proposed control scheme's effectiveness is validated through Lyapunov stability theory and numerical simulations on a multiple-UAV system.
