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TSTKD: Triple-spike train kernel-driven supervised learning algorithm☆

PATTERN RECOGNITION 2025年 164卷

作者： Chen, Guojun Wang, Guoen Hubei Normal Univ Huangshi 435002 Peoples R China Wuhan Univ Wuhan 430072 Peoples R China

Precise artificial intelligence is one of the most promising research fields, where supervised learning for spiking neurons (SNs) plays an imperative and fundamental role. This study proposes a novel supervised learning algorithm based on triple-spike train kernels to address the shortcomings of the latest learning algorithms, such as local best learning and low learning accuracy. First, we divided the time intervals of the spike trains, including the firing time of the input spikes. Subsequently, we discovered and analyzed the relationship between the firing times of all spikes, added a third spike to solve the existing problem, and constructed a triple-spike-driven (TSD) minimum direct computational unit. In addition to the simple and efficient adjustment of synaptic weights based on pair-spike, TSD maintains a relationship between all useful spikes to approximate the global best learning. Finally, we proposed a triple-spike train kernel driven (TSTKD) supervised learning algorithm to improve the learning performance. Many fundamental experiments were implemented to demonstrate the learning performance, which proved that the successful learning ability and some learning factors of our proposed algorithm in spike train learning. We then verified the positive effect of the TSD on the proposed algorithm. Many experiments also proved the much higher learning accuracy of the proposed state-of-the-art algorithm compared to some of the latest algorithms, especially in the complex spike train learning. In addition, the proposed algorithm is more adaptive to SNs and much better at generalizing, memorizing, and classifying than the corresponding algorithm with pair-spike and some of the latest algorithms. Considering the above experimental results, our study blazes a trail for pattern recognition using spike train supervised learning with global optimization.

关键词： Spiking neuron Spike train kernel Spike pattern learning supervised learning algorithm Triple spikes

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Demand response management system with discrete time window using supervised learning algorithm

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COGNITIVE SYSTEMS RESEARCH 2019年 57卷 131-138页

作者： Chellamani, Ganesh Kumar Chandramani, Premanand Venkatesh Sri Sivasubramaniya Nadar Coll Engn Dept Elect & Commun Engn Chennai 603110 Tamil Nadu India

Demand Response (DR) is a key attribute to enhance the operation of smart grid. Demand response improves the performance of the electric power systems and also deals with peak demand issues. Demand Response (DR) implementation for residential consumers is potentially accredited by Home Energy Management System (HEMS). This paper presents an algorithm for home energy management system to shift the schedulable loads in a residential home, that neglects consumer discomfort and minimizes electricity bill of energy consumption using Hourly-Time-Of-Use (HTOU) pricing scheme. supervised learning algorithm is used in this paper to learn the usage patterns of consumers to allow schedulable appliances at a residential home to autonomously overcome consumer discomfort. Simulation results confirms that the proposed algorithm effectively decreases consumer electricity bill, decreases peak load demand and also avoids consumer discomfort. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Demand response Smart grid Home energy management HTOU pricing Schedulable appliance supervised learning algorithm

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A supervised learning algorithm for Recurrent Spiking Neural Networks Based on BP-STDP 28th

A Supervised Learning Algorithm for Recurrent Spiking Neural...

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28th International Conference on Neural Information Processing

作者： Guo, Wenjun Lin, Xianghong Yang, Xiaofei Northwest Normal Univ Coll Comp Sci & Engn Lanzhou 730070 Peoples R China

ISBN: (纸本)9783030923068;9783030923075

Spiking neural network model encodes information with precisely timed spike train, it is very suitable to process complex spatiotemporal patterns. Recurrent spiking neural network has more complex dynamics characteristics because of feedback connections, which makes it difficult to design efficient learning algorithms. This paper proposes a supervised learning algorithm to train recurrent spiking neural networks. By mapping the integrate-and-fire neuron model to the rectified linear unit activation function, the learning rule is induced using error backpropagation and spike-timing dependent plasticity mechanism. The results of spike train learning task and non-linear pattern classification show that the algorithm is effective to learn spatiotemporal patterns. In addition, the influences of different parameters on learning performance are analyzed.

关键词： Recurrent spiking neural networks supervised learning algorithm Spiking-timing dependent plasticity Backpropagation

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A Novel supervised learning algorithm for Musical Instrument Classification

A Novel Supervised Learning Algorithm for Musical Instrument...

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35th International Conference on Telecommunications and Signal Processing (TSP)

作者： Rui, Rui Bao, Changchun Beijing Univ Technol Sch Elect Informat & Control Engn Speech & Audio Signal Proc Lab Beijing 100124 Peoples R China

ISBN: (纸本)9781467311182

In this paper, a novel supervised learning algorithm for automatic classification of individual musical instrument sounds is addressed deriving from the idea of supervised non-negative matrix factorization (NMF) algorithm. In our approach, the orthogonal basis matrix could be obtained without updating the matrix iteratively, which supervised NMF algorithm is unable to do. Afterwards, each data is projected onto several training orthogonal basis matrices and three classifiers have been employed to compare the performance with different methods. In addition, feature selection is also applied in order to choose the most discriminative features for instrument classification. The results indicate that the classification accuracy of proposed method is 87.6%, which is comparable to the performance of supervised NMF algorithm for the same experiments.

关键词： Musical instrument classification supervised learning algorithm feature selection

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Unmanned Hybrid Electric Vehicles FNN Control based on Self-organized learning algorithm and supervised learning algorithm

Unmanned Hybrid Electric Vehicles FNN Control based on Self-...

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2010 International Conference on Computer,Mechatronics,Control and Electronic Engineering(2010计算机、机电、控制与电子工程国际会议 CMCE 2010)

作者： Yun Zhang Xuemei Chen Xiumin Yu Mingshuang Bi College of Automobile Engineering Jilin University Changchun China Water Resources Department of the JiLin Province Changchun China

ISBN: (纸本)9781424479573

To resolve unmanned hybrid electric vehicles control problems, the fuzzy neural network control method based on self-organized learning algorithm and supervised learning algorithm is proposed in this paper. This algorithm can learn proper fuzzy logic rules and optimal memberships functions from training examples. Using this control method,,we can control an unmanned hybrid electric vehicle by learning the driving technique of a skilled drive. By combining both unsu per vised self-organized and supervised learning algorithm,the learning speed converges much faster than the original backpropagation learning algorithm. Simulation results are presented to illustrate the performance and applicability of the proposed learning algorithm.

关键词： unmanned hybrid electric vehicle fuzzy neural network(FNN) self-organized learning algorithm supervised learning algorithm

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Unmanned Hybrid Electric Vehicles FNN Control based on Self-organized learning algorithm and supervised learning algorithm

Unmanned Hybrid Electric Vehicles FNN Control based on Self-...

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2010 International Conference on Computer,Mechatronics, Control and Electronic Engineering (CMCE 2010)

作者： Yun Zhang Xiumin Yu Xuemei Chen College of Automobile Engineering Jilin University Changchun, China Mingshuang Bi Water Resources Department of the JiLin Province Changchun, China

To resolve unmanned hybrid electric vehicles control problems, the fuzzy neural network control method based on self-organized learning algorithm and supervised learning algorithm is proposed in this paper. This algorithm can learn proper fuzzy logic rules and optimal memberships functions from training examples. Using this control method,,we can control an unmanned hybrid electric vehicle by learning the driving technique of a skilled drive. By combining both unsupervised self-organized and supervised learning algorithm,the learning speed converges much faster than the original backpropagation learning algorithm. Simulation results are presented to illustrate the performance and applicability of the proposed learning algorithm.

关键词： unmanned hybrid electric vehicle fuzzy neural network(FNN) self-organized learning algorithm supervised learning algorithm

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supervised learning and meshless methods for two-dimensional fractional PDEs on irregular domains

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MATHEMATICS AND COMPUTERS IN SIMULATION 2024年 216卷 77-103页

作者： Abbaszadeh, Mostafa Zaky, Mahmoud A. Hendy, Ahmed S. Dehghan, Mehdi Amirkabir Univ Technol Tehran Polytech Fac Math & Comp Sci Dept Appl Math 424 Hafez Ave Tehran 15914 Iran Imam Mohammad Ibn Saud Islamic Univ IMSIU Coll Sci Dept Math & Stat Riyadh Saudi Arabia Natl Res Ctr Dokki Dept Appl Math Giza 12622 Egypt Ural Fed Univ Inst Nat Sci & Math Dept Computat Math & Comp Sci 19 Mira St Ekaterinburg 620002 Russia Benha Univ Fac Sci Dept Math Banha 13511 Egypt

Recently, several numerical methods have been developed for solving time-fractional differential equations not only on rectangular computational domains but also on convex and non-convex non-rectangular computational geometries. On the other hand, due to the existence of integrals in the definition of space-fractional operators, there are few numerical schemes for solving space-fractional differential equations on irregular regions. In this paper, we develop a novel numerical solution based on the machine learning technique and a generalized moving least squares approximation for two-dimensional fractional PDEs on irregular domains. The scheme is constructed on the monomials, and this is the strength of this technique. Moreover, it will be used to approximate the space derivatives on convex and non-convex non-rectangular computational domains. The numerical results are extended to solve the fractional Bloch-Torrey equation, fractional Gray-Scott equation, and fractional Fitzhugh-Nagumo equation. (c) 2023 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： supervised learning algorithm Least squares support vector regression machines Fractional Bloch-Torrey equation Generalized moving least squares approximation Convex and non-convex computational domains

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A supervised learning approach for heading detection

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EXPERT SYSTEMS 2020年第4期37卷 e12520-e12520页

作者： Budhiraja, Sahib Singh Mago, Vijay Lakehead Univ Dept Comp Sci DaTALab Thunder Bay ON P7B 5E1 Canada

As the popularity of the portable document format (PDF) file format increases, research that facilitates PDF text analysis or extraction is necessary. Heading detection is a crucial component of PDF-based text classification processes. This research involves training a supervised learning model to detect headings by systematically testing and selecting classifier features usingrecursive feature elimination. Results indicate that decision tree is the best classifier with an accuracy of 95.83%, sensitivity of 0.981, and a specificity of 0.946. This research into heading detection contributes to the field of PDF-based text extraction and can be applied to the automation of large scale PDF text analysis in a variety of professional and policy-based contexts.

关键词： heading detection machine learning supervised learning algorithm text segmentation

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supervised learning Method of Parallel Classified Hesitant Information and its Application in the Smart Home System Selection

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IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT 2024年 71卷 11212-11224页

作者： Liu, Man Zhou, Wei Xu, Zeshui Chongqing Normal Univ Sch Econ & Management Chongqing 401331 Peoples R China Yunnan Univ Finance & Econ Business Sch Kunming 650221 Peoples R China Sichuan Univ Business Sch Chengdu 610064 Peoples R China

In the group decision-making process, experts and decision makers can sometimes provide subjective evaluation information with classification labels. To effectively deal with it and make a reasonable decision, two key issues should be addressed first, which include the information representation and consensus classification model in the above-mentioned uncertain information environment. To do so, this article extends the hesitant fuzzy set (HFS), which has been a hot and effective presentation tool in recent years, to the classification HFS (CHFS), the parallel HFS, and the parallel CHFS. Thus, we can mathematically present three types of evaluation information with classification labels or parallel characteristics in the consensus classification process. Then, to model the parallel classified hesitant fuzzy information and help further consensus classification, this article proposes a supervised learning algorithm and proves its generalization and optimization. In addition, based on the supervised learning algorithm and the obtained classification probability information, we develop a consensus classification method in the parallel classified hesitant fuzzy environment to derive the optimal consensus classification results. Finally, this article applies the proposed algorithm and methods to a real example of smart home system selection, which can show their rationality and feasibility.

关键词： Classification probability information group classification consensus parallel classified hesitant fuzzy set (HFS) smart home system supervised learning algorithm

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Multiclass supervised Machine learning algorithms Applied to Damage and Assessment Using Beam Dynamic Response

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JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES 2023年第6期11卷 2709-2731页

作者： de Sousa, Amanda Aryda Silva Rodrigues da Silva Coelho, Jefferson Machado, Marcela Rodrigues Dutkiewicz, Maciej Univ Brasilia Postgrad Program Integr Engn Mat Campus Univ Darcy Ribeiro BR-70910900 Brasilia DF Brazil Univ Brasilia Dept Mech Engn Campus Univ Darcy Ribeiro BR-70910900 Brasilia DF Brazil Bydgoszcz Univ Sci & Technol Fac Civil Environm Engn & Architecture PL-85796 Bydgoszcz Poland

PurposeStructural damage can significantly alter a system's local flexibility, leading to undesirable displacements and vibrations. Analysing the dynamic structure feature through statistical analysis enables us to discriminate the current structural condition and predict its short- or long-term lifespan. By directly affecting the system's vibration, cracks and discontinuities can be detected, and their severity quantified using the DI. Two damage indexes (DI) are used to build a dataset from the beam's natural frequency and frequency response function (FRF) under both undamaged and damaged conditions, and numerical and experimental tests provided the *** this paper, we present the methodology based on machine learning (ML) to monitor the structural integrity of a beam-like structure. The performance of six ML algorithms, including k-nearest neighbors (kNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Naive Bayes (NB) are *** paper discusses the challenges of implementing each technique and assesses their performance in accurately classifying the dataset and indicating the beam's *** structural monitoring performed with the ML algorithm achieved excellent metrics when inputting the simulation-generated dataset, up to 100%, and up to 95% having as input dataset provided from experimental tests. Demonstrating that the ML algorithm could correctly classify the health condition of the structure.

关键词： Machine learning Structural health monitoring Damage detection Data-driven supervised learning algorithm

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