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A physics-informed learning algorithm in dynamic speed prediction method for series hybrid electric powertrain

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 133卷

作者： Liu, Wei Yang, Chao Wang, Weida Yang, Liuquan Wang, Muyao Su, Jie Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China China North Vehicle Res Inst Beijing 100072 Peoples R China

Engine-generator set (EGS) is an important energy supply component of high-voltage microgrid in series hybrid electric powertrain (SHEP). Sustained and steady energy supply from EGS is one of the conditions for the balanced energy between supply and demand. In some high-power processes, the balanced energy would be broken and the dynamic speed of EGS would be out of expectation, which can result in unstable working states of EGS. If the unstable working states of EGS can be known prior, it is significant for the research of unstable state identification and avoidance. Predicting rotational speed of EGS can warn of the previous issue in advance, while the insufficient data of unstable states would encounter overfitting problems in common prediction methods, so it is a challenge to improve the prediction effect of dynamic speed and then accurately predict the unstable states. Base on the above problems, a physics-informed learning algorithm with adaptive mechanism is proposed for EGS rotational speed prediction in this paper. First, a prediction problem related to the stability of SHEP running state is studied, which is found from engineering knowledge. Second, a new mechanism is proposed for physicsinformed learning algorithm, and the physical information adopted to learning algorithm is more selective. Third, a professional adaptive function is originally formed according to speed characteristics, which bridge the information between physics and learning algorithm. By importing the experimental data, the prediction accuracy of proposed method in one of the test cycles is better than the results of baseline methods, specifically 27.11% and 3.49%, 11.90% and 7.94%, 53.83% and 27.62%. In summary, the proposed method can have better predictions against other baseline methods.

关键词： Series hybrid electric powertrain Prediction method Dynamic speed Adaptive function learning algorithm Data analysis

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A learning algorithm for applying synthesized stable dynamics to system identification

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NEURAL NETWORKS 1998年第1期11卷 81-87页

作者： Howse, JW Abdallah, CT Heileman, GL Univ Calif Los Alamos Natl Lab Computat Sci Methods Grp Los Alamos NM 87545 USA Univ New Mexico Dept Elect & Comp Engn Albuquerque NM 87131 USA

In this paper the models discussed by Cohen are extended by introducing an input term. This allows the resulting models to be utilized for system identification tasks. This approach gives a direct way to encode qualitative information such as attractor dimension into the model. We prove that this model is stable in the sense that a bounded input leads to a bounded state when a minor restriction is imposed on the Lyapunov function. By employing this stability result, we are able to find a learning algorithm which guarantees convergence to a set of parameters for which the error between the model trajectories and the desired trajectories vanishes. (C) 1998 Elsevier Science Ltd. All lights reserved.

关键词： stability learning algorithm system identification

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An iterative learning algorithm for feedforward neural networks with random weights

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INFORMATION SCIENCES 2016年 328卷 546-557页

作者： Cao, Feilong Wang, Dianhui Zhu, Houying Wang, Yuguang China Jiliang Univ Dept Appl Math Hangzhou 310018 Zhejiang Peoples R China La Trobe Univ Dept Comp Sci & Informat Technol Melbourne Vic 3086 Australia Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Liaoning Peoples R China Univ New S Wales Sch Math & Stat Sydney NSW 2052 Australia

Feedforward neural networks with random weights (FNNRWs), as random basis function approximators, have received considerable attention due to their potential applications in dealing with large scale datasets. Special characteristics of such a learner model come from weights specification, that is, the input weights and biases are randomly assigned and the output weights can be analytically evaluated by a Moore-Penrose generalized inverse of the hidden output matrix. When the size of data samples becomes very large, such a learning scheme is infeasible for problem solving. This paper aims to develop an iterative solution for training FNNRWs with large scale datasets, where a regularization model is employed to potentially produce a learner model with improved generalization capability. Theoretical results on the convergence and stability of the proposed learning algorithm are established. Experiments on some UCI benchmark datasets and a face recognition dataset are carried out, and the results and comparisons indicate the applicability and effectiveness of our proposed learning algorithm for dealing with large scale datasets. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Neural networks with random weights learning algorithm Stability Convergence

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A precise and stable machine learning algorithm: eigenvalue classification (EigenClass)

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NEURAL COMPUTING & APPLICATIONS 2021年第10期33卷 5381-5392页

作者： Erkan, Ugur Karamanoglu Mehmetbey Univ Engn Fac Dept Comp Engn TR-70200 Karaman Turkey

In this study, a precise and efficient eigenvalue-based machine learning algorithm, particularly denoted as Eigenvalue Classification (EigenClass) algorithm, has been presented to deal with classification problems. The EigenClass algorithm is constructed by exploiting an eigenvalue-based proximity evaluation. To appreciate the classification performance of EigenClass, it is compared with the well-known algorithms, such as k-nearest neighbours, fuzzy k-nearest neighbours, random forest (RF) and multi-support vector machines. Number of 20 different datasets with various attributes and classes are used for the comparison. Every algorithm is trained and tested for 30 runs through 5-fold cross-validation. The results are then compared among each other in terms of the most used measures, such as accuracy, precision, recall, micro-F-measure, and macro-F-measure. It is demonstrated that EigenClass exhibits the best classification performance for 15 datasets in terms of every metric and, in a pairwise comparison, outperforms the other algorithms for at least 16 datasets in consideration of each metric. Moreover, the algorithms are also compared through statistical analysis and computational complexity. Therefore, the achieved results show that EigenClass is a precise and stable algorithm as well as the most successful algorithm considering the overall classification performances.

关键词： Data classification Eigenvalues learning algorithm Machine learning Supervised learning

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Approximation analysis of ontology learning algorithm in linear combination setting

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JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2020年第1期9卷 1-10页

作者： Gao, Wei Chen, Yaojun Yunnan Normal Univ Sch Informat Sci & Technol Kunming 650500 Yunnan Peoples R China Nanjing Univ Dept Math Nanjing 210093 Peoples R China

In the past ten years, researchers have always attached great importance to the application of ontology to its relevant specific fields. At the same time, applying learning algorithms to many ontology algorithms is also a hot topic. For example, ontology learning technology and knowledge are used in the field of semantic retrieval and machine translation. The field of discovery and information systems can also be integrated with ontology learning techniques. Among several ontology learning tricks, multi-dividing ontology learning is the most popular one which proved to be in high efficiency for the similarity calculation of tree structure ontology. In this work, we study the multi-dividing ontology learning algorithm from the mathematical point of view, and an approximation conclusion is presented under the linear representation assumption. The theoretical result obtained here has constructive meaning for the similarity calculation and concrete engineering applications of tree-shaped ontologies. Finally, linear combination multi-dividing ontology learning is applied to university ontologies and mathematical ontologies, and the experimental results imply that the higher efficiency of the proposed approach in actual applications.

关键词： Ontology learning algorithm Multi-dividing setting Linear combination

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FAST learning algorithm OF WAVELET NETWORK BASED ON FAST WAVELET TRANSFORM

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2011年第8期25卷 1297-1319页

作者： Jemai, Olfa Zaied, Mourad Ben Amar, Chokri Alimi, Mohamed Adel Higher Inst Comp Sci Medenine 4119 Tunisia Univ Sfax REGIM Natl Engn Sch Sfax ENIS Sfax 3038 Tunisia

In this paper, a novel learning algorithm of wavelet networks based on the Fast Wavelet Transform (FWT) is proposed. It has many advantages compared to other algorithms, in which we solve the problem in previous works, when the weights of the hidden layer to the output layer are determined by applying the back propagation algorithm or by direct solution which requires to compute the matrix inversion, this may cause intensive computation when the learning data is too large. However, the new algorithm is realized by iterative application of FWT to compute the connection weights. Furthermore, we have extended the novel learning algorithm by using Levenberg - Marquardt method to optimize the learning functions. The experimental results have demonstrated that our model is remarkably more refreshing than some of the previously established models in terms of both speed and efficiency.

关键词： Wavelet network fast wavelet transform learning algorithm

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A NOVEL APPROACH FOR FACE RECOGNITION BASED ON FAST learning algorithm AND WAVELET NETWORK THEORY

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INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING 2011年第6期9卷 923-945页

作者： Zaied, Mourad Said, Salwa Jemai, Olfa Ben Amar, Chokri Natl Engn Sch Gabes Gabes Tunisia Natl Engn Sch Sfax ENIS Res Grp Intelligent Machines Sfax 3038 Tunisia

This paper presents a new approach of face recognition based on wavelet network using 2D fast wavelet transform and multiresolution analysis. This approach is divided in two stages: the training stage and the recognition stage. The first consists to approximate every training face image by a wavelet network. The second consists in recognition of a new test image by comparing it to all the training faces, the distances between this test face and all images from the training set are calculated in order to identify the searched person. The usual training algorithms presents some disadvantages when the weights of the wavelet network are computed by applying the back-propagation algorithm or by direct solution which requires computing an inversion of matrix, this computation may be intensive when the learning data is too large. We present in this paper our solutions to overcome these limitations. We propose a novel learning algorithm based on the 2D Fast Wavelet Transform. Furthermore, we have increased the performances of our algorithm by introducing the Levenberg-Marquardt method to optimize the learning functions and using the Beta wavelet which has at both an analytical expression and wavelet filter bank. Extensive empirical experiments are performed to compare the proposed method with other approaches as PCA, LDA, EBGM and RBF neural network using the ORL and FERET benchmarks.

关键词： Wavelet network Fast Wavelet Transform learning algorithm face recognition

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Force and torque parameter estimation for a 4-pole hybrid electromagnet by ANFIS hybrid learning algorithm

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2017年第5期25卷 3684-3698页

作者： Atlihan, Murat Yalcin, Baris Can Erkan, Kadir Yildiz Tech Univ Dept Mech Engn Fac Mech Engn Istanbul Turkey

In this study, a force and torque estimation method based on an adaptive neuro-fuzzy inference system (ANFIS) has been developed to get rid of multiple integral calculations of air gap coefficients that cause time delay for magnetic levitation control applications. During magnetic levitation applications that contain a 4-pole hybrid electromagnet, multiple integral calculations have to be done for obtaining air gap permanence parameters, and these parameters are needed to calculate force and torque parameters that are produced by the poles of the hybrid electromagnet, which means, if time delay occurs for calculation of permanence parameters, actual force and actual torque values that are produced by hybrid electromagnet's poles cannot be exactly known;thus, the advantage of having an exact model of the system gets lost and, as a result, the controller's performance goes down. To address a solution, an ANFIS using a hybrid learning algorithm consisting of backpropagation and least-squares learning methods is proposed to estimate force and torque parameters using training data already obtained using multiple integral calculations before.

关键词： Magnetic levitation hybrid electromagnet parameter estimation ANFIS learning algorithm

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Global dynamics and learning algorithm of non-autonomous neural networks with time-varying delays

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NEUROCOMPUTING 2020年 416卷 59-68页

作者： Zhang, Yurong Yang, Zhichun Chongqing Normal Univ Coll Math Chongqing 400047 Peoples R China

In this paper, a class of non-autonomous neural networks with time-varying delays is considered. By using a new differential inequality and M-matrix, we investigate the positive invariant set and global attracting set of the networks without the assumption on boundedness of time delays or system coefficients. On this basis, we obtain sufficient conditions on the uniformly boundedness, the existence of periodic attractor and give its existence range for periodic neural networks. Furthermore, we offer a weight learning algorithms to ensure input-to-state stability, and give the state estimate and attracting set for the system. Our results can extend and improve earlier ones. Some examples and simulations are given to demonstrate the effectiveness of the obtained results. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Neural networks Global dynamics learning algorithm Input-to-state stability (ISS) Unbounded delays

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Recognition of speech emotion using custom 2D-convolution neural network deep learning algorithm

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INTELLIGENT DATA ANALYSIS 2020年第5期24卷 1065-1086页

作者： Zvarevashe, Kudakwashe Olugbara, Oludayo O. Durban Univ Technol ICT & Soc Res Grp South Africa Luban Workshop ZA-4000 Durban South Africa

Speech emotion recognition has become the heart of most human computer interaction applications in the modern world. The growing need to develop emotionally intelligent devices has opened up a lot of research opportunities. Most researchers in this field have applied the use of handcrafted features and machine learning techniques in recognising speech emotion. However, these techniques require extra processing steps and handcrafted features are usually not robust. They are computationally intensive because the curse of dimensionality results in low discriminating power. Research has shown that deep learning algorithms are effective for extracting robust and salient features in dataset. In this study, we have developed a custom 2D-convolution neural network that performs both feature extraction and classification of vocal utterances. The neural network has been evaluated against deep multilayer perceptron neural network and deep radial basis function neural network using the Berlin database of emotional speech, Ryerson audio-visual emotional speech database and Surrey audio-visual expressed emotion corpus. The described deep learning algorithm achieves the highest precision, recall and F1-scores when compared to other existing algorithms. It is observed that there may be need to develop customized solutions for different language settings depending on the area of applications.

关键词： Deep learning emotion recognition feature extraction learning algorithm neural network speech emotion

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