检索结果-内蒙古大学图书馆

Recent advancement of remaining useful life prediction of lithium-ion battery in electric vehicle applications: A review of modelling mechanisms, network configurations, factors, and outstanding issues

引用

ENERGY REPORTS 2024年 11卷 4824-4848页

作者： Reza, M. S. Mannan, M. Mansor, M. Ker, Pin Jern Mahlia, T. M. Indra Hannan, M. A. Univ Tenaga Nas Dept Elect & Elect Engn Kajang 43000 Malaysia Univ Technol Sydney Sch Civil & Environm Engn Ultimo NSW 2007 Australia Sunway Univ Sch Engn & Technol Bandar Sunway 47500 Malaysia Korea Univ Sch Elect Engn Seoul 136701 South Korea

The remaining useful life (RUL) prediction of lithium -ion batteries (LIBs) plays a crucial role in battery management, safety assurance, and the anticipation of maintenance needs for reliable electric vehicle (EV) operation. An efficient prediction of RUL can ensure its safe operation and prevent both internal and external failures, as well as avoid any unwanted catastrophic events. However, achieving precise RUL prediction for electric vehicles presents a challenging task due to several issues related to intricate operational characteristics and dynamic shifts in model parameters throughout the aging process, battery parameters data extraction, data preprocessing, and hyperparameters tuning of the prediction model. This phenomenon significantly impacts the advancement of electric vehicle technology. To address these challenges, this study offers a comprehensive overview of various RUL prediction methods, presenting a comparative analysis of their outcomes, advantages, drawbacks, and associated research constraints. Emphasis is placed on the necessity of a battery management system (BMS) to ensure the safe and reliable functioning of LIBs. The review delves into crucial implementation factors, including battery test bench considerations, data selection, feature extraction, data preprocessing, performance evaluation indicators, and hyperparameter tuning. Additionally, the issues and challenges related to RUL prediction approaches such as;thermal runaway, material selection, cell balancing, battery aging, relaxation impact, training algorithms, data acquisition, and hyperparameter tuning were outlined to provide an in-depth understanding of the recent situations. The outcome of this review comprehensively examines various methods for predicting the RUL of LIB in EV applications, offering insights into their advantages, limitations, and research challenges. Recommendations for future trends in LIBs technology comprise enhancing prognostic accuracy and developing robus

关键词： Lithium -ion battery Remaining useful life Electric vehicle Battery management system Prediction method Battery aging training algorithm

来源：评论

学校读者我要写书评

暂无评论

A Sparse Auto Encoder Deep Process Neural Network Model and its Application

引用

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2017年第1期10卷 1116-1131页

作者： Xu Shaohua Xue Jiwei Li Xuegui Shandong Univ Sci & Technol Coll Informat Sci & Engn Qingdao 266590 Shandong Peoples R China Northeast Petr Univ Sch Comp & Informat Technol Daqing 163318 Heilongjiang Peoples R China

Aiming at the problem of time-varying signal pattern classification, a sparse auto-encoder deep process neural network (SAE-DPNN) is proposed. The input of SAE-DPNN is time-varying process signal and the output is pattern category. It combines the time-varying signal classification method of process neural network (PNN) and the data feature extraction and hierarchical sparse representation mechanism of sparse automatic encoder (SAE). Based on the feedforward PNN model, SAE-DPNN is constructed by stacking the process neurons, SAE network and softmax classifier. It can maintain the time-sequence and structure of the input signal, express and synthesize the process distribution characteristics of multidimensional time-varying signals and their combinations. SAE-DPNN improves the identification of complex features and distinguishes between different types of signals, realizes the direct classification of time-varying signals. In this paper, the feature extraction and representation mechanism of time-varying signal in SAE-DPNN are analyzed, and a specific learning algorithm is given. The experimental results verify the effectiveness of the model and algorithm.

关键词： time-varying signal classification process neural network deep learning SAE training algorithm

来源：评论

学校读者我要写书评

暂无评论

Vocoder-free text-to-speech synthesis incorporating generative adversarial networks using low-/multi-frequency STFT amplitude spectra

引用

COMPUTER SPEECH AND LANGUAGE 2019年 58卷 347-363页

作者： Saito, Yuki Takamichi, Shinnosuke Saruwatari, Hiroshi Univ Tokyo Grad Sch Informat Sci & Technol Bunkyo Ku 7-3-1 Hongo Tokyo 1138656 Japan

This paper proposes novel training algorithms for vocoder-free text-to-speech (TTS) synthesis based on generative adversarial networks (GANs) that compensate for short-term Fourier transform (STFT) amplitude spectra in low/multi frequency resolution. Vocoder-free TTS using STFT amplitude spectra can avoid degradation of synthetic speech quality caused by the vocoder-based parameterization used in conventional TTS. Our previous work for the vocoder-based TTS proposed a method for incorporating the GAN-based distribution compensation into acoustic model training to improve synthetic speech quality. This paper extends the algorithm to the vocoder-free TTS and propose a GAN-based training algorithm using low-frequency-resolution amplitude spectra to overcome the difficulty in modeling complicated distribution of the high-dimensional spectra. In the proposed algorithm, amplitude spectra are transformed into low-frequency-resolution amplitude spectra by applying an average pooling function along with a frequency axis;then the GAN-based distribution compensation is performed in the low-frequency-resolution domain. Because the low-frequency-resolution amplitude spectra approximately emulate filter banks, the proposed algorithm is expected to improve synthetic speech quality by reducing differences in spectral envelopes of natural and synthetic speech. Furthermore, various frequency scales that are related to human speech perception (e.g., mel and inverse mel frequency scales) can be introduced to the proposed training algorithm by applying an frequency warping function to amplitude spectra. This paper also proposes a GAN-based training algorithm using multi-frequency-resolution amplitude spectra that uses both low- and original-frequency-resolution amplitude spectra to reduce the differences in not only spectral envelopes but also fine structures. Experimental results demonstrate that (1) GANs using low-frequency-resolution amplitude spectra improve speech quality and work

关键词： Vocoder-free text-to-speech training algorithm STFT amplitude spectra Generative adversarial networks Frequency resolution Frequency warping

来源：评论

学校读者我要写书评

暂无评论

Freeze-drying behaviour prediction of button mushrooms using artificial neural network and comparison with semi-empirical models

引用

NEURAL COMPUTING & APPLICATIONS 2019年第11期31卷 7257-7268页

作者： Tarafdar, Ayon Shahi, Navin Chandra Singh, Anupama Natl Inst Food Technol Entrepreneurship & Managem Dept Food Engn Sonipat 131028 Haryana India GB Pant Univ Agr & Technol Dept Postharvest Proc & Food Engn Pantnagar 263145 Uttar Pradesh India

The application of artificial neural networks (ANN) in the freeze-drying of button mushrooms has been investigated. Networks with a single hidden layer, different training algorithms and complexity in terms of the number of neurons were evaluated for identifying the best ANN infrastructure. Moisture content, moisture ratio and drying rate were taken as output drying parameters for which ANN models provided an overall correlation coefficient (R) of 0.994, 0.991 and 0.992, respectively. The predictive efficiency of ANN was compared to semi-empirical models. Coefficients for semi-empirical models of moisture ratio were determined. Logarithm model gave the best fit (R-2 = 0.985) for moisture ratio prediction but with larger mean square error and lower correlation than ANN model. The study highlights that ANN models with low complexity can be developed to precisely predict drying behaviour of biological materials while providing comparable and even superior results to that obtained from available semi-empirical drying models.

关键词： Artificial neural network training algorithm Freeze-drying Button mushroom

来源：评论

学校读者我要写书评

暂无评论

Robust fuzzy regression analysis using neural networks

引用

INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS 2008年第4期16卷 579-598页

作者： Nasrabadi, Ebrahim Hashemi, S. Mehdi Amir Kabir Univ Technol Dept Comp Sci Tehran Iran

Some neural network related methods have been applied to nonlinear fuzzy regression analysis by several investigators. The performance of these methods will significantly worsen when the outliers exist in the training data set. In this paper, we propose a training algorithm for fuzzy neural networks with general fuzzy number weights, biases, inputs and outputs for computation of nonlinear fuzzy regression models. First, we define a cost function that is based on the concept of possibility of fuzzy equality between the fuzzy output of fuzzy neural network and the corresponding fuzzy target. Next, a training algorithm is derived from the cost function in a similar manner as the back-propagation algorithm. Last, we examine the ability of our approach by computer simulations on numerical examples. Simulation results show that the proposed algorithm is able to reduce the outlier effects.

关键词： fuzzy regression fuzzy neural networks back-propagation training algorithm outlier problem

来源：评论

学校读者我要写书评

暂无评论

Adaptive fuzzy modeling versus artificial neural networks

引用

ENVIRONMENTAL MODELLING & SOFTWARE 2008年第2期23卷 215-224页

作者： Wieland, Ralf Mirschel, Wilfried Inst Landscape Syst Anal Leibniz Ctr Agr Landscape Res D-15374 Muencheberg Germany

In this paper two areas of soft computing (fuzzy modeling and artificial neural networks) are discussed. Based on the fundamental mathematical similarity of fuzzy techniques and radial basis function networks a new training algorithm for fuzzy models is introduced. A feed forward neural network (NN), a radial basis function network (RBF) and a trained fuzzy algorithm are compared for regional yield estimation of agricultural crops (winter rye, winter barley). As training pattern a data set from a training region (Maerkisch-Oderland district, Germany) and as test pattern a data set from a three times larger region were used. Specific advantages and disadvantages of these methods for the estimation of yield were discussed. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： fuzzy modeling artificial neural network feed forward network radial basis function network training algorithm yield estimation agricultural crops

来源：评论

学校读者我要写书评

暂无评论

The No-Prop algorithm: A new learning algorithm for multilayer neural networks

引用

NEURAL NETWORKS 2013年 37卷 180-186页

作者： Widrow, Bernard Greenblatt, Aaron Kim, Youngsik Park, Dookun Stanford Univ Dept Elect Engn ISL Stanford CA 94305 USA

A new learning algorithm for multilayer neural networks that we have named No-Propagation (No-Prop) is hereby introduced. With this algorithm, the weights of the hidden-layer neurons are set and fixed with random values. Only the weights of the output-layer neurons are trained, using steepest descent to minimize mean square error, with the LMS algorithm of Widrow and Hoff. The purpose of introducing nonlinearity with the hidden layers is examined from the point of view of Least Mean Square Error Capacity (LMS Capacity), which is defined as the maximum number of distinct patterns that can be trained into the network with zero error. This is shown to be equal to the number of weights of each of the output-layer neurons. The No-Prop algorithm and the Back-Prop algorithm are compared. Our experience with No-Prop is limited, but from the several examples presented here, it seems that the performance regarding training and generalization of both algorithms is essentially the same when the number of training patterns is less than or equal to LMS Capacity. When the number of training patterns exceeds Capacity, Back-Prop is generally the better performer. But equivalent performance can be obtained with No-Prop by increasing the network Capacity by increasing the number of neurons in the hidden layer that drives the output layer. The No-Prop algorithm is much simpler and easier to implement than Back-Prop. Also, it converges much faster. It is too early to definitively say where to use one or the other of these algorithms. This is still a work in progress. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Neural networks training algorithm Backpropagation

来源：评论

学校读者我要写书评

暂无评论

A New Multiplicative Seasonal Neural Network Model Based on Particle Swarm Optimization

引用

NEURAL PROCESSING LETTERS 2013年第3期37卷 251-262页

作者： Aladag, Cagdas Hakan Yolcu, Ufuk Egrioglu, Erol Hacettepe Univ Fac Sci Dept Stat TR-06800 Ankara Turkey Giresun Univ Fac Arts & Sci Dept Stat TR-28000 Giresun Turkey Ondokuz Mayis Univ Fac Arts & Sci Dept Stat TR-55139 Samsun Turkey

In recent years, artificial neural networks (ANNs) have been commonly used for time series forecasting by researchers from various fields. There are some types of ANNs and feed forward neural networks model is one of them. This type has been used to forecast various types of time series in many implementations. In this study, a novel multiplicative seasonal ANN model is proposed to improve forecasting accuracy when time series with both trend and seasonal patterns is forecasted. This neural networks model suggested in this study is the first model proposed in the literature to model time series which contain both trend and seasonal variations. In the proposed approach, the defined neural network model is trained by particle swarm optimization. In the training process, local minimum traps are avoided by using this population based heuristic optimization method. The performance of the proposed approach is examined by using two real seasonal time series. The forecasts obtained from the proposed method are compared to those obtained from other forecasting techniques available in the literature. It is seen that the proposed forecasting model provides high forecasting accuracy.

关键词： Feed forward neural networks Forecasting Multiplicative neuron model Particle swarm optimization Time series training algorithm

来源：评论

学校读者我要写书评

暂无评论

Environment prediction for a mobile robot in a dynamic environment

引用

IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION 1997年第6期13卷 862-872页

作者： Chang, CC Song, KT Natl Chiao Tung Univ Dept Control Engn Hsinchu Taiwan Natl Chiao Tung Univ Dept Elect & Control Engn Hsinchu Taiwan

The problem of navigating a mobile robot among moving obstacles is usually solved on the condition of knowing the velocity of obstacles. However, it is difficult to provide such information to a robot in real time. In this paper, we present an environment predictor that provides an estimate of future environment configuration by fusing multisensor data in real time. The predictor is implemented by an artificial neural network (ANN) trained using a relative-error-backpropagation (REBP) algorithm. The REBP algorithm enables the ANN to provide output data with a minimum relative error, which is better than conventional backpropagation (BP) algorithms in this prediction application. The mobile robot can, therefore, respond to anticipated changes in the environment. The performance is verified by prediction simulation and navigation experiments.

关键词： artificial neural networks environment prediction mobile robots moving obstacles training algorithm

来源：评论

学校读者我要写书评

暂无评论

BASS: Broad Network Based on Localized Stochastic Sensitivity

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第2期35卷 1681-1695页

作者： Wang, Ting Zhang, Mingyang Zhang, Jianjun Ng, Wing W. Y. Chen, C. L. Philip South China Univ Technol Sch Med Guangzhou Peoples Hosp 1 Dept Radiol Guangzhou 510006 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangdong Prov Key Lab Computat Intelligence & Cy Guangzhou 510006 Peoples R China Pazhou Lab Brain & Affect Cognit Res Ctr Guangzhou 510335 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Dalian Maritime Univ Nav Coll Dalian 116026 Peoples R China

The training of the standard broad learning system (BLS) concerns the optimization of its output weights via the minimization of both training mean square error (MSE) and a penalty term. However, it degrades the generalization capability and robustness of BLS when facing complex and noisy environments, especially when small perturbations or noise appear in input data. Therefore, this work proposes a broad network based on localized stochastic sensitivity (BASS) algorithm to tackle the issue of noise or input perturbations from a local perturbation perspective. The localized stochastic sensitivity (LSS) prompts an increase in the network's noise robustness by considering unseen samples located within a Q-neighborhood of training samples, which enhances the generalization capability of BASS with respect to noisy and perturbed data. Then, three incremental learning algorithms are derived to update BASS quickly when new samples arrive or the network is deemed to be expanded, without retraining the entire model. Due to the inherent superiorities of the LSS, extensive experimental results on 13 benchmark datasets show that BASS yields better accuracies on various regression and classification problems. For instance, BASS uses fewer parameters (12.6 million) to yield 1% higher Top-1 accuracy in comparison to AlexNet (60 million) on the large-scale ImageNet (ILSVRC2012) dataset.

关键词： Broad learning system (BLS) generalization error localized stochastic sensitivity (LSS) training algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：