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stacked autoencoders and extreme learning machine based hybrid model for electrical load prediction

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第4期37卷 5403-5416页

作者： Peng, Wei Xu, Liwen Li, Chengdong Xie, Xiuying Zhang, Guiqing Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan 250101 Shandong Peoples R China Shandong Coinnovat Ctr Green Bldg Jinan 250101 Shandong Peoples R China

Electrical load prediction plays an important role in power system management and economic development. However, because electrical load has non-linear relationships with several factors such as the political environment, the economic policy, the human activities, the irregular behaviors and the other factors, it is quite difficult to predict power load accurately. In order to further improve the electrical load forecasting performance, a hybrid model is proposed in this paper. The proposed hybrid model combines the stacked autoencoders (SAE) and extreme learning machines (ELMs) to learn the characteristics of the time series data of electrical load. In this proposed method, in order to utilize the characteristics of the electrical load in different depths, the outputs of each layer of the SAE are taken as the inputs of one specific ELM. Then, the obtained results from the constructed different ELMs are integrated by the linear regression to obtain the final output. The linear regression part is trained by the least square estimation method. In addition, the hybrid model is applied to predict two real-world electrical load time series. And, detailed comparisons with the SAE, ELM, the back propagation neural network (BPNN), the multiple linear regression (MLR) and the support vector regression (SVR) are done to show the advantages of the proposed forecasting model. Experimental and comparison results demonstrate that the proposed hybrid model can achieve much better performance than the comparative methods in electrical load forecasting application.

关键词： electrical load prediction hybrid model stacked autoencoder extreme learning machine

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stacked autoencoders Using Low-Power Accelerated Architectures for Object Recognition in Autonomous Systems

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NEURAL PROCESSING LETTERS 2016年第2期43卷 445-458页

作者： Maria, Joao Amaro, Joao Falcao, Gabriel Alexandre, Luis A. Univ Coimbra Inst Telecomunicacoes Dept Elect & Comp Engn P-3030290 Coimbra Portugal Univ Beira Interior Dept Informat P-6201001 Covilha Portugal Univ Beira Interior Inst Telecomunicacoes P-6201001 Covilha Portugal

This paper investigates low-energy consumption and low-power hardware models and processor architectures for performing the real-time recognition of objects in power-constrained autonomous systems and robots. Most recent developments show that convolutional deep neural networks are currently the state-of-the-art in terms of classification accuracy. In this article we propose to use of a different type of deep neural network-stacked autoencoders-and show that within a limited number of layers and nodes, for accommodating the use of low-power accelerators such as mobile GPUs and FPGAs, we are still able to achieve both classification levels not far from the state-of-the-art and a high number of processed frames per second. We present experiments using the color CIFAR-10 dataset. This enables the adaptation of the architecture to a live feed camera. Another novelty equally proposed for the first time in this work suggests that the training phase can also be performed in these low-power devices, instead of the usual approach that uses a desktop CPU or a GPU to perform this task and only runs the trained network later on the FPGA. This allows incorporating new functionalities as, for example, a robot performing runtime learning.

关键词： Deep learning Neural networks stacked autoencoder Parallel computing FPGAs Mobile GPUs OpenCL Low-power Autonomous systems

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HYPERSPECTRAL IMAGE CLASSIFICATION BASED ON stacked MARGINAL DISCRIMINATIVE autoencoder 37

HYPERSPECTRAL IMAGE CLASSIFICATION BASED ON STACKED MARGINAL...

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IEEE International Geoscience & Remote Sensing Symposium

作者： Feng, Jie Liu, Liguo Zhang, Xiangrong Wang, Rongfang Liu, Hongying Xidian Univ Key Lab Intelligent Percept & Image Undersatnding Minist Educ Xian 710071 Shaanxi Peoples R China

ISBN: (纸本)9781509049516

In this paper, a novel stacked marginal discriminative autoencoder (SMDAE) method is proposed for hyperspectral image classification. It uses a deep neural network to learn discriminative features from hyperspectral images automatically. In hyperspectral images, the collection of training samples is difficult. When the number of training samples is not enough, these training samples are difficult to estimate the statistical distribution of hyperspectral images accurately. In order to solve the small sample problem and improve the classification performance of the autoencoder, the marginal samples are selected through the distribution characteristics of samples. The marginal samples are searched based on k nearest neighbors between different classes. These samples are used to fine-tune the SMDAE network. The experimental results show that the proposed SMDAE method can achieve satisfying performance under small training set.

关键词： stacked autoencoder hyperspectral classification deep learning discriminative features marginal samples

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stacked autoencoderS FOR MULTICLASS CHANGE DETECTION IN HYPERSPECTRAL IMAGES 38

STACKED AUTOENCODERS FOR MULTICLASS CHANGE DETECTION IN HYPE...

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38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Lopez-Fandino, Javier Garea, Alberto S. Heras, Dora B. Arguello, Francisco Univ Santiago de Compostela Ctr Singular Invest Tecnol Informac CiTIUS Santiago De Compostela Spain

ISBN: (纸本)9781538671504

Change detection (CD) in multitemporal datasets is a key task in remote sensing. In this paper, a scheme to perform multiclass CD for remote sensing hyperspectral datasets extracting features by means of stacked autoencoders (SAEs) is introduced. The scheme combines multiclass and binary CD to obtain an accurate multiclass changemap. The multiclass CD begins with the fusion of the multitemporal data followed by Feature Extraction (FE) by SAEs. The binary CD is based on the spectral information by calculating pixel-wise distances and thresholding, and it also incorporates spatial information through watershed segmentation. The processed image is filtered by using the binary CD map and later classified by a Support VectorMachine or an Extreme LearningMachine algorithm. The scheme was evaluated over a multitemporal hyperspectral dataset obtained fromthe Hyperion sensor. Experimental results show the effectiveness of the proposed scheme using a SAE for extracting the relevant features of the fused information when compared to other published FE methods.

关键词： Hyperspectral Change Detection Feature Extraction stacked autoencoder Support Vector Machine Extreme Learning Machine

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Mutual Information-Weighted Principle Components Identified From the Depth Features of stacked autoencoders and Original Variables for Oil Dry Point Soft Sensor

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IEEE ACCESS 2019年 7卷 1981-1990页

作者： Wang, Jie Yan, Xuefeng East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China

In modern chemical process control, the application of data-driven soft sensor has become increasingly extensive. Feature extraction is an important step in soft sensor. A novel feature extraction and integration method based on stacked autoencoders (SAE) and mutual information (MI)-weighted principle component analysis (PCA) was proposed to solve the loss of information on shallow depth features and original variables in neural network models. First, an SAE model was trained to extract the features of the original variables with varying depths. Second, through an MI indicator, the original variables and features with strong dependency on the outputs were selected. Then, MI was used to assign varied weights to the features and original variables, and the PCA method was used to remove any possible redundancy between the original variables and features of varying depths to obtain the principle components. Finally, the principle components were used to construct a regressor, such as a neural network. The model was first tested using the Boston housing dataset as a benchmark and then applied to the soft sensor of a constant top oil dry point. The proposed model achieved optimal results in terms of the root mean squared error and r indicators in the experiments and was thus proved feasible and useful.

关键词： Feature extraction mutual information soft sensor stacked autoencoder

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Cross-lingual sentiment classification with stacked autoencoders

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KNOWLEDGE AND INFORMATION SYSTEMS 2016年第1期47卷 27-44页

作者： Zhou, Guangyou Zhu, Zhiyuan He, Tingting Hu, Xiaohua Tony Cent China Normal Univ Sch Comp Wuhan 430079 Peoples R China Cent China Normal Univ Sch Comp Nat Language Proc Lab Wuhan 430079 Peoples R China Chinese Inst Elect Beijing 100036 Peoples R China Drexel Univ Coll Comp & Informat Philadelphia PA 19104 USA

Cross-lingual sentiment classification is a popular research topic in natural language processing. The fundamental challenge of cross-lingual learning stems from a lack of overlap between the feature spaces of the source language data and the target language data. In this article, we propose a new model which uses stacked autoencoders to learn language-independent high-level feature representations for the both languages in an unsupervised fashion. The proposed framework aims to force the aligned input bilingual sentences into a common latent space, and the objective function is defined by minimizing the input and output vector representations as well as the distance of the common representations in the latent space. Sentiment classifiers trained on the source language can be adapted to predict sentiment polarity of the target language with the language-independent high-level feature representations. We conduct extensive experiments on English-Chinese sentiment classification tasks of multiple data sets. Our experimental results demonstrate the efficacy of the proposed cross-lingual approach.

关键词： Sentiment classification Cross-lingual stacked autoencoder

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Micro-crack detection method of steel beam surface using stacked autoencoders on massive full-scale sensing strains

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2020年第4期19卷 1175-1187页

作者： Song, Qingsong Chen, Yu Oskoui, Elias Abdoli Fang, Zheng Taylor, Todd Tang, Guangwu Zhao, Xiangmo Ansari, Farhad Changan Univ Sch Informat Engn Xian Peoples R China Univ Illinois Dept Civil & Mat Engn 842 W Taylor St Chicago IL 60607 USA China Merchants Chongqing Commun Technol Res & De State Key Lab Bridge Engn Struct Dynam Chongqing Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing Peoples R China

Accurate micro-crack detections on the whole surface of civil structures have great significance. Distributed optical fiber sensor based on Brillouin optical time-domain analysis technology exhibits great facility to measure strain distributions along the whole surface of structures with a high spatial resolution, thus providing a potential and competitive solution to the detection problem. However, mainly due to low signal-to-noise ratio in measurements, such sensor system is still limited in crack detection-based structural health monitoring applications. How to extract high-quality micro-crack feature representations from the low signal-to-noise ratio-distributed strain measurements is crucial to solve the problem. It has been demonstrated in field of pattern recognition that deep learning can automatically extract high-quality noise-robust feature representations from mass chaos data. Therefore, a micro-crack detection method is proposed herein based on deep learning to analyze the full-scale strain measurements. Each measurement is normalized and segmented into a set of equal-length subsequences. autoencoders, a typical kind of building block of deep neural network, are stacked layer-wise into a deep network and then exploited to automatically extract feature representations from the subsequences. Each extracted feature representation is labeled as one of the two categories by a Softmax regression. One category originates in the subsequences acquired from structure sections with crack defects and another from sections without any cracks. The micro-crack detections are achieved by solving such a crack/non-crack binary classification problem. A 15-m-long steel I-beam with artifact crack defects is built up in laboratory to verify the proposed method. Experimental results demonstrate that the minimum size of detectable crack opening width reaches to 23 mu m, and besides, the proposed method is significantly better than traditional Fisher linear discriminant analys

关键词： Structural health monitoring crack detection pattern recognition deep learning feature representation stacked autoencoder Brillouin optical time-domain analyzer

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A Novel Prediction Method for Blast Furnace Gas Utilization Rate Based on Dynamic Weighted stacked Output-Relevant autoencoder

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STEEL RESEARCH INTERNATIONAL 2023年第5期94卷

作者： Jiang, Zhaohui Zhu, Jicheng Pan, Dong Yu, Haoyang Zhou, Ke Gui, Weihua Cent South Univ Sch Automat Changsha 410083 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China

In the blast furnace (BF) ironmaking process, the gas utilization rate (GUR) is a crucial indicator for reflecting the energy consumption and operating status of BF. However, due to the complex and harsh environment in the BF top, accurately obtaining GUR online is not an effortless task. Although many studies have been carried out to predict GUR through data-driven methods, some challenges still exist: 1) limited feature extraction capability for complex data patterns;2) prediction accuracy is sensitive to the fluctuation of BF working conditions. Therefore, a novel deep learning method is proposed based on dynamic weighted stacked output-relevant autoencoder (DW-SOAE) for GUR online prediction. First, the input layer variables for each AE are weighted according to their importance, which will reduce output-unrelated features. Then, the output variable is also reconstructed at the output layer of each AE, which ensures extracted features can largely predict GUR. Next, considering that the fluctuation of BF working conditions may affect prediction accuracy, the density peak clustering algorithm is used to cluster the process variables, and several DW-SOAE-based submodels are built for GUR prediction. Finally, the effectiveness and superiority of the proposed GUR prediction method are verified in industrial experiments.

关键词： blast furnace density peak clustering gas utilization rate prediction stacked autoencoder

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Adaptive parameter tuning stacked autoencoders for process monitoring

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SOFT COMPUTING 2020年第17期24卷 12937-12951页

作者： Kong, Diehao Yan, Xuefeng East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China Tongji Univ Shanghai Inst Intelligent Sci & Technol Shanghai 200237 Peoples R China

In process monitoring based on stacked autoencoders (SAEs), the performance of monitoring models is directly decided by the validity of the structure and parameters, which are primarily determined by time-consuming manual adjustments. This paper presents a novel method that can adaptively select parameters rather than tuning them manually. The proposed method is called adaptive parameter tuning SAE (APT-SAE). Basic SAEs aim to compress the original input data and extract simple and abstract features. Thus, the redundant information of each hidden layer output should be as small as possible. The next layer of nodes can be remarkably reduced if the amount of redundant information is large. During the pre-training stage of APT-SAE, an adaptive parameter tuning strategy is used for rapidly determining the number of layers and nodes in the paper. The cross-covariance of each AE's input data is used to determine the node number of succeeding AE. The pre-training stage ends when the correlation is weak, which is decided by the average value of cross-variance matrix. The proposed method is applied to a benchmark problem, and it outperforms several state-of-the-art methods.

关键词： Process monitoring autoencoder stacked autoencoder Adaptive parameter tuning

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A study of combination of autoencoders and boosted Big-Bang crunch theory architectures for Land-Use classification using remotely sensed imagery

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Scientific Reports 2025年第1期15卷 1-18页

作者： Xiong, Qiongbing Wu, Xuecheng Yu, Cizhen Hosseinzadeh, Hasan College of Tourism Management Guizhou University of Commerce Guiyang Guizhou 550014 China Ardabil Branch Islamic Azad University Ardabil Iran College of Technical Engineering The Islamic University Najaf Iraq

The research introduced a new method for land-use classification by merging deep convolutional neural networks with a modified variant of a metaheuristic optimization technique. The methodology involved utilizing the VGG-19 model for feature extraction, dimensionality reduction, and a stacked autoencoder optimized with a boosted version of the Big Bang Crunch Theory. Through testing on the Aerial Image Dataset the and UC Merced Land Use Dataset and comparing it with other published works, the approach showed higher classification accuracy compared to current state-of-the-art methods. The study revealed that incorporating boosted Big-Bang Crunch significantly enhances the performance of stacked autoencoder in land-use classification tasks. Moreover, comparisons with other techniques, including convolutional neural networks, Cascaded Residual Dilated Networks, hierarchical convolutional recurrent neural networks, Fusion Region Proposal Networks, and multi-level context-guided classification techniques using Object-Based Convolutional Neural Networks, emphasized the benefits of using Convolutional Neural Network models over traditional methods. The proposed model achieved an accuracy of 92.49% on the AID dataset and 95.93% on the UC Merced dataset, with precision scores of 98.64% and 98.93%, respectively. These results emphasize the importance of integrating deep learning architectures with sophisticated optimization techniques, contributing to enhanced land-use classification accuracy. © The Author(s) 2025.

关键词： Boosted Big-Bang crunch theory Deep learning Land-use classification Metaheuristic stacked autoencoder VGG-19

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