Multilayer Extreme Learning Machine Research Articles

Research of Bearing Fault Diagnosis Method Based on Multi-Layer Extreme Learning Machine Optimized by Novel Ant Lion Algorithm

In this paper, a novel bearing fault diagnosis method based on multi-layer extreme learning machine (MELM) optimized by the novel ant lion algorithm (NALO) is proposed. First, using permutation entropy of different scales (MPE) to extract fault features of bearings, a group of fault feature vectors composed of permutation entropy is obtained. Then, the fault feature vectors are classified by the MELM. However, with the increase of the number of hidden layers, the random input weight and bias will also increase, which will lead to the increase of the randomness of the MELM and affect the accuracy of fault diagnosis. Therefore, this paper uses the NALO to optimize the MELM. For the NALO, opposite populations are added to the initial population to improve its global search ability. When the ant lion updates its location, the influence of pheromones left by other ants with a certain sensing distance is taken into account to prevent the ant lion from falling into the local optimal and increased the robustness. Finally, the NALO-MELM and other bearing fault diagnosis methods are applied to the bearing fault experiment of Western Reserve University to test the performance and generalization of the proposed method.

Suitability and importance of deep learning feature space in the domain of text categorisation

One of the important features of multilayer ELM (ML-ELM) is its capability of nonlinearly mapping the features to an extended dimensional space and thereby builds the input features linearly separable. This paper studies the significance of deep learning feature space using ML-ELM for classification of text data which is the follow-up of my earlier approach. The previous approach discusses a new feature selection technique named combined cohesion separation and silhouette coefficient (CCSS) to generate a good feature vector and then used it for classification of text data using ML-ELM, which is a deep learning classifier. That approach is extended here which has two main aspects. The first aspect is to compare the performance of CCSS approach with the traditional feature selection techniques and the second aspect is to test the performance of different conventional classification techniques on the higher dimensional feature space of ML-ELM. Results of the experiment on different benchmark datasets justify that the proposed CCSS technique is comparable with the existing feature selection techniques and the ML-ELM feature space is more promising compared to the traditional TF-IDF vector space for classification of text data.

Suitability and importance of deep learning feature space in the domain of text categorisation

One of the important features of multilayer ELM (ML-ELM) is its capability of nonlinearly mapping the features to an extended dimensional space and thereby builds the input features linearly separable. This paper studies the significance of deep learning feature space using ML-ELM for classification of text data which is the follow-up of my earlier approach. The previous approach discusses a new feature selection technique named combined cohesion separation and silhouette coefficient (CCSS) to generate a good feature vector and then used it for classification of text data using ML-ELM, which is a deep learning classifier. That approach is extended here which has two main aspects. The first aspect is to compare the performance of CCSS approach with the traditional feature selection techniques and the second aspect is to test the performance of different conventional classification techniques on the higher dimensional feature space of ML-ELM. Results of the experiment on different benchmark datasets justify that the proposed CCSS technique is comparable with the existing feature selection techniques and the ML-ELM feature space is more promising compared to the traditional TF-IDF vector space for classification of text data.

Multilayer Incremental Hybrid Cost-Sensitive Extreme Learning Machine With Multiple Hidden Output Matrix and Subnetwork Hidden Nodes

Recently, multilayer extreme learning machine with subnetwork nodes and cost sensitive learning was applied to representation learning and classification. However, the existing ML-ELM methods suffer from several drawbacks: 1) manual tuning for the number of hidden nodes in each layer will influent the training speed of the model, the best optimal determine approach for the number of hidden nodes is still for probing; 2) random projection of the input weight and bias in each layer still suboptimal and the network structure hard to reach best generalization performance. Inspired by the Subnetwork nodes and cost sensitive methods, using multiple hidden output matrix, a novel ELM method which called Multilayer Incremental Hybrid Cost Sensitive Extreme Learning Machine with Multiple Hidden Output Matrix and Subnetwork Hidden Nodes is proposed in this paper. Whose contributions are 1) a hybrid hidden nodes optimization methods is given which based on ant clone method and multiple grey wolf optimization method; 2) a multiple hidden layer output matrices are being using through weighted calculation of different output matrices which can optimize the network architecture effectively; 3) a novel ELM structure is proposed by merging the merits of Cost Sensitive Extreme Learning Machine and Subnetwork Hidden Nodes, while the regularized parameter $C$ is not sensitive to the generalization performance. Extensive experiments results show that the proposed method achieves better performance than existing state-of-art ELM methods.

Imbalanced biomedical data classification using self-adaptive multilayer ELM combined with dynamic GAN

BackgroundImbalanced data classification is an inevitable problem in medical intelligent diagnosis. Most of real-world biomedical datasets are usually along with limited samples and high-dimensional feature. This seriously affects the classification performance of the model and causes erroneous guidance for the diagnosis of diseases. Exploring an effective classification method for imbalanced and limited biomedical dataset is a challenging task.MethodsIn this paper, we propose a novel multilayer extreme learning machine (ELM) classification model combined with dynamic generative adversarial net (GAN) to tackle limited and imbalanced biomedical data. Firstly, principal component analysis is utilized to remove irrelevant and redundant features. Meanwhile, more meaningful pathological features are extracted. After that, dynamic GAN is designed to generate the realistic-looking minority class samples, thereby balancing the class distribution and avoiding overfitting effectively. Finally, a self-adaptive multilayer ELM is proposed to classify the balanced dataset. The analytic expression for the numbers of hidden layer and node is determined by quantitatively establishing the relationship between the change of imbalance ratio and the hyper-parameters of the model. Reducing interactive parameters adjustment makes the classification model more robust.ResultsTo evaluate the classification performance of the proposed method, numerical experiments are conducted on four real-world biomedical datasets. The proposed method can generate authentic minority class samples and self-adaptively select the optimal parameters of learning model. By comparing with W-ELM, SMOTE-ELM, and H-ELM methods, the quantitative experimental results demonstrate that our method can achieve better classification performance and higher computational efficiency in terms of ROC, AUC, G-mean, and F-measure metrics.ConclusionsOur study provides an effective solution for imbalanced biomedical data classification under the condition of limited samples and high-dimensional feature. The proposed method could offer a theoretical basis for computer-aided diagnosis. It has the potential to be applied in biomedical clinical practice.

Prediction model of hot metal temperature for blast furnace based on improved multi-layer extreme learning machine

In the blast furnace production site, the disposable thermocouple is used to measure the hot metal temperature. However, this method is not only inconvenient for continuous data acquisition but also costly for the use of one-time thermocouple. Hence, this paper establishes a prediction model to predict the hot metal temperature. Before the prediction model is established, the corresponding factors of influencing the hot metal temperature are selected, and the noises of production data are removed. In this paper, multi-layer extreme learning machine (ML-ELM) is used as the prediction algorithm of the prediction model. However, the input weights, hidden layer weights and hidden biases of ML-ELM are randomly selected, and the solution of the output weights is based on them, which makes ML-ELM inevitably have a set of non-optimal or unnecessary weights and biases. In addition, ML-ELM may suffer from over-fitting problem. Hence, this paper uses the adaptive particle swarm optimization (APSO) and the ensemble model to improve ML-ELM, and the improved algorithm is named as EAPSO-ML-ELM. APSO can optimize the selections of the input weights, hidden layer weights and hidden biases, the ensemble model can alleviate the over-fitting problem, i.e., this paper combines several of the optimized ML-ELMs which have different input weights, hidden layer weights and hidden biases. Finally, this paper also uses other algorithms to establish the prediction model, and simulation results demonstrate that the prediction model based on EAPSO-ML-ELM has better prediction accuracy and generalization performance.

Region-Enhanced Multi-layer Extreme Learning Machine

Deep neural networks have made significant achievements in representation learning of traditionally man-made features, especially in terms of complex objects. Over the decades, this learning process has attracted thousands of researchers and has been widely used in the speech, visual, and text recognition fields. One deep network multi-layer extreme learning machine (ML-ELM) achieves a good performance in representation learning while inheriting the advantages of faster learning and the approximating capability of the extreme learning machine (ELM). However, as with most deep networks, the ML-ELM’s algorithmic performance largely depends on the probability distribution of the training data. In this paper, we propose an improved ML-ELM made via using the local significant regions at the input end to enhance the contributions of these regions according to the idea of the selective attention mechanism. To avoid involving and exploring the complex principle of the attention system and to focus on the clarification of our local regional enhancement idea, the paper only selects two typical attention regions. One is the geometric central region, which is normally the important region to attract human attention due to the focal attention mechanism. The other is the task-driven interest region, with facial recognition as an example. The comprehensive experiments are done on the three public datasets of MNIST, NORB, and ORL. The comparison experiment results demonstrate that our proposed region-enhanced ML-ELM (RE-ML-ELM) achieves performance increases in important feature learning by utilizing the apriori knowledge of attention and has a higher recognition rate than that of the normal ML-ELM and the basic ELM. Moreover, it benefits from the non-iterative parameter training method of other ELMs, and our proposed algorithm outperforms most state-of-the-art deep networks such as deep belief network(DBN), in the aspects of training efficiency. Furthermore, because of the deep structure with fewer hidden nodes at each layer, our proposed RE-ML-ELM achieves a comparable training efficiency to that of the ML-ELM but has a higher training speed with the basic ELM, which is normally the width single network that has more hidden nodes to obtain the similar recognition accuracy with the deep networks. Based on our idea of combining the apriori knowledge of the human selective attention system with the data learning, our proposed region-enhanced ML-ELM increases the image classification performance. We believe that the idea of intentionally combining psychological knowledge with the most algorithms based on data-driven learning has the potential to improve their cognitive computing ability.

A novel deep output kernel learning method for bearing fault structural diagnosis

Abstract In recent years, machine learning techniques have been proved a promising tool for bearing fault diagnosis. However, in the traditional machine learning-based diagnosis methods, the fault features tend to be relatively simple and couldn’t work well for different fault type once a specific feature extraction method is determined. Meanwhile, although deep learning techniques can adaptively extract more representative features from bearing fault data, they are generally computationally expensive with slow convergence speed. Even if some deep learning algorithms like Multi-Layer Extreme Learning Machine (ML-ELM) can get fast training speed by means of non-tuned training strategy, they are inevitably of randomness to some extents. To solve this problem, a new deep learning method called deep output kernel learning is proposed in this paper to conduct collaborative diagnosis of multiple bearing fault types. The initial motivation is using the structural domain information among multiple bearing fault types to improve the diagnosis model’s generalization ability and robustness. By adopting ML-ELM as baseline algorithm, this paper firstly utilizes autoencoder to adaptively extract deep features, and then uses them to construct an objective function with output kernel regularizer. Finally, after solving this optimization problem, an output kernel matrix is obtained, and with this matrix, the final diagnosis model is built by fusing the multiple outputs of fault classifier. Experimental results on CWRU and IMS bearing data sets show that, compared to one state-of-the-art signal analysis method and eight typical machine learning-based diagnosis methods including four shallow learning algorithms and four deep learning algorithms, the proposed method can effectively improve the accuracy of bearing fault diagnosis in an acceptable time. Moreover, the results from the Kruskal-Wallis Test also indicate the proposed method has good numerical stability.

Correntropy-based robust multilayer extreme learning machines

In extreme learning machines (ELM), the hidden node parameters are randomly generated and the output weights can be analytically computed. To overcome the bad feature extraction ability of the shallow architecture of ELM, the hierarchical ELM has been extensively studied as a deep architecture with multilayer neural network. However, the commonly used mean square error (MSE) criterion is very sensitive to outliers and impulsive noises, generally existing in real world data. In this paper, we investigate the correntropy to improve the robustness of the multilayer ELM and provide sparser representation. The correntropy, as a nonlinear measure of similarity, is robust to outliers and can approximate different norms (from ℓ0 to ℓ2). A new full correntropy based multilayer extreme learning machine (FC-MELM) algorithm is proposed to handle the classification of datasets which are corrupted by impulsive noises or outliers. The contributions of this paper are three-folds: (1) The MSE based reconstruction loss is replaced by the correntropy based loss function; In this way, the robustness of the ELM based multilayer algorithms is enhanced. (2) The traditional ℓ1-based sparsity penalty term is also replaced by a correntropy-based sparsity penalty term, which can further improve the performance of the proposed algorithm with a sparser representation of the data. The combination of (1) and (2) provides the correntropy-based ELM autoencoder. (3) The FC-MELM is proposed by using the correntropy-based ELM autoencoder as a building block. It is notable that the FC-MELM is trained in a forward manner, which means fine-tuning procedure is not required. Thus, the FC-MELM has great advantage in learning efficiently when compared with traditional deep learning algorithms. The good property of the proposed algorithm is confirmed by the experiments on well-known benchmark datasets, including the MNIST datasets, the NYU Object Recognition Benchmark dataset, and the Moore network traffic dataset. Finally, the proposed FC-MELM algorithm is applied to address Computer Aided Cancer Diagnosis. Experiments conducted on the well-known Wisconsin Breast Cancer Data (Diagnostic) dataset are presented and show that the proposed FC-MELM outperforms state-of-the-art methods in solving computer aided cancer diagnosis problems.

Urban noise recognition with convolutional neural network

Urban noise recognition play a vital role in city management and safety operation, especially in the recent smart city engineering. Exiting studies on urban noise recognition are mostly based on conventional acoustic features, such as Mel-Frequency Cepstral Coefficients (MFCC) and Linear Prediction Cepstral Coefficients (LPCC), and the shallow structure based classifiers, such as support vector machine (SVM). However, the urban acoustic environment is complicated and changeable. Conventional acoustic representation and recognition methods may be insufficient in characterizing urban noises, and generally suffer from a degraded performance. In this paper, we study the recent deep neural network based urban noise recognition. The log-Mel-spectrogram, namely, the FBank feature is first derived for acoustic representation. Then, the FBank spectrum constructed with a set of FBank feature vectors from multiple acoustic signal frames is fed to a convolutional neural network (CNN) for urban noise recognition. Comprehensive studies on the dimension of FBank spectrums and the parameters in CNN, including the size of learnable kernels, the dropout rate, and the activation function, etc., are presented in the paper. An acoustic database collected in real environment covering 11 most common urban noises with more than 56,000 samples is constructed for model verification and performance evaluation. In addition, the traditional LPCC and MFCC acoustic feature combining with two popular machine learning algorithms, extreme learning machine (ELM) and support vector machine (SVM), and the FBank image feature combining with extreme learning machine (ELM), hierarchical extreme learning machine (H-ELM) and multilayer extreme learning machine (ML-ELM), have also been presented for discussions. Experimental results show that the proposed method generally outperforms conventional shallow structure based classifiers.

Neural-Response-Based Extreme Learning Machine for Image Classification.

This paper proposes a novel and simple multilayer feature learning method for image classification by employing the extreme learning machine (ELM). The proposed algorithm is composed of two stages: the multilayer ELM (ML-ELM) feature mapping stage and the ELM learning stage. The ML-ELM feature mapping stage is recursively built by alternating between feature map construction and maximum pooling operation. In particular, the input weights for constructing feature maps are randomly generated and hence need not be trained or tuned, which makes the algorithm highly efficient. Moreover, the maximum pooling operation enables the algorithm to be invariant to certain transformations. During the ELM learning stage, elastic-net regularization is proposed to learn the output weight. Elastic-net regularization helps to learn more compact and meaningful output weight. In addition, we preprocess the input data with the dense scale-invariant feature transform operation to improve both the robustness and invariance of the algorithm. To evaluate the effectiveness of the proposed method, several experiments are conducted on three challenging databases. Compared with the conventional deep learning methods and other related ones, the proposed method achieves the best classification results with high computational efficiency.

Autoencoder With Invertible Functions for Dimension Reduction and Image Reconstruction

The extreme learning machine (ELM), which was originally proposed for “generalized” single-hidden layer feedforward neural networks, provides efficient unified learning solutions for the applications of regression and classification. Although, it provides promising performance and robustness and has been used for various applications, the single-layer architecture possibly lacks the effectiveness when applied for natural signals. In order to over come this shortcoming, the following work indicates a new architecture based on multilayer network framework. The significant contribution of this paper are as follows: 1) unlike existing multilayer ELM, in which hidden nodes are obtained randomly, in this paper all hidden layers with invertible functions are calculated by pulling the network output back and putting it into hidden layers. Thus, the feature learning is enriched by additional information, which results in better performance; 2) in contrast to the existing multilayer network methods, which are usually efficient for classification applications, the proposed architecture is implemented for dimension reduction and image reconstruction; and 3) unlike other iterative learning-based deep networks (DL), the hidden layers of the proposed method are obtained via four steps. Therefore, it has much better learning efficiency than DL. Experimental results on 33 datasets indicate that, in comparison to the other existing dimension reduction techniques, the proposed method performs competitively better with fast training speeds.

Radar emitter identification with bispectrum and hierarchical extreme learning machine

Radar Emitter Identification (REI) has been broadly used in military and civil fields. In this paper, a novel method is proposed for radar emitter signal identification, where the bispectrum estimation of radar signal is extracted and the recent hierarchical extreme learning machine (BS + H-ELM) is adopted for further feature learning and recognition. Conventional REI methods generally rely on the time-difference-of-arrival, carrier frequency, pulse width, pulse amplitude, direction-of-arrival, etc., for signal representation and recognition. However, the increasingly violent electronic confrontation and the emergence of new types of radar signals generally degrade the recognition performance. With this objective, we explore radar emitter signal representation and classification method with the high order spectrum and deep network based H-ELM. After extracting the bispectrum of radar signals, the sparse autoencoder (AE) in H-ELM is employed for feature learning. Simulations on four representative radar signals, namely, the continuous wave (CW), linear frequency modulation wave(LFM), nonlinear frequency modulation wave(NLFM) and binary phase shift keying wave (BPSK), are conducted for performance validation. In comparison to the existing multilayer ELM algorithm and the popular histogram of gradient (HOG) based feature extraction method are proved that the proposal is feasible and potentially applicable in real applications.

Empirical kernel map-based multilayer extreme learning machines for representation learning

Abstract Recently, multilayer extreme learning machine (ML-ELM) and hierarchical extreme learning machine (H-ELM) were developed for representation learning whose training time can be reduced from hours to seconds compared to traditional stacked autoencoder (SAE). However, there are three practical issues in ML-ELM and H-ELM: (1) the random projection in every layer leads to unstable and suboptimal performance; (2) the manual tuning of the number of hidden nodes in every layer is time-consuming; and (3) under large hidden layer, the training time becomes relatively slow and a large storage is necessary. More recently, issues (1) and (2) have been resolved by kernel method, namely, multilayer kernel ELM (ML-KELM), which encodes the hidden layer in form of a kernel matrix (computed by using kernel function on the input data), but the storage and computation issues for kernel matrix pose a big challenge in large-scale application. In this paper, we empirically show that these issues can be alleviated by encoding the hidden layer in the form of an approximate empirical kernel map (EKM) computed from low-rank approximation of the kernel matrix. This proposed method is called ML-EKM-ELM, whose contributions are: (1) stable and better performance is achieved under no random projection mechanism; (2) the exhaustive manual tuning on the number of hidden nodes in every layer is eliminated; (3) EKM is scalable and produces a much smaller hidden layer for fast training and low memory storage, thereby suitable for large-scale problems. Experimental results on benchmark datasets demonstrated the effectiveness of the proposed ML-EKM-ELM. As an illustrative example, on the NORB dataset, ML-EKM-ELM can be respectively up to 16 times and 37 times faster than ML-KELM for training and testing with a little loss of accuracy of 0.35%, while the memory storage can be reduced up to 1/9.

Data-driven prediction model for adjusting burden distribution matrix of blast furnace based on improved multilayer extreme learning machine

Reasonable burden distribution matrix is one of important requirements that can realize low consumption, high efficiency, high quality and long campaign life of the blast furnace. This paper proposes a data-driven prediction model of adjusting the burden distribution matrix based on the improved multilayer extreme learning machine (ML-ELM) algorithm. The improved ML-ELM algorithm is based on our previously modified ML-ELM algorithm (named as PLS-ML-ELM) and the ensemble model. It is named as EPLS-ML-ELM. The PLS-ML-ELM algorithm uses the partial least square (PLS) method to improve the algebraic property of the last hidden layer output matrix for the ML-ELM algorithm. However, the PLS-ML-ELM algorithm may have different results in different trails of simulations. The ensemble model can overcome this problem. Moreover, it can improve the generalization performance. Hence, the EPLS-ML-ELM algorithm is consisted of several PLS-ML-ELMs. The real blast furnace data are used to testify the data-driven prediction model. Compared with other prediction models which are based on the SVM algorithm, the ELM algorithm, the ML-ELM algorithm and the PLS-ML-ELM algorithm, the simulation results demonstrate that the data-driven prediction model based on the EPLS-ML-ELM algorithm has better prediction accuracy and generalization performance.

Weakly paired multimodal fusion using multilayer extreme learning machine

Multimodal data have recently become nearly ubiquitous in the real world. Exploring the multimodal fusion is beneficial to improve the performance of the system. However, it is difficult to ensure that data collected from different sources are full pairing. In this paper, we will focus on the weakly paired case of multimodal data, i.e., each modality is partitioned into multiple groups, only paired information on groups is known instead of full pairing between data samples. A new framework of weakly paired multimodal fusion based on multilayer extreme learning machine (ML-ELM) is proposed in this paper, which will find complex nonlinear transformations of each modality of data such that the resulting representations are highly correlated. In this framework, unsupervised hierarchical ELM performs feature extraction for all modalities separately. Then, the higher-level representations from all modalities perform joint dimension reduction by weakly paired maximum covariance analysis. We evaluate our framework on three challenging cross-modal datasets, and the results have proved the effectiveness of proposed method.

Detecting spam web pages using multilayer extreme learning machine

Web spamming generally increases the ranking of some unimportant pages higher in the search results. Detecting and eliminating such spam pages are the need of the day, which mislead the search engine to obtain high-quality information. Aiming in this direction, this study focuses on two important aspects of machine learning. First, it proposes a new content-based spam detection technique which identifies nine important features that help to detect a page is either spam or non-spam. Each feature has an associated value which is calculated by parsing the documents and then performing the require techniques i.e. necessary steps to compute its score. These nine important features along with the class label (spam or non-spam) generate a feature vector for training the classifiers in order to detect the spam pages. Secondly, it highlights the importance of deep learning using multilayer extreme learning machine in the field of spam page detection. For experimental work, two benchmark datasets (WEBSPAM-UK2002 and WEBSPAM-UK2006) have been used and the results using multilayer ELM are found to be more promising compared to other established classifiers.

Coal Exploration Based on a Multilayer Extreme Learning Machine and Satellite Images

The demand for coal has been on the rise in modern society. With the number of opencast coal mines decreasing, it has become increasingly difficult to find coal. Low efficiencies and high casualty rates have always been problems in the process of coal exploration due to complicated geological structures in coal mining areas. Therefore, we propose a new exploration technology for coal that uses satellite images to explore and monitor opencast coal mining areas. First, we collected bituminous coal and lignite from the Shenhua opencast coal mine in China in addition to non-coal objects, including sandstones, soils, shales, marls, vegetation, coal gangues, water, and buildings. Second, we measured the spectral data of these objects through a spectrometer. Third, we proposed a multilayer extreme learning machine algorithm and constructed a coal classification model based on that algorithm and the spectral data. The model can assist in the classification of bituminous coal, lignite, and non-coal objects. Fourth, we collected Landsat 8 satellite images for the coal mining areas. We divided the image of the coal mine using the constructed model and correctly described the distributions of bituminous coal and lignite. Compared with the traditional coal exploration method, our method manifested an unparalleled advantage and application value in terms of its economy, speed, and accuracy.

Fabric Weave Pattern and Yarn Color Recognition and Classification Using a Deep ELM Network

Usually, a fabric weave pattern is recognized using methods which identify the warp floats and weft floats. Although these methods perform well for uniform or repetitive weave patterns, in the case of complex weave patterns, these methods become computationally complex and the classification error rates are comparatively higher. Furthermore, the fault-tolerance (invariance) and stability (selectivity) of the existing methods are still to be enhanced. We present a novel biologically-inspired method to invariantly recognize the fabric weave pattern (fabric texture) and yarn color from the color image input. We proposed a model in which the fabric weave pattern descriptor is based on the HMAX model for computer vision inspired by the hierarchy in the visual cortex, the color descriptor is based on the opponent color channel inspired by the classical opponent color theory of human vision, and the classification stage is composed of a multi-layer (deep) extreme learning machine. Since the weave pattern descriptor, yarn color descriptor, and the classification stage are all biologically inspired, we propose a method which is completely biologically plausible. The classification performance of the proposed algorithm indicates that the biologically-inspired computer-aided-vision models might provide accurate, fast, reliable and cost-effective solution to industrial automation.

Prediction model of permeability index for blast furnace based on the improved multi-layer extreme learning machine and wavelet transform

The permeability index of the blast furnace is a significant symbol to measure the smooth operation of the blast furnace. This paper proposes a novel prediction model for permeability index of the blast furnace based on the multi-layer extreme learning machine (ML-ELM), the principal component analysis (PCA) method and wavelet transform (called as W-PCA-ML-ELM prediction model). This modified ML-ELM algorithm is based on the ML-ELM algorithm and the PCA method (named as PCA-ML-ELM). The PCA method is applied on the ML-ELM algorithm to improve the algebraic property of the last hidden layer output matrix which deteriorates its generalization performance due to the high multicollinearity. Because the production data of the blast furnace field contain noises, this paper applies the wavelet transform to remove the noise. Comparing with other prediction models which are based on the ML-ELM, the ELM, the BP and the SVM, simulation results illustrate that the better generalization performance and stability of the proposed W-PCA-ML-ELM prediction model.