TFFN: Two hidden layer feed forward network using the randomness of extreme learning machine

Adhikari, Nimai Chand Das; Alka, Arpana; George, Raju K.

doi:10.1109/iss1.2017.8389298

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…In this algorithm, the weight and bias parameters of the input to the hidden layer are randomly assigned, and only the weights of the output layer are regulated [35][36][37]. Thus, it has fast learning and high accuracy [38][39][40], making it suitable for online learning. For details regarding the extreme learning algorithm, please refer to References [35,36].…”

Section: Elm-based Afl-slfnmentioning

confidence: 99%

ELM-Based AFL–SLFN Modeling and Multiscale Model-Modification Strategy for Online Prediction

et al. 2019

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Online prediction of key parameters (e.g., process indices) is essential in many industrial processes because online measurement is not available. Data-based modeling is widely used for parameter prediction. However, model mismatch usually occurs owing to the variation of the feed properties, which changes the process dynamics. The current neural network online prediction models usually use fixed activation functions, and it is not easy to perform dynamic modification. Therefore, a few methods are proposed here. Firstly, an extreme learning machine (ELM)-based single-layer feedforward neural network with activation-function learning (AFL-SLFN) is proposed. The activation functions of the ELM are adjusted to enhance the ELM network structure and accuracy. Then, a hybrid model with adaptive weights is established by using the AFL-SLFN as a sub-model, which improves the prediction accuracy. To track the process dynamics and maintain the generalization ability of the model, a multiscale model-modification strategy is proposed. Here, small-, medium-, and large-scale modification is performed in accordance with the degree and the causes of the decrease in model accuracy. In the small-scale modification, an improved just-in-time local modeling method is used to update the parameters of the hybrid model. In the medium-scale modification, an improved elementary effect (EE)-based Morris pruning method is proposed for optimizing the sub-model structure. Remodeling is adopted in the large-scale modification. Finally, a simulation using industrial process data for tailings grade prediction in a flotation process reveals that the proposed method has better performance than some state-of-the-art methods. The proposed method can achieve rapid online training and allows optimization of the model parameters and structure for improving the model accuracy.Processes 2019, 7, 893 2 of 23 Two kinds of methods are typically used to solve model-mismatch problems [5,6]. The first involves retraining the model using the most recent samples. For example, Feng et al. [6] used the object-reidentification method to deal with object changes. The second kind involves adjusting the structure and parameters of the model according to the original model [7][8][9]. Reidentification of the model parameters cannot always maintain good model performance, because the model structure is important. Thus, updating the model at different scales is necessary. For online updating, the computation time of the algorithm is also significant.As for retraining and updating the model parameters, in a common method, the most recent samples are used to re-estimate the model parameters offline or online. For example, Feng et al. [6] used an object-reidentification method to deal with changes in the object model and object-mismatch problems. Wang et al.[10] used a four-step recursive algorithm to estimate the weights of a neural network. Here, online spatiotemporal measurements were performed to obtain time-varying model parameters.In recent years, for selecting relate...

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Section: Elm-based Afl-slfnmentioning

confidence: 99%

ELM-Based AFL–SLFN Modeling and Multiscale Model-Modification Strategy for Online Prediction

et al. 2019

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PiXelNet: A DL-Based method for Diagnosing Lung Cancer using the Histopathological images

Adhikari,

Guha,

Alka

et al. 2022

2022 5th Asia Conference on Machine Learning and Computing (ACMLC)

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Rigorous assessment of data mining algorithms in gestational diabetes mellitus prediction

Reddy

Sethi

Rajender³

2022

KES

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Gestational diabetes mellitus (GDM) is the type of diabetes that affects pregnant women due to high blood sugar levels. The women with gestational diabetes have a chance of miscarriage during pregnancy and having chance of developing type-2 diabetes in the future. It is a general practice to take proper diabetes test like OGTT to detect GDM. This test is to be done during 24 to 28 weeks of pregnancy. In addition, the use of machine learning could be exploited for predicting gestational diabetes. The main goal of this work is to propose optimal ML algorithms for effective prediction of gestational diabetes mellitus and there by avoid it’s side effects and future complications. In this work different machine algorithms are planned to be compared for their performance in predicting GDM. Before analysing the algorithms they are implemented using 10 fold cross validation technique to obtain better performance. The algorithms implemented are Linear Discriminant Analysis, Mixture Discriminant Analysis, Quadratic Discriminant Analysis, Flexible Discriminant Analysis, Regularized Discriminant Analysis and Feed Forward Neural Networks. These algorithms are compared depending on performance measures accuracy, kappa statistic, sensitivity, specificity, precision and F-measure. Then feed forward neural networks and Flexible Discriminant Analysis are obtained as optimal in this work.

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TFFN: Two hidden layer feed forward network using the randomness of extreme learning machine

Cited by 3 publications

References 11 publications

ELM-Based AFL–SLFN Modeling and Multiscale Model-Modification Strategy for Online Prediction

ELM-Based AFL–SLFN Modeling and Multiscale Model-Modification Strategy for Online Prediction

PiXelNet: A DL-Based method for Diagnosing Lung Cancer using the Histopathological images

Rigorous assessment of data mining algorithms in gestational diabetes mellitus prediction

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