A Connection between Extreme Learning Machine and Neural Network Kernel

Parviainen, Eli; Riihimäki, Jaakko

doi:10.1007/978-3-642-29764-9_8

Cited by 9 publications

(2 citation statements)

References 10 publications

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“…In elmNNR, complexity of model selection is determined by the number of parameters (weight variance, number of hidden units...) and the ranges used for them. The complexity is O(H 3 ), where H is the number of hidden units [57]. CUBIST is a rule-based model that is an extension of Quinlan's M5 model tree.…”

Section: Regression Modelsmentioning

confidence: 99%

A Computer-Aided Inspection System to Predict Quality Characteristics in Food Technology

et al. 2022

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Physicochemical and sensory analyses are commonly used to determine the quality characteristics of food samples in Food Industries. These methods are tedious, laborious, produce chemical residues, and involve the destruction of the samples. For the meat industries, this work proposes a noninvasive and non-destructive computer-aided inspection system, based on computer vision and ensemble machine learning techniques. The paper presents all the possibilities for the development of the system, making an exhaustive comparison of different algorithms used to extract features from the images of the samples, and various machine learning approaches, studying up to 6160 different models, and selecting the top 110 for the ensemble proposal. The system determines all the physicochemical, textural, and sensory quality characteristics of pork and beef loins in four meat states (fresh, thawed, cooked, and cured) with good precision, being a real alternative to the usual methods for the Food Industry.

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Section: Regression Modelsmentioning

confidence: 99%

A Computer-Aided Inspection System to Predict Quality Characteristics in Food Technology

et al. 2022

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“…The greatest disadvantage of a standard ELM is that a non-linear transformation with randomly selected parameters may be unable to represent all important features of the input space. Hence, a large number of neurons is necessary, which generates the numerical problems described above and in [39][40][41][42][43][44].…”

Section: Elm With Input-dependent Output Weights 41 New Network Structurementioning

confidence: 99%

Approximation of Permanent Magnet Motor Flux Distribution by Partially Informed Neural Networks

Jastrzębski

Kabziński

2021

Energies

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New results in the area of neural network modeling applied in electric drive automation are presented. Reliable models of permanent magnet motor flux as a function of current and rotor position are particularly useful in control synthesis — allowing one to minimize the losses, analyze motor performance (torque ripples etc.) and to identify motor parameters—and may be used in the control loop to compensate flux and torque variations. The effectiveness of extreme learning machine (ELM) neural networks used for approximation of permanent magnet motor flux distribution is evaluated. Two original network modifications, using preliminary information about the modeled relationship, are introduced. It is demonstrated that the proposed networks preserve all appealing features of a standard ELM (such as the universal approximation property and extremely short learning time), but also decrease the number of parameters and deal with numerical problems typical for ELMs. It is demonstrated that the proposed modified ELMs are suitable for modeling motor flux versus position and current, especially for interior permanent magnet motors. The modeling methodology is presented. It is shown that the proposed approach produces more accurate models and provides greater robustness against learning data noise. The execution times obtained experimentally from well-known DSP boards are short enough to enable application of derived models in modern algorithms of electric drive control.

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Is Extreme Learning Machine Effective for Multisource Friction Modeling?

Kabziński

2015

IFIP Advances in Information and Communication Technology

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Abstract. The aim of this contribution is to discuss suitability of extreme learning machine (ELM) approach for modeling multisource friction for motion control purposes. The specific features of multisource friction in mechatronic systems are defined, the main aspects of friction modeling by a standard ELM are investigated and some modifications are proposed to make it more suitable for specific demands of the discussed task. This allows to formulate some general remarks concerning properties of ELM for function approximation.

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A Connection between Extreme Learning Machine and Neural Network Kernel

Cited by 9 publications

References 10 publications

A Computer-Aided Inspection System to Predict Quality Characteristics in Food Technology

A Computer-Aided Inspection System to Predict Quality Characteristics in Food Technology

Approximation of Permanent Magnet Motor Flux Distribution by Partially Informed Neural Networks

Is Extreme Learning Machine Effective for Multisource Friction Modeling?

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