scholarly journals An Integrated Methodology for Rule Extraction from ELM-Based Vacuum Tank Degasser Multiclassifier for Decision-Making

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3535 ◽  
Author(s):  
Wang ◽  
Li ◽  
Zhang ◽  
Zou
Keyword(s):  
Decision Making ◽  
Liquid Steel ◽  
Classification Problem ◽  
Rule Extraction ◽  
Black Box ◽  
Production Data ◽  
Integrated Methodology ◽  
Irrelevant Attributes ◽  
Learning Machine ◽  
Elm Classifier

The present work proposes an integrated methodology for rule extraction in a vacuum tank degasser (VTD) for decision-making purposes. An extreme learning machine (ELM) algorithm is established for a three-class classification problem according to an end temperature of liquid steel that is higher than its operating restriction, within the operation restriction and lower than the operating restriction. Based on these black-box model results, an integrated three-step approach for rule extraction is constructed to interpret the understandability of the proposed ELM classifier. First, the irrelevant attributes are pruned without decreasing the classification accuracy. Second, fuzzy rules are generated in the form of discrete input attributes and the target classification. Last but not the least, the rules are refined by generating rules with continuous attributes. The novelty of the proposed rule extraction approach lies in the generation of rules using the discrete and continuous attributes at different stages. The proposed method is analyzed and validated on actual production data derived from a No.2 steelmaking workshop in Baosteel. The experimental results revealed that the extracted rules are effective for the VTD system in classifying the end temperature of liquid steel into high, normal, and low ranges. In addition, much fewer input attributes are needed to implement the rules for the manufacturing process of VTD. The extracted rules serve explicit instructions for decision-making for the VTD operators.

scholarly journals Opening the Black Box: the Relationship between Neural Networks and Linear Discriminant Functions

1997 ◽  
Vol 14 (1) ◽  
pp. 19-30 ◽  
Author(s):  
Roger A. Kemp ◽  
Calum MacAulay ◽  
Branko Palcic
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Classification Problem ◽  
Black Box ◽  
Discriminant Functions ◽  
Classification Problems ◽  
Statistical Decision ◽  
Feed Forward ◽  
Linear Discriminant ◽  
Feed Forward Neural Networks

Over the last ten years feed‐forward neural networks have become a popular tool for statistical decision making. During this time, they have been applied in many fields, including cytological classification. Neural networks are often treated as a black box, whose inner workings are concealed from the researcher. This is unfortunate, since the inner workings of a neural network can be understood in a manner similar to that of a linear discriminant function, which is the standard tool that researchers use for decision making.This paper discusses feed‐forward neural networks and some methods to improve their performance for classification problems. Their relationship to discriminant functions will be examined for a simple two‐dimensional classification problem.

scholarly journals Evolutionary Voting-Based Extreme Learning Machines

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Nan Liu ◽  
Jiuwen Cao ◽  
Zhiping Lin ◽  
Pin Pin Pek ◽  
Zhi Xiong Koh ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Extreme Learning Machine ◽  
Real World ◽  
Classification Accuracy ◽  
Majority Voting ◽  
Extreme Learning Machines ◽  
Learning Machines ◽  
Learning Machine ◽  
Elm Classifier

Voting-based extreme learning machine (V-ELM) was proposed to improve learning efficiency where majority voting was employed. V-ELM assumes that all individual classifiers contribute equally to the decision ensemble. However, in many real-world scenarios, this assumption does not work well. In this paper, we aim to enhance V-ELM by introducing weights to distinguish the importance of each individual ELM classifier in decision making. Genetic algorithm is used for optimizing these weights. This evolutionary V-ELM is named as EV-ELM. Results on several benchmark databases show that EV-ELM achieves the highest classification accuracy compared with V-ELM and ELM.

Islands of Stability: Engaging Emergence from Cellular Automata to the Occupy Movement

2014 ◽  
Vol 5 (1) ◽  
Author(s):  
Andrew Pickering
Keyword(s):  
Decision Making ◽  
Cellular Automata ◽  
Black Box ◽  
Collective Decision ◽  
Collective Decision Making ◽  
Occupy Movement ◽  
History Of ◽  
The 1960S

"Instead of considering »being with« in terms of non-problematic, machine-like places, where reliable entities assemble in stable relationships, STS conjures up a world where the achievement of chancy stabilisations and synchronisations is local.We have to analyse how and where a certain regularity and predictability in the intersection of scientists and their instruments, say, or of human individuals and groups, is produced.The paper reviews models of emergence drawn from the history of cybernetics—the canonical »black box,« homeostats, and cellular automata—to enrich our imagination of the stabilisation process, and discusses the concept of »variety« as a way of clarifying its difficulty, with the antiuniversities of the 1960s and the Occupy movement as examples. Failures of »being with« are expectable. In conclusion, the paper reviews approaches to collective decision-making that reduce variety without imposing a neoliberal hierarchy. "

MEMPREDIKSI JUMLAH PRODUKSI ULOS BATAK DENGAN MENGGUNAKAN LOGIKA FUZZY DENGAN METODE TSUKAMOTO (Studi Kasus Cv. Ala Dos Roha)

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Sri Handayani Sianipar ◽  
Fince Tinus Waruwu ◽  
Lince Tomoria Sianturi
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Production Method ◽  
Production Data ◽  
Speed Up

Ulos batak toba is one of indonesia traditional fabric, precisely the traditional cloth of the batak toba. From time to time the ulos fabric was growing in terms of  type and motif. One of the companies that produces ulos batak is cv. Ala dos roha. The authors conducted this study aimed at predicting the amount of production of ulos batak to produced later. The author uses the previous request, inventory and production data using fuzzy logic tsukamoto. The final result of the calculation with this method will be more effective and efficient so as to speed up the decision making time to predict the amount of production to be produced next.Keywords: prediction, amount of  production, method of tsukamoto

scholarly journals Data-Driven Dispatching Rules Mining and Real-Time Decision-Making Methodology in Intelligent Manufacturing Shop Floor with Uncertainty

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4836
Author(s):  
Liping Zhang ◽  
Yifan Hu ◽  
Qiuhua Tang ◽  
Jie Li ◽  
Zhixiong Li
Keyword(s):  
Decision Making ◽  
Data Base ◽  
Real Time ◽  
Manufacturing Industry ◽  
Job Shop ◽  
Data Driven ◽  
Dispatching Rules ◽  
Shop Floor ◽  
Production Data ◽  
Online Decision Making

In modern manufacturing industry, the methods supporting real-time decision-making are the urgent requirement to response the uncertainty and complexity in intelligent production process. In this paper, a novel closed-loop scheduling framework is proposed to achieve real-time decision making by calling the appropriate data-driven dispatching rules at each rescheduling point. This framework contains four parts: offline training, online decision-making, data base and rules base. In the offline training part, the potential and appropriate dispatching rules with managers’ expectations are explored successfully by an improved gene expression program (IGEP) from the historical production data, not just the available or predictable information of the shop floor. In the online decision-making part, the intelligent shop floor will implement the scheduling scheme which is scheduled by the appropriate dispatching rules from rules base and store the production data into the data base. This approach is evaluated in a scenario of the intelligent job shop with random jobs arrival. Numerical experiments demonstrate that the proposed method outperformed the existing well-known single and combination dispatching rules or the discovered dispatching rules via metaheuristic algorithm in term of makespan, total flow time and tardiness.

Stable rule extraction and decision making in rough non-deterministic information analysis

2011 ◽  
Vol 8 (1) ◽  
pp. 41-57 ◽  
Author(s):  
Hiroshi Sakai ◽  
Hitomi Okuma ◽  
Michinori Nakata ◽  
Dominik Ślȩzak
Keyword(s):  
Decision Making ◽  
Rule Extraction ◽  
Information Analysis

scholarly journals A Machine Learning Approach to Reveal the NeuroPhenotypes of Autisms

2019 ◽  
Vol 29 (07) ◽  
pp. 1850058 ◽  
Author(s):  
Juan M. Górriz ◽  
Javier Ramírez ◽  
F. Segovia ◽  
Francisco J. Martínez ◽  
Meng-Chuan Lai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Structure ◽  
Feature Space ◽  
Classification Problem ◽  
Small Sample ◽  
Biological Sex ◽  
Machine Learning Approach ◽  
Learning Machine ◽  
Small Sample Sizes ◽  
Low Dimensional

Although much research has been undertaken, the spatial patterns, developmental course, and sexual dimorphism of brain structure associated with autism remains enigmatic. One of the difficulties in investigating differences between the sexes in autism is the small sample sizes of available imaging datasets with mixed sex. Thus, the majority of the investigations have involved male samples, with females somewhat overlooked. This paper deploys machine learning on partial least squares feature extraction to reveal differences in regional brain structure between individuals with autism and typically developing participants. A four-class classification problem (sex and condition) is specified, with theoretical restrictions based on the evaluation of a novel upper bound in the resubstitution estimate. These conditions were imposed on the classifier complexity and feature space dimension to assure generalizable results from the training set to test samples. Accuracies above [Formula: see text] on gray and white matter tissues estimated from voxel-based morphometry (VBM) features are obtained in a sample of equal-sized high-functioning male and female adults with and without autism ([Formula: see text], [Formula: see text]/group). The proposed learning machine revealed how autism is modulated by biological sex using a low-dimensional feature space extracted from VBM. In addition, a spatial overlap analysis on reference maps partially corroborated predictions of the “extreme male brain” theory of autism, in sexual dimorphic areas.

scholarly journals Fault Diagnosis for Analog Circuits by Using EEMD, Relative Entropy, and ELM

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Jian Xiong ◽  
Shulin Tian ◽  
Chenglin Yang
Keyword(s):  
Fault Diagnosis ◽  
Relative Entropy ◽  
Analog Circuits ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Eemd Method ◽  
Learning Machine ◽  
Elm Classifier

This paper presents a novel fault diagnosis method for analog circuits using ensemble empirical mode decomposition (EEMD), relative entropy, and extreme learning machine (ELM). First, nominal and faulty response waveforms of a circuit are measured, respectively, and then are decomposed into intrinsic mode functions (IMFs) with the EEMD method. Second, through comparing the nominal IMFs with the faulty IMFs, kurtosis and relative entropy are calculated for each IMF. Next, a feature vector is obtained for each faulty circuit. Finally, an ELM classifier is trained with these feature vectors for fault diagnosis. Via validating with two benchmark circuits, results show that the proposed method is applicable for analog fault diagnosis with acceptable levels of accuracy and time cost.

scholarly journals A microarray gene expressions with classification using extreme learning machine

Genetika ◽  
2015 ◽  
Vol 47 (2) ◽  
pp. 523-534
Author(s):  
M. Yasodha ◽  
P. Ponmuthuramalingam
Keyword(s):  
Extreme Learning Machine ◽  
High Speed ◽  
Dna Microarrays ◽  
Research Work ◽  
The Body ◽  
Cancer Classification ◽  
Medical Sciences ◽  
Gene Expressions ◽  
Learning Machine ◽  
Elm Classifier

In the present scenario, one of the dangerous disease is cancer. It spreads through blood or lymph to other location of the body, it is a set of cells display uncontrolled growth, attack and destroy nearby tissues, and occasionally metastasis. In cancer diagnosis and molecular biology, a utilized effective tool is DNA microarrays. The dominance of this technique is recognized, so several open doubt arise regarding proper examination of microarray data. In the field of medical sciences, multicategory cancer classification plays very important role. The need for cancer classification has become essential because the number of cancer sufferers is increasing. In this research work, to overcome problems of multicategory cancer classification an improved Extreme Learning Machine (ELM) classifier is used. It rectify problems faced by iterative learning methods such as local minima, improper learning rate and over fitting and the training completes with high speed.

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