Machine Learning: Automated Knowledge Acquisition Based on Unsupervised Neural Network and Expert System Paradigms

2005 ◽  
Vol 9 (6) ◽  
pp. 693-697 ◽  
Author(s):  
Nazar Elfadil ◽  
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Knowledge Acquisition ◽  
Training Data ◽  
Neural Network Models ◽  
Self Organizing Maps ◽  
Data Set ◽  
Unsupervised Neural Network ◽  
Automated Knowledge ◽  
Self Organizing

Self-organizing maps are unsupervised neural network models that lend themselves to the cluster analysis of high-dimensional input data. Interpreting a trained map is difficult because features responsible for specific cluster assignment are not evident from resulting map representation. This paper presents an approach to automated knowledge acquisition using Kohonen's self-organizing maps and k-means clustering. To demonstrate the architecture and validation, a data set representing animal world has been used as the training data set. The verification of the produced knowledge base is done by using conventional expert system.

scholarly journals Using self-organizing maps for determination of soil fertility (case study: Shiraz plain)

2018 ◽  
Vol 13 (No. 1) ◽  
pp. 11-17 ◽  
Author(s):  
M. Mokarram ◽  
M. Najafi-Ghiri ◽  
A.R. Zarei
Keyword(s):  
Neural Network ◽  
Soil Fertility ◽  
Network Models ◽  
The Other ◽  
Neural Network Models ◽  
Self Organizing Maps ◽  
Soil Features ◽  
Close Relationship ◽  
Artificial Neural Network Ann ◽  
Self Organizing

Soil fertility refers to the ability of a soil to supply plant nutrients. Naturally, micro and macro elements are made available to plants by breakdown of the mineral and organic materials in the soil. Artificial neural network (ANN) provides deeper understanding of human cognitive capabilities. Among various methods of ANN and learning an algorithm, self-organizing maps (SOM) are one of the most popular neural network models. The aim of this study was to classify the factors influencing soil fertility in Shiraz plain, southern Iran. The relationships among soil features were studied using the SOM in which, according to qualitative data, the clustering tendency of soil fertility was investigated using seven parameters (N, P, K, Fe, Zn, Mn, and Cu). The results showed that for soil fertility there is a close relationship between P and N, and also between P and Zn. The other parameters, such as K, Fe, Mn, and Cu, are not mutually related. The results showed that there are six clusters for soil fertility and also that group 1 soils are more fertile than the other.

scholarly journals PS1-STRM: neural network source classification and photometric redshift catalogue for PS1 3π DR1

2020 ◽  
Vol 500 (2) ◽  
pp. 1633-1644
Author(s):  
Róbert Beck ◽  
István Szapudi ◽  
Heather Flewelling ◽  
Conrad Holmberg ◽  
Eugene Magnier ◽  
...  
Keyword(s):  
Neural Network ◽  
Broad Band ◽  
Monte Carlo Sampling ◽  
Training Data ◽  
Space Telescopes ◽  
Absolute Deviation ◽  
Self Organizing Maps ◽  
Validation Data ◽  
Data Set ◽  
Photometric Redshift

ABSTRACT The Pan-STARRS1 (PS1) 3π survey is a comprehensive optical imaging survey of three quarters of the sky in the grizy broad-band photometric filters. We present the methodology used in assembling the source classification and photometric redshift (photo-z) catalogue for PS1 3π Data Release 1, titled Pan-STARRS1 Source Types and Redshifts with Machine learning (PS1-STRM). For both main data products, we use neural network architectures, trained on a compilation of public spectroscopic measurements that has been cross-matched with PS1 sources. We quantify the parameter space coverage of our training data set, and flag extrapolation using self-organizing maps. We perform a Monte Carlo sampling of the photometry to estimate photo-z uncertainty. The final catalogue contains 2902 054 648 objects. On our validation data set, for non-extrapolated sources, we achieve an overall classification accuracy of $98.1{{\ \rm per\ cent}}$ for galaxies, $97.8{{\ \rm per\ cent}}$ for stars, and $96.6{{\ \rm per\ cent}}$ for quasars. Regarding the galaxy photo-z estimation, we attain an overall bias of 〈Δznorm〉 = 0.0005, a standard deviation of σ(Δznorm) = 0.0322, a median absolute deviation of MAD(Δznorm) = 0.0161, and an outlier fraction of $P\left(|\Delta z_{\mathrm{norm}}|\gt 0.15\right)=1.89{{\ \rm per\ cent}}$. The catalogue will be made available as a high-level science product via the Mikulski Archive for Space Telescopes.

scholarly journals Evaluation of Neural Network Model for Estimating Pile Load Capacity

2021 ◽  
Vol 21 (5) ◽  
pp. 221-228
Author(s):  
Byungsik Lee
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Load Capacity ◽  
Absolute Error ◽  
Descent Method ◽  
Training Data ◽  
Gradient Descent Method ◽  
Neural Network Models ◽  
Data Set

Neural network models based on deep learning algorithms are increasingly used for estimating pile load capacities as supplements of bearing capacity equations and field load tests. A series of hyperparameter tuning is required to improve the performance and reliability of developing a neural network model. In this study, the number of hidden layers and neurons, the activation functions, the optimizing algorithms of the gradient descent method, and the learning rates were tuned. The grid search method was applied for the tuning, which is a hyperpameter optimizer supplied by the developing platform. The cross-validation method was applied to enhance reliability for model validation. An appropriate number of epochs was determined using the early stopping method to prevent the overfitting of the model to the training data. The performance of the tuned optimum model evaluated for the test data set revealed that the model could estimate pile load capacities approximately with an average absolute error of 3,000 kN and a coefficient of determinant of 0.5.

KNOWLEDGE EXTRACTION FROM RISE-TIME AUTO-CORRELATED PATTERNS

2008 ◽  
Vol 05 (02) ◽  
pp. 181-187 ◽  
Author(s):  
NAZAR ELFADIL
Keyword(s):  
Expert System ◽  
Rise Time ◽  
Knowledge Extraction ◽  
Correlated Data ◽  
Self Organizing Maps ◽  
Data Set ◽  
Knowledge Based ◽  
Correlated Patterns ◽  
Automated Knowledge ◽  
Learning Data

In this paper, the author presents an approach for automated knowledge extraction from rise time auto-correlated patterns by using self-organizing maps and k-means clustering. The extracted knowledge in terms of rules will be used as knowledge base for an expert system. Rise-time auto-correlated data patterns are used as a learning data set. The produced knowledge based was verified by using a conventional expert system.

KNOWLEDGE EXTRACTION FROM FALL-TIME AUTO-CORRELATED PATTERNS BY USING NEURAL RULE BASED EXPERT SYSTEM

2006 ◽  
Vol 03 (01) ◽  
pp. 15-24 ◽  
Author(s):  
NAZAR ELFADIL ◽  
INTISAR IBRAHIM
Keyword(s):  
Expert System ◽  
Correlated Data ◽  
Self Organizing Maps ◽  
Data Set ◽  
Rule Based ◽  
Fall Time ◽  
Knowledge Based ◽  
Correlated Patterns ◽  
Automated Knowledge ◽  
Learning Data

In this paper, the author presents an approach for automated knowledge acquisition system using Kohonen self-organizing maps and k-means clustering. The extracted knowledge in terms of rules are used as knowledge base for a rule based expert system. For the sake of illustrating and validating the system overall architecture, a fall-time auto-correlated data patterns has been used as a learning data set. The verification of the produced knowledge based was conducted by conventional expert system.

Constraining self-organizing map facies analysis with stratigraphy: An approach to increase the credibility in automatic seismic facies classification

2017 ◽  
Vol 5 (2) ◽  
pp. T163-T171 ◽  
Author(s):  
Tao Zhao ◽  
Fangyu Li ◽  
Kurt J. Marfurt
Keyword(s):  
Facies Analysis ◽  
Vertical Axis ◽  
Training Data ◽  
Seismic Facies ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Data Set ◽  
Statistical Correlations ◽  
Facies Classification ◽  
Self Organizing

Pattern recognition-based seismic facies analysis techniques are commonly used in modern quantitative seismic interpretation. However, interpreters often treat techniques such as artificial neural networks and self-organizing maps (SOMs) as a “black box” that somehow correlates a suite of attributes to a desired geomorphological or geomechanical facies. Even when the statistical correlations are good, the inability to explain such correlations through principles of geology or physics results in suspicion of the results. The most common multiattribute facies analysis begins by correlating a suite of candidate attributes to a desired output, keeping those that correlate best for subsequent analysis. The analysis then takes place in attribute space rather than ([Formula: see text], [Formula: see text], and [Formula: see text]) space, removing spatial trends often observed by interpreters. We add a stratigraphy layering component to a SOM model that attempts to preserve the intersample relation along the vertical axis. Specifically, we use a mode decomposition algorithm to capture the sedimentary cycle pattern as an “attribute.” If we correlate this attribute to the training data, it will favor SOM facies maps that follow stratigraphy. We apply this workflow to a Barnett Shale data set and find that the constrained SOM facies map shows layers that are easily overlooked on traditional unconstrained SOM facies map.

Map Matching Using De-Noise Interpolation Kohonen Self-Organizing Maps

2011 ◽  
Vol 460-461 ◽  
pp. 680-686 ◽  
Author(s):  
Zhan Wei Du ◽  
Yong Jian Yang ◽  
Yong Xiong Sun ◽  
Chi Jun Zhang
Keyword(s):  
Neural Network ◽  
Large Error ◽  
Original Position ◽  
Map Matching ◽  
Self Organizing Maps ◽  
Data Set ◽  
Position Information ◽  
True Value ◽  
Match Accuracy ◽  
Self Organizing

In this work, we have proposed a de-noise interpolation Kohonen Self-Organizing Maps(DNIKSOM) -based method for the Map matching(MM). It has been seen that there are some problems in the MM, such as large error range of the original position information, low match accuracy and so on. Therefore, in MM problem to achieve high accuracy, it is necessary to consider the topography of roads and the requirement for match accuracy lying within the original position information in the matching process. In the present study, Kohonen Self-Organizing Maps(KSOM) in the field of pattern recognition possesses good performance. Now to get more valuable position information, A kind of de-noise algorithm for Kohonen neural network is proposed to meet the case that neural network may not be trained sufficiently with consideration for the topography of roads. And a kind of Lagrange interpolation algorithm is also proposed to meet the requirements for matching accuracy. These processes make the amended position information closer to the true value. In this application to a city’s MM, we investigate DNIKSOM’s ,KSOM’s and Centroid localization algorithm’s location performance on a original position data set. Finally, the comparison of experimental results shows that DNIKSOM has better location performance than others.

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