ARTIFICIAL NEURAL NETWORK WITH REGULAR GRAPH FOR MAXIMUM AIR TEMPERATURE FORECASTING: THE EFFECT OF DECREASE IN NODES DEGREE ON LEARNING

2012 ◽  
Vol 23 (02) ◽  
pp. 1250008 ◽  
Author(s):  
A. H. GHADERI ◽  
A. H. DAROONEH
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Nonlinear Systems ◽  
Air Temperature ◽  
Regular Graph ◽  
Regular Structures ◽  
Temperature Forecasting ◽  
Artificial Neural ◽  
Fully Connected ◽  
Maximum Air Temperature

The behavior of nonlinear systems can be analyzed by artificial neural networks. Air temperature change is one example of the nonlinear systems. In this work, a new neural network method is proposed for forecasting maximum air temperature in two cities. In this method, the regular graph concept is used to construct some partially connected neural networks that have regular structures. The learning results of fully connected ANN and networks with proposed method are compared. In some case, the proposed method has the better result than conventional ANN. After specifying the best network, the effect of input pattern numbers on the prediction is studied and the results show that the increase of input patterns has a direct effect on the prediction accuracy.

scholarly journals KLASIFIKASI BATIK RIAU DENGAN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORKS (CNN)

2020 ◽  
Vol 9 (1) ◽  
pp. 7-10
Author(s):  
Hendry Fonda
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
18Th Century ◽  
The Public ◽  
Artificial Neural ◽  
The Difference ◽  
Fully Connected

ABSTRACT Riau batik is known since the 18th century and is used by royal kings. Riau Batik is made by using a stamp that is mixed with coloring and then printed on fabric. The fabric used is usually silk. As its development, comparing Javanese  batik with riau batik Riau is very slowly accepted by the public. Convolutional Neural Networks (CNN) is a combination of artificial neural networks and deeplearning methods. CNN consists of one or more convolutional layers, often with a subsampling layer followed by one or more fully connected layers as a standard neural network. In the process, CNN will conduct training and testing of Riau batik so that a collection of batik models that have been classified based on the characteristics that exist in Riau batik can be determined so that images are Riau batik and non-Riau batik. Classification using CNN produces Riau batik and not Riau batik with an accuracy of 65%. Accuracy of 65% is due to basically many of the same motifs between batik and other batik with the difference lies in the color of the absorption in the batik riau. Kata kunci: Batik; Batik Riau; CNN; Image; Deep Learning   ABSTRAK   Batik Riau dikenal sejak abad ke 18 dan digunakan oleh bangsawan raja. Batik Riau dibuat dengan menggunakan cap yang dicampur dengan pewarna kemudian dicetak di kain. Kain yang digunakan biasanya sutra. Seiring perkembangannya, dibandingkan batik Jawa maka batik Riau sangat lambat diterima oleh masyarakat. Convolutional Neural Networks (CNN) merupakan kombinasi dari jaringan syaraf tiruan dan metode deeplearning. CNN terdiri dari satu atau lebih lapisan konvolutional, seringnya dengan suatu lapisan subsampling yang diikuti oleh satu atau lebih lapisan yang terhubung penuh sebagai standar jaringan syaraf. Dalam prosesnya CNN akan melakukan training dan testing terhadap batik Riau sehingga didapat kumpulan model batik yang telah terklasi    fikasi berdasarkan ciri khas yang ada pada batik Riau sehingga dapat ditentukan gambar (image) yang merupakan batik Riau dan yang bukan merupakan batik Riau. Klasifikasi menggunakan CNN menghasilkan batik riau dan bukan batik riau dengan akurasi 65%. Akurasi 65% disebabkan pada dasarnya banyak motif yang sama antara batik riau dengan batik lainnya dengan perbedaan terletak pada warna cerap pada batik riau. Kata kunci: Batik; Batik Riau; CNN; Image; Deep Learning

scholarly journals FORMING EVOLUTIONARY DESIGN OF NEURAL NETWORKS WITH DIFFERENT NODES

2014 ◽  
pp. 16-23
Author(s):  
Eva Volna
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Feedforward Neural Network ◽  
Evolutionary Design ◽  
Pattern Recognition Problem ◽  
Multilayer Feedforward Neural Network ◽  
Artificial Neural ◽  
Fully Connected

Evolution in artificial neural networks (e.g. neuroevolution) searches through the space of behaviours for a network that performs well at a given task. Here is presented a neuroevolution system evolving populations of neurons that are combined to form the fully connected multilayer feedforward neural network with fixed architecture. In this article, the transfer function has been shown to be an important part of architecture of the artificial neural network and have significant impact on an artificial neural network’s performance. In order to test the efficiency of described method, we applied it to the pattern recognition problem and to the alphabet coding problem.

scholarly journals Artificial Intelligence Simulating Grain Productivity During the Wheat Development Considering Biological And Environmental Indicators

2019 ◽  
Vol 7 (2) ◽  
pp. 197
Author(s):  
Ângela Teresinha Woschinski De Mamann ◽  
José Antonio Gonzalez da Silva ◽  
Manuel Osório Binelo ◽  
Osmar Bruneslau Scremin ◽  
Adriana Roselia Kraisig ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Grain Yield ◽  
Air Temperature ◽  
Network Architecture ◽  
Wheat Grain ◽  
Development Cycle ◽  
Grain Productivity ◽  
Artificial Neural ◽  
Maximum Air Temperature

The artificial neural networks modeling might simulate the efficiency of wheat grain yield involving biological and environmental conditions during the development cycle.  Considering the main succession systems in wheat crop in Brazil, the study aimed to adapt an artificial neural network architecture capable of predict the wheat grain productivity throughout the growth cycle, involving nitrogen and non-linearity of maximum air temperature and rainfall. The field experiment was conducted in two successions systems (soybean/wheat and maize/wheat) in 2017 and 2018, the trial design was in a randomize blocs with eight replicate in the level 0, 30, 60, and 120 kg ha-1 N-fertilizer doses in the phenological stage of third fully expanded leaves. Every 30 day of the development cycle were obtained the biomass yield, maximum air temperature and accumulated rainfall information. The perceptron multi-layered artificial neural networks with backpropagation algorithm with network architecture 5-8-1 and 5-7-1 in soybean/wheat and maize/wheat system respectively, is able to simulate the wheat grain yield involving the nitrogen dose at top-dressing and the non-linearity of maximum air temperature and rainfall with biomass information obtained during the cycle crop. 

scholarly journals Relationship between training database size and structural connectivity in artificial neural networks: Do specific local connections improve prediction accuracy?

2021 ◽  
Author(s):  
Min Sik Park
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Accuracy ◽  
Structural Connectivity ◽  
Large Size ◽  
Artificial Neural ◽  
Neuronal Systems ◽  
Similar Accuracy ◽  
Fully Connected

Abstract Training of artificial neural networks is very expensive, as a large-size database is necessary. Moreover, it is usually difficult to find such large-size training databases. Hence, it will be interesting to design artificial neural networks that can be used for training with a small-size database, while maintaining a similar accuracy for prediction compared to fully connected neural networks. We studied neural networks with partial disconnections, additional bypass connections, and negative activation nodes, which are found in the neuronal systems of the human brain. By combining the fully connected neural network and the above three brain-like elements, we found that the modified neural network showed improved prediction accuracy of 13% compared to the fully connected one despite the small size of the training database. To analyze the improved neural network, the contribution of each node in the hidden layers affecting the total prediction accuracy of the neural networks was studied. We also found important local connections that improve the prediction accuracy, and discussed the design of a neural network with a small-size training database without reduction in prediction accuracy.

Air temperature forecasting using artificial neural network for Ararat valley

2021 ◽  
Author(s):  
Hrachya Astsatryan ◽  
Hayk Grigoryan ◽  
Aghasi Poghosyan ◽  
Rita Abrahamyan ◽  
Shushanik Asmaryan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Air Temperature ◽  
Temperature Forecasting ◽  
Artificial Neural

scholarly journals Methodology for Knowledge Extraction from Trained Artificial Neural Networks

2018 ◽  
Vol 21 ◽  
pp. 6-14
Author(s):  
Andrey Bondarenko ◽  
Ludmila Aleksejeva
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Decision Tree ◽  
Binary Classification ◽  
Knowledge Extraction ◽  
Classification Problems ◽  
Critical Areas ◽  
Artificial Neural ◽  
Fully Connected

Artificial neural networks are widely spread models that outperform more basic, but explainable machine learning models like classification decision tree. Although their lack of explainability severely limits their area of application. All mission critical areas or law regulated areas (like European GDPR) require model to be explained. Explainability allows model validation for correctness and lack of bias. Thus methods for knowledge extraction from artificial neural networks have gained attention and development efforts. Current paper addresses this problem and describes knowledge extraction methodology which can be applied to classification problems. It is based on previous research and allows knowledge to be extracted from trained fully connected feed-forward artificial neural network, from radial basis function neural network and from hyper-polytope based classifier in the form of binary classification decision tree, elliptical rules and If-Then rules.

scholarly journals The Budapest Amyloid Predictor and Its Applications

Biomolecules ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 500
Author(s):  
László Keresztes ◽  
Evelin Szögi ◽  
Bálint Varga ◽  
Viktor Farkas ◽  
András Perczel ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector ◽  
User Needs ◽  
Computational Technique ◽  
Linear Support Vector Machine ◽  
Β Sheets ◽  
Artificial Neural ◽  
Amorphous Aggregation

The amyloid state of proteins is widely studied with relevance to neurology, biochemistry, and biotechnology. In contrast with nearly amorphous aggregation, the amyloid state has a well-defined structure, consisting of parallel and antiparallel β-sheets in a periodically repeated formation. The understanding of the amyloid state is growing with the development of novel molecular imaging tools, like cryogenic electron microscopy. Sequence-based amyloid predictors were developed, mainly using artificial neural networks (ANNs) as the underlying computational technique. From a good neural-network-based predictor, it is a very difficult task to identify the attributes of the input amino acid sequence, which imply the decision of the network. Here, we present a linear Support Vector Machine (SVM)-based predictor for hexapeptides with correctness higher than 84%, i.e., it is at least as good as the best published ANN-based tools. Unlike artificial neural networks, the decisions of the linear SVMs are much easier to analyze and, from a good predictor, we can infer rich biochemical knowledge. In the Budapest Amyloid Predictor webserver the user needs to input a hexapeptide, and the server outputs a prediction for the input plus the 6 × 19 = 114 distance-1 neighbors of the input hexapeptide.

scholarly journals Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks

Metals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Rahel Jedamski ◽  
Jérémy Epp
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Case Hardening ◽  
Artificial Neural ◽  
Non Destructive

Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration.

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