Generalization ability of artificial neural network using Fahlman and Lebiere's learning algorithm

2003 ◽  
Author(s):  
M. Hamamoto ◽  
J. Kamruzzaman ◽  
Y. Kumagai
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Algorithm ◽  
Generalization Ability ◽  
Artificial Neural

A Framework for an Artificial-Neural-Network-Based Electronic Nose

2022 ◽  
pp. 350-374
Author(s):  
Mudassir Ismail ◽  
Ahmed Abdul Majeed ◽  
Yousif Abdullatif Albastaki
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Electronic Nose ◽  
Learning Algorithm ◽  
General Purpose ◽  
Food Spoilage ◽  
Standard Data ◽  
Semiconductor Gas Sensor ◽  
Artificial Neural

Machine odor detection has developed into an important aspect of our lives with various applications of it. From detecting food spoilage to diagnosis of diseases, it has been developed and tested in various fields and industries for specific purposes. This project, artificial-neural-network-based electronic nose (ANNeNose), is a machine-learning-based e-nose system that has been developed for detection of various types of odors for a general purpose. The system can be trained on any odor using various e-nose sensor types. It uses artificial neural network as its machine learning algorithm along with an OMX-GR semiconductor gas sensor for collecting odor data. The system was trained and tested with five different types of odors collected through a standard data collection method and then purified, which in turn had a result varying from 93% to 100% accuracy.

scholarly journals IMPLEMENTASI BACKPROPAGATION NEURAL NETWORK DALAM PRAKIRAAN CUACA DI DAERAH BALI SELATAN

2016 ◽  
Vol 5 (4) ◽  
pp. 126 ◽  
Author(s):  
I MADE DWI UDAYANA PUTRA ◽  
G. K. GANDHIADI ◽  
LUH PUTU IDA HARINI
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Algorithm ◽  
Weather Forecast ◽  
Weather Forecasts ◽  
Forecast Models ◽  
Artificial Neural Network Method ◽  
Artificial Neural ◽  
Hidden Layer ◽  
Backpropagation Learning

Weather information has an important role in human life in various fields, such as agriculture, marine, and aviation. The accurate weather forecasts are needed in order to improve the performance of various fields. In this study, use artificial neural network method with backpropagation learning algorithm to create a model of weather forecasting in the area of ??South Bali. The aim of this study is to determine the effect of the number of neurons in the hidden layer and to determine the level of accuracy of the method of artificial neural network with backpropagation learning algorithm in weather forecast models. Weather forecast models in this study use input of the factors that influence the weather, namely air temperature, dew point, wind speed, visibility, and barometric pressure.The results of testing the network with a different number of neurons in the hidden layer of artificial neural network method with backpropagation learning algorithms show that the increase in the number of neurons in the hidden layer is not directly proportional to the value of the accuracy of the weather forecasts, the increase in the number of neurons in the hidden layer does not necessarily increase or decrease value accuracy of weather forecasts we obtain the best accuracy rate of 51.6129% on a network model with three neurons in the hidden layer.

An Artificial Neural Network Model for Chilling Environment Control in Meat Production

2015 ◽  
Vol 781 ◽  
pp. 479-482
Author(s):  
Apirachai Wongsriworaphon ◽  
Arthit Apichottanakul ◽  
Teerawat Laonapakul ◽  
Supachai Pathumnakul
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Weight Loss ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Learning Algorithm ◽  
Meat Production ◽  
Weight Class ◽  
Environment Control ◽  
Artificial Neural

In this study, artificial neural network with a supervised learning algorithm called vector-quantized temporal associative memory (VQTAM) is proposed to estimate chilled weight loss during chilling process of pig slaughtering plant. Four models based on carcass weights are developed. The results show that the proposed algorithms can accurately predict chilled weight loss with an error rate of less than 5% on average. The models are also employed to determine the suitable chilling times for each weight class.

Prediction of cyanobacterial blooms in the Dau Tieng Reservoir using an artificial neural network

2017 ◽  
Vol 68 (11) ◽  
pp. 2070 ◽  
Author(s):  
Manh-Ha Bui ◽  
Thanh-Luu Pham ◽  
Thanh-Son Dao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Adaptive Learning ◽  
Transfer Functions ◽  
Learning Algorithm ◽  
Oxygen Demand ◽  
Cyanobacterial Blooms ◽  
Cyanobacteria Bloom ◽  
Feed Forward Back Propagation ◽  
Artificial Neural

An artificial neural network (ANN) model was used to predict the cyanobacteria bloom in the Dau Tieng Reservoir, Vietnam. Eight environmental parameters (pH, dissolved oxygen, temperature, total dissolved solids, total nitrogen (TN), total phosphorus, biochemical oxygen demand and chemical oxygen demand) were introduced as inputs, whereas the cell density of three cyanobacteria genera (Anabaena, Microcystis and Oscillatoria) with microcystin concentrations were introduced as outputs of the three-layer feed-forward back-propagation ANN. Eighty networks covering all combinations of four learning algorithms (Bayesian regularisation (BR), gradient descent with momentum and adaptive learning rate, Levenberg–Mardquart, scaled conjugate gradient) with two transfer functions (tansig, logsig) and 10 numbers of hidden neurons (6–16) were trained and validated to find the best configuration fitting the observed data. The result is a network using the BR learning algorithm, tansig transfer function and nine neurons in the hidden layer, which shows satisfactory predictions with the low values of error (root mean square error=0.108) and high correlation coefficient values (R=0.904) between experimental and predicted values. Sensitivity analysis on the developed ANN indicated that TN and temperature had the most positive and negative effects respectively on microcystin concentrations. These results indicate that ANN modelling can effectively predict the behaviour of the cyanobacteria bloom process.

scholarly journals Deep Extreme Learning Machine and Its Application in EEG Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Shifei Ding ◽  
Nan Zhang ◽  
Xinzheng Xu ◽  
Lili Guo ◽  
Jian Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Deep Learning ◽  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Eeg Classification ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Learning Machine

Recently, deep learning has aroused wide interest in machine learning fields. Deep learning is a multilayer perceptron artificial neural network algorithm. Deep learning has the advantage of approximating the complicated function and alleviating the optimization difficulty associated with deep models. Multilayer extreme learning machine (MLELM) is a learning algorithm of an artificial neural network which takes advantages of deep learning and extreme learning machine. Not only does MLELM approximate the complicated function but it also does not need to iterate during the training process. We combining with MLELM and extreme learning machine with kernel (KELM) put forward deep extreme learning machine (DELM) and apply it to EEG classification in this paper. This paper focuses on the application of DELM in the classification of the visual feedback experiment, using MATLAB and the second brain-computer interface (BCI) competition datasets. By simulating and analyzing the results of the experiments, effectiveness of the application of DELM in EEG classification is confirmed.

Application of Artificial Neural Network for Prediction of Heat Treated Sintered Steels Properties

2008 ◽  
Vol 273-276 ◽  
pp. 323-328 ◽  
Author(s):  
H. Khorsand ◽  
M. Arjomandi ◽  
H. Abdoos ◽  
S.H. Sadati
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Heat Treatment ◽  
Artificial Neural Network ◽  
Learning Algorithm ◽  
Initial Density ◽  
Back Propagation ◽  
Heat Treated ◽  
Algorithm Comparison ◽  
Artificial Neural

Heat treatment is an important method for improving the mechanical properties of industrial parts that are made through the powder metallurgy. Most PM steels are subjected to hardening and tempering, and it is due to this treatment that tempered martensite is formed. After heat treatment, these steel’s mechanical properties are affected by the heat treatment parameters and the initial density. In this paper, in order to make an evaluation of the effect of the above parameters, FN-0205 PM steel with various densities is heat treated in different austenite conditions and tempering time. Their mechanical properties are then evaluated and recorded. Afterwards, this data obtained by experimental procedure are predicted for various conditions. The method employed here is the well-known feedforward Artificial Neural Network (ANN) with the Back Propagation (BP) learning algorithm. Comparison between predicted values and experimental data, in the present study, indicate that the predicted results from this model are in good agreement with the experimental values.

Application of artificial neural network to loan recovery prediction

2016 ◽  
Vol 9 (2) ◽  
pp. 222-238 ◽  
Author(s):  
Amos Olaolu Adewusi ◽  
Tunbosun Biodun Oyedokun ◽  
Mustapha Oyewole Bello
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Interest Rates ◽  
Classification Accuracy ◽  
Learning Algorithm ◽  
Type I ◽  
Ann Model ◽  
Content Type ◽  
Credit Evaluation ◽  
Artificial Neural

Purpose This study assesses the classification accuracy of an artificial neural network (ANN) model. It examines the application of loan recovery probability rather than odds of default as the case with traditional credit evaluation models. Design/methodology/approach Data on 2,300 loans granted over the period 2001-2012 was obtained from the databases of Nigerian commercial banks and primary mortgage institutions. A multilayer feed-forward ANN model with back-propagation learning algorithm was developed having classified the sample into training (38 per cent), testing (41 per cent) and validation (21 per cent) sub-samples. Findings The model exhibits a high overall percentage classification accuracy of 92.6 per cent. It also achieves relatively low misclassification Type I and Type II errors at 6.5 per cent and 8.2 per cent, respectively. Macroeconomic variables such as gross domestic product, inflation and interest rates have the strongest influence on the ANN model classification power. The result of the analysis shows that adopting odds of recovery in ANN classification models can lead to improved loan evaluation. Originality/value The paper is distinct from extant studies in that it presents a new dimension to loan evaluation in Nigerian lending market. To the best knowledge of the authors, the paper is among the first to explore probability of loan recovery as the basis for credit evaluation in the country.

scholarly journals Digit recognition using decimal coding and artificial neural network

2021 ◽  
Vol 49 (1) ◽  
Author(s):  
Toufik Datsi ◽  
◽  
Khalid Aznag ◽  
Ahmed El Oirrak ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multilayer Perceptron ◽  
Recognition Accuracy ◽  
Learning Algorithm ◽  
Binary Representation ◽  
Initial Image ◽  
Digit Recognition ◽  
Handwritten Digit ◽  
Artificial Neural

Current artificial neural network image recognition techniques use all the pixels of an image as input. In this paper, we present an efficient method for handwritten digit recognition that involves extracting the characteristics of a digit image by coding each row of the image as a decimal value, i.e., by transforming the binary representation into a decimal value. This method is called the decimal coding of rows. The set of decimal values calculated from the initial image is arranged as a vector and normalized; these values represent the inputs to the artificial neural network. The approach proposed in this work uses a multilayer perceptron neural network for the classification, recognition, and prediction of handwritten digits from 0 to 9. In this study, a dataset of 1797 samples were obtained from a digit database imported from the Scikit-learn library. Backpropagation was used as a learning algorithm to train the multilayer perceptron neural network. The results show that the proposed approach achieves better performance than two other schemes in terms of recognition accuracy and execution time.

scholarly journals PENERAPAN ARTIFICIAL NEURAL NETWORK DENGAN OPTIMASI MODIFIED ARTIFICIAL BEE COLONY UNTUK MERAMALKAN HARGA BITCOIN TERHADAP RUPIAH

2020 ◽  
Vol 9 (2) ◽  
pp. 135-142
Author(s):  
Di Mokhammad Hakim Ilmawan ◽  
Budi Warsito ◽  
Sugito Sugito
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Bee Colony ◽  
Time Series Data ◽  
Learning Algorithm ◽  
Series Data ◽  
Future Period ◽  
Bee Colony ◽  
Artificial Neural ◽  
Digital Assets

Bitcoin is one of digital assets that can be used to make a profit. One of the ways to use Bitcoin profitly is to trade Bitcoin. At trade activities, decisions making whether to buy or not are very crucial. If we can predict the price of Bitcoin in the future period, we can make a decisions whether to buy Bitcoin or not. Artificial Neural Network can be used to predict Bitcoin price data which is time series data. There are many learning algorithm in Artificial Neural Network, Modified Artificial Bee Colony is one of optimization algorithm that used to solve the optimal weight of Artificial Neural Network. In this study, the Bitcoin exchage rate against Rupiah starting September 1, 2017 to January 4, 2019 are used. Based on the training results obtained that MAPE value is 3,12% and the testing results obtained that MAPE value is 2,02%. This represent that the prediction results from Artificial Neural Network optimized by Modified Artificial Bee Colony algorithm are quite accurate because of small MAPE value.

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