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 TIME SERIES FORECASTING USING ARTIFICIAL NEURAL NETWORK WITH EXTENDED ADAPTIVE LEARNING

2021 ◽  
Vol 6 (4) ◽  
Author(s):  
Mr. Dhanaji Vilas Mirajkar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Network ◽  
Adaptive Learning ◽  
Learning Algorithm ◽  
Learning Rate ◽  
Experimental Result ◽  
Adaptive Learning Rate ◽  
Artificial Neural

Artificial neural network (ANN) mainly consists of learning algorithms, which are require to optimize the convergence of neural networks. We need to optimize the convergence of neural networks in order to improve the speed and accuracy of decision making process. To enable the optimization process one of the widely used algorithm is back propagation learning algorithm. Objective of study is to applied backpropagation algorithm for solving multivariate time series problem. To better the accuracy of neural network it is important to find optimized architecture for the problem under consideration. The learning rate is also an important factor which affects the performance of result. In this study, we proposed extended adaptive learning approach in which learning rate is adapted from number of previous iteration error trend in first half of training. In next half of training learning rate is adapted as per adaptive learning rate algorithm. Compare performance of three variation of backpropagationalgorithm. All these variation experimented on two standard dataset. Experimental result shows that during validation and training ANN with extended adaptive learning rate outperforms other than two variations.

ARTIFICIAL NEURAL NETWORK BASED SOFT ESTIMATOR FOR ESTIMATION OF TRANSDUCER STATIC NONLINEARITY

2004 ◽  
Vol 14 (04) ◽  
pp. 237-246 ◽  
Author(s):  
AMAR PARTAP SINGH ◽  
TARA SINGH KAMAL ◽  
SHAKTI KUMAR
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Single Layer ◽  
Functional Link ◽  
Static Nonlinearity ◽  
Feed Forward Back Propagation ◽  
Direct Model ◽  
Artificial Neural

In this work, the development of an Artificial Neural Network (ANN) based soft estimator is reported for the estimation of static-nonlinearity associated with the transducers. Under the realm of ANN based transducer modeling, only two neural models have been suggested to estimate the static-nonlinearity associated with the transducers with quite successful results. The first existing model is based on the concept of a functional link artificial neural network (FLANN) trained with μ-LMS (Least Mean Squares) learning algorithm. The second one is based on the architecture of a single layer linear ANN trained with α-LMS learning algorithm. However, both these models suffer from the problem of slow convergence (learning). In order to circumvent this problem, it is proposed to synthesize the direct model of transducers using the concept of a Polynomial-ANN (polynomial artificial neural network) trained with Levenberg-Marquardt (LM) learning algorithm. The proposed Polynomial-ANN oriented transducer model is implemented based on the topology of a single-layer feed-forward back-propagation-ANN. The proposed neural modeling technique provided an extremely fast convergence speed with increased accuracy for the estimation of transducer static nonlinearity. The results of convergence are very stimulating with the LM learning algorithm.

Improved estimation of energy expenditure by artificial neural network modeling

2008 ◽  
Vol 33 (6) ◽  
pp. 1213-1222 ◽  
Author(s):  
Dean Charles Hay ◽  
Akinobu Wakayama ◽  
Ken Sakamura ◽  
Senshi Fukashiro
Keyword(s):  
Neural Network ◽  
Heart Rate ◽  
Artificial Neural Network ◽  
Energy Expenditure ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Validation Data ◽  
Artificial Neural Network Modeling ◽  
Feed Forward Back Propagation ◽  
Artificial Neural

Estimation of energy expenditure in daily living conditions can be a tool for clinical assessment of health status, as well as a self-measure of lifestyle and general activity levels. Criterion measures are either prohibitively expensive or restricted to laboratory settings. Portable devices (heart rate monitors, pedometers) have gained recent popularity, but accuracy of the prediction equations remains questionable. This study applied an artificial neural network modeling approach to the problem of estimating energy expenditure with different dynamic inputs (accelerometry, heart rate above resting (HRar), and electromyography (EMG)). Nine feed-forward back-propagation models were trained, with the goal of minimizing the mean squared error (MSE) of the training datasets. Model 1 (accelerometry only) and model 2 (HRar only) performed poorly and had significantly greater MSE than all other models (p < 0.001). Model 3 (combined accelerometry and HRar) had overall performance similar to EMG models. Validation of all models was performed by simulating untrained datasets. MSE of all models increased when tested with validation data. While models 1 and 2 again performed poorly, model 3 MSE was lower than all but 2 EMG models. Squared correlation coefficients of measured and predicted energy expenditure for models 3 to 9 ranged from 0.745 to 0.817. Analysis of mean error within specific movement categories indicates that EMG models may be better at predicting higher-intensity energy expenditure, but combined accelerometry and HRar provides an economical solution, with sufficient accuracy.

scholarly journals Modeling Nonlinear Transfer Functions from Speech Envelopes to Encephalography with Neural Networks

2019 ◽  
Vol 11 (4) ◽  
pp. 1 ◽  
Author(s):  
Tobias de Taillez ◽  
Florian Denk ◽  
Bojana Mirkovic ◽  
Birger Kollmeier ◽  
Bernd T. Meyer
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Linear Models ◽  
Transfer Functions ◽  
Event Related Potentials ◽  
Stimulus Presentation ◽  
Related Potentials ◽  
Linear And Nonlinear ◽  
Artificial Neural

Diferent linear models have been proposed to establish a link between an auditory stimulus and the neurophysiological response obtained through electroencephalography (EEG). We investigate if non-linear mappings can be modeled with deep neural networks trained on continuous speech envelopes and EEG data obtained in an auditory attention two-speaker scenario. An artificial neural network was trained to predict the EEG response related to the attended and unattended speech envelopes. After training, the properties of the DNN-based model are analyzed by measuring the transfer function between input envelopes and predicted EEG signals by using click-like stimuli and frequency sweeps as input patterns. Using sweep responses allows to separate the linear and nonlinear response components also with respect to attention. The responses from the model trained on normal speech resemble event-related potentials despite the fact that the DNN was not trained to reproduce such patterns. These responses are modulated by attention, since we obtain significantly lower amplitudes at latencies of 110 ms, 170 ms and 300 ms after stimulus presentation for unattended processing in contrast to the attended. The comparison of linear and nonlinear components indicates that the largest contribution arises from linear processing (75%), while the remaining 25% are attributed to nonlinear processes in the model. Further, a spectral analysis showed a stronger 5 Hz component in modeled EEG for attended in contrast to unattended predictions. The results indicate that the artificial neural network produces responses consistent with recent findings and presents a new approach for quantifying the model properties.

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.

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