Identification of Complex System Based on Neural Network

2012 ◽  
Vol 433-440 ◽  
pp. 4342-4347
Author(s):  
Zhen Hai Dou ◽  
Ya Jing Wang
Keyword(s):  
Neural Network ◽  
Complex System ◽  
Mean Squared Error ◽  
Model Identification ◽  
Complex Model ◽  
System Model ◽  
Mathematics Model ◽  
Identification Method ◽  
Squared Error ◽  
Actual Output

In order to conquer the difficulty of building up the mathematics model of some complex system, model identification method based on neural network is put forward. By this method, according to actual sample datum, the complex model of crude oil heating furnace is identified at appropriate quantity of net layers and notes. The identification results show that output of model can basically consistent with the actual output and their mean squared error (MSE) almost is 0. Therefore, model identification method based on neural network is an effective method in complex system identification.

scholarly journals Prediksi Indeks Harga Saham Gabungan (IHSG) Menggunakan Algoritma Neural Network

2018 ◽  
Vol 4 (1) ◽  
pp. 24
Author(s):  
Imam Halimi ◽  
Wahyu Andhyka Kusuma
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Linear Regression ◽  
Mean Squared Error ◽  
Composite Index ◽  
T Test ◽  
Sliding Windows ◽  
Root Mean Squared Error ◽  
Squared Error

Investasi saham merupakan hal yang tidak asing didengar maupun dilakukan. Ada berbagai macam saham di Indonesia, salah satunya adalah Indeks Harga Saham Gabungan (IHSG) atau dalam bahasa inggris disebut Indonesia Composite Index, ICI, atau IDX Composite. IHSG merupakan parameter penting yang dipertimbangkan pada saat akan melakukan investasi mengingat IHSG adalah saham gabungan. Penelitian ini bertujuan memprediksi pergerakan IHSG dengan teknik data mining menggunakan algoritma neural network dan dibandingkan dengan algoritma linear regression, yang dapat dijadikan acuan investor saat akan melakukan investasi. Hasil dari penelitian ini berupa nilai Root Mean Squared Error (RMSE) serta label tambahan angka hasil prediksi yang didapatkan setelah dilakukan validasi menggunakan sliding windows validation dengan hasil paling baik yaitu pada pengujian yang menggunakan algoritma neural network yang menggunakan windowing yaitu sebesar 37,786 dan pada pengujian yang tidak menggunakan windowing sebesar 13,597 dan untuk pengujian algoritma linear regression yang menggunakan windowing yaitu sebesar 35,026 dan pengujian yang tidak menggunakan windowing sebesar 12,657. Setelah dilakukan pengujian T-Test menunjukan bahwa pengujian menggunakan neural network yang dibandingkan dengan linear regression memiliki hasil yang tidak signifikan dengan nilai T-Test untuk pengujian dengan windowing dan tanpa windowing hasilnya sama, yaitu sebesar 1,000.

scholarly journals Forecasting Wind Power Generation Using Artificial Neural Network: “Pawan Danawi”—A Case Study from Sri Lanka

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Amila T. Peiris ◽  
Jeevani Jayasinghe ◽  
Upaka Rathnayake
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Wind Power ◽  
Wind Farm ◽  
Mean Squared Error ◽  
Wind Power Generation ◽  
Training Algorithms ◽  
Training Algorithm ◽  
Squared Error ◽  
Ann Models

Wind power, as a renewable energy resource, has taken much attention of the energy authorities in many countries, as it is used as one of the major energy sources to satisfy the ever-increasing energy demand. However, careful attention is needed in identifying the wind power potential in a particular area due to climate changes. In this sense, forecasting both wind power generation and wind power potential is essential. This paper develops artificial neural network (ANN) models to forecast wind power generation in “Pawan Danawi”, a functioning wind farm in Sri Lanka. Wind speed, wind direction, and ambient temperature of the area were used as the independent variable matrices of the developed ANN models, while the generated wind power was used as the dependent variable. The models were tested with three training algorithms, namely, Levenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), and Bayesian Regularization (BR) training algorithms. In addition, the model was calibrated for five validation percentages (5% to 25% in 5% intervals) under each algorithm to identify the best training algorithm with the most suitable training and validation percentages. Mean squared error (MSE), coefficient of correlation (R), root mean squared error ratio (RSR), Nash number, and BIAS were used to evaluate the performance of the developed ANN models. Results revealed that all three training algorithms produce acceptable predictions for the power generation in the Pawan Danawi wind farm with R > 0.91, MSE < 0.22, and BIAS < 1. Among them, the LM training algorithm at 70% of training and 5% of validation percentages produces the best forecasting results. The developed models can be effectively used in the prediction of wind power at the Pawan Danawi wind farm. In addition, the models can be used with the projected climatic scenarios in predicting the future wind power harvest. Furthermore, the models can acceptably be used in similar environmental and climatic conditions to identify the wind power potential of the area.

scholarly journals Novel FTLRNN with Gamma Memory for Short-Term and Long-Term Predictions of Chaotic Time Series

2009 ◽  
Vol 2009 ◽  
pp. 1-21
Author(s):  
Sanjay L. Badjate ◽  
Sanjay V. Dudul
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Mean Squared Error ◽  
Chaotic Time Series ◽  
Nonstationary Time Series ◽  
Medium Term ◽  
Short Term ◽  
Squared Error ◽  
Wide Range

Multistep ahead prediction of a chaotic time series is a difficult task that has attracted increasing interest in the recent years. The interest in this work is the development of nonlinear neural network models for the purpose of building multistep chaotic time series prediction. In the literature there is a wide range of different approaches but their success depends on the predicting performance of the individual methods. Also the most popular neural models are based on the statistical and traditional feed forward neural networks. But it is seen that this kind of neural model may present some disadvantages when long-term prediction is required. In this paper focused time-lagged recurrent neural network (FTLRNN) model with gamma memory is developed for different prediction horizons. It is observed that this predictor performs remarkably well for short-term predictions as well as medium-term predictions. For coupled partial differential equations generated chaotic time series such as Mackey Glass and Duffing, FTLRNN-based predictor performs consistently well for different depths of predictions ranging from short term to long term, with only slight deterioration after k is increased beyond 50. For real-world highly complex and nonstationary time series like Sunspots and Laser, though the proposed predictor does perform reasonably for short term and medium-term predictions, its prediction ability drops for long term ahead prediction. However, still this is the best possible prediction results considering the facts that these are nonstationary time series. As a matter of fact, no other NN configuration can match the performance of FTLRNN model. The authors experimented the performance of this FTLRNN model on predicting the dynamic behavior of typical Chaotic Mackey-Glass time series, Duffing time series, and two real-time chaotic time series such as monthly sunspots and laser. Static multi layer perceptron (MLP) model is also attempted and compared against the proposed model on the performance measures like mean squared error (MSE), Normalized mean squared error (NMSE), and Correlation Coefficient (r). The standard back-propagation algorithm with momentum term has been used for both the models.

Application of Artificial Neural Network Over Nickel-Based Catalyst for Combined Steam-Carbon Dioxide of Methane Reforming (CSDRM)

2020 ◽  
Vol 20 (9) ◽  
pp. 5716-5719 ◽  
Author(s):  
Cho Hwe Kim ◽  
Young Chul Kim
Keyword(s):  
Neural Network ◽  
Carbon Dioxide ◽  
Artificial Neural Network ◽  
Mean Squared Error ◽  
Coefficient Of Determination ◽  
Feed Ratio ◽  
Squared Error ◽  
Highly Nonlinear ◽  
Feed Forward Network ◽  
Artificial Neural

The application of artificial neural network (ANN) for modeling, combined steam-carbon dioxide reforming of methane over nickel-based catalysts, was investigated. The artificial neural network model consisted of a 3-layer feed forward network, with hyperbolic tangent function. The number of hidden neurons is optimized by minimization of mean square error and maximization of R2 (R square, coefficient of determination) and set of 8 neurons. With feed ratio, flow rate, and temperature as independent variables, methane, carbon dioxide conversion, and H2/CO ratio, were measured using artificial neural network. Coefficient of determination (R2) values of 0.9997, 0.9962, and 0.9985 obtained, and MAE (Mean Absolute Error), MSE (Mean Squared Error), RMSE (Root Mean Squared Error), and MAPE (Mean Absolute Percentage Error) showed low value. This study indicates ANN can successfully model a highly nonlinear process and function.

Using BPNN to Predict Number of Low-Income Households in Taiwan Based on Seasonally Adjusted Annualized Rates for Real GDP

2014 ◽  
Vol 1044-1045 ◽  
pp. 1824-1827
Author(s):  
Yi Ti Tung ◽  
Tzu Yi Pai
Keyword(s):  
Neural Network ◽  
Low Income ◽  
Mean Squared Error ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Percentage Error ◽  
Real Gdp ◽  
Squared Error ◽  
Input Variables ◽  
Low Income Households

In this study, the back-propagation neural network (BPNN) was used to predict the number of low-income households (NLIH) in Taiwan, taking the seasonally adjusted annualized rates (SAAR) for real gross domestic product (GDP) as input variables. The results indicated that the lowest mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and highest correlation coefficient (R) for training and testing were 4.759 % versus 19.343 %, 24429972.268 versus 781839890.859, 4942.669 versus 27961.400, and 0.945 versus 0.838, respectively.

scholarly journals RANDOM NEURAL NETWORK LEARNING HEURISTICS

2017 ◽  
Vol 31 (4) ◽  
pp. 436-456 ◽  
Author(s):  
Abbas Javed ◽  
Hadi Larijani ◽  
Ali Ahmadinia ◽  
Rohinton Emmanuel
Keyword(s):  
Neural Network ◽  
Mean Squared Error ◽  
Training Algorithms ◽  
Local Minima ◽  
Neural Network Learning ◽  
Bee Colony ◽  
Squared Error ◽  
Random Neural Network ◽  
Evolution Algorithms ◽  
Learning Heuristics

The random neural network (RNN) is a probabilitsic queueing theory-based model for artificial neural networks, and it requires the use of optimization algorithms for training. Commonly used gradient descent learning algorithms may reside in local minima, evolutionary algorithms can be also used to avoid local minima. Other techniques such as artificial bee colony (ABC), particle swarm optimization (PSO), and differential evolution algorithms also perform well in finding the global minimum but they converge slowly. The sequential quadratic programming (SQP) optimization algorithm can find the optimum neural network weights, but can also get stuck in local minima. We propose to overcome the shortcomings of these various approaches by using hybridized ABC/PSO and SQP. The resulting algorithm is shown to compare favorably with other known techniques for training the RNN. The results show that hybrid ABC learning with SQP outperforms other training algorithms in terms of mean-squared error and normalized root-mean-squared error.

scholarly journals Penerapan Jaringan Saraf Tiruan Dalam Memprediksi Indikator Utama Ekonomi Dunia

2018 ◽  
Vol 2 (2) ◽  
pp. 169
Author(s):  
Alan Boy Sandy Damanik ◽  
Agung Bimantoro
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Mean Squared Error ◽  
Economic Indicator ◽  
Squared Error ◽  
The World ◽  
Artificial Neural Network Method ◽  
World Economic ◽  
Architectural Models ◽  
The Mean

Economics is one of the most important aspects in the world. Economics greatly determines the progress and development of a country. However, there are still many countries with low economic levels. Therefore the aim of this study is to predict and determine the level of the main indicators of the world economy as one of the anticipatory steps to further increase the level of the country's economy. World Economic Indicator Data to be used is sourced from Bloomberg and Bank Indonesia. To find out further developments, it is necessary to research the existing data. The algorithm used is Backpropagatian Neural Network. Data analysis was carried out using artificial neural network method using Matlab R2011b software. The study uses 5 architectural models. The best network architecture produced is 3-43-1 with an accuracy rate of 86% and the Mean Squared Error (MSE) value is 1.336593.

scholarly journals Predicting Vodka Adulteration: A Combination of Electronic Tongue and Artificial Neural Networks

2021 ◽  
Author(s):  
Leonardo Fabio León Marenco ◽  
Luiza Pereira Oliveira ◽  
Daniella Lopez Vale ◽  
Maiara Oliveira Salles
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Mean Squared Error ◽  
Tap Water ◽  
Electronic Tongue ◽  
Classification Model ◽  
Squared Error ◽  
Artificial Neural ◽  
Voltammetric Electronic Tongue

Abstract An artificial neural network was used to build models caple of predicting and quantifying vodka adulteration with methanol and/or tap water. A voltammetric electronic tongue based on gold and copper microelectrodes was used, and 310 analyses were performed. Vodkas were adulterated with tap water (5 to 50% (v/v)), methanol (1 to 13% (v/v)), and with a fixed addition of 5% methanol and tap water varying from 5 to 50% (v/v). The classification model showed 99.5% precision, and it correctly predicted the type of adulterant in all samples. Regarding the regression model, the root mean squared error was 3.464% and 0.535% for the water and methanol addition, respectively, and the prediction of the adulterant content presented an R2 0.9511 for methanol and 0.9831 for water adulteration.

scholarly journals HiCNN2: Enhancing the Resolution of Hi-C Data Using an Ensemble of Convolutional Neural Networks

Genes ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 862
Author(s):  
Tong Liu ◽  
Zheng Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Mean Squared Error ◽  
Correlation Coefficients ◽  
Chromatin Interactions ◽  
Squared Error ◽  
Genome Wide

We present a deep-learning package named HiCNN2 to learn the mapping between low-resolution and high-resolution Hi-C (a technique for capturing genome-wide chromatin interactions) data, which can enhance the resolution of Hi-C interaction matrices. The HiCNN2 package includes three methods each with a different deep learning architecture: HiCNN2-1 is based on one single convolutional neural network (ConvNet); HiCNN2-2 consists of an ensemble of two different ConvNets; and HiCNN2-3 is an ensemble of three different ConvNets. Our evaluation results indicate that HiCNN2-enhanced high-resolution Hi-C data achieve smaller mean squared error and higher Pearson’s correlation coefficients with experimental high-resolution Hi-C data compared with existing methods HiCPlus and HiCNN. Moreover, all of the three HiCNN2 methods can recover more significant interactions detected by Fit-Hi-C compared to HiCPlus and HiCNN. Based on our evaluation results, we would recommend using HiCNN2-1 and HiCNN2-3 if recovering more significant interactions from Hi-C data is of interest, and HiCNN2-2 and HiCNN if the goal is to achieve higher reproducibility scores between the enhanced Hi-C matrix and the real high-resolution Hi-C matrix.

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