scholarly journals Pemodelan Generator Uap Berbasis Jaringan Saraf Tiruan dengan Algoritme Pelatihan BPGD-ALAM

2016 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Fadhlia Annisa ◽  
Agfianto Eko Putra
Keyword(s):  
Neural Network ◽  
Steam Generator ◽  
Multiple Input Multiple Output ◽  
Network Models ◽  
Training Data ◽  
Training Process ◽  
Training Simulator ◽  
Neural Network Models ◽  
Validation Data ◽  
Testing Data

Steam generator is unit plant which has nonlinear and complex system with multiple-input-multiple-output (MIMO) configuration which is hard to be modeled. Whereas, steam generator model is very useful to create simulation such as operator training simulator (OTS). The purpose of this research is to obtain model of steam generator which has 8 output parameters and 9 input parameters based neural network (NN) with BPGD-ALAM training algorithm. Data had been taken from steam generator of PT. Chevron Pacific Indonesia, Duri and it is divided into three types, i.e training data, validation data and testing data. Training data was used to obtain model for each ouput through training process. Verification model is also done for each epoch using validation data to monitor training process whether overfitting occurs or not. Eight NN model of each output which is obtained from training and verification, is tested using testing data for getting its performance. From the reseach results, architecture of neural network models are obtained with various configuration for each output with RMSE value under 9.71 %. It shows that model which has been obtained, close with steam generator real system.

scholarly journals Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Charles H. Martin ◽  
Tongsu Peng ◽  
Michael W. Mahoney
Keyword(s):  
Neural Network ◽  
Power Law ◽  
Meta Analysis ◽  
Network Models ◽  
Training Data ◽  
Neural Network Models ◽  
Expected Performance ◽  
Testing Data ◽  
Heavy Tailed

AbstractIn many applications, one works with neural network models trained by someone else. For such pretrained models, one may not have access to training data or test data. Moreover, one may not know details about the model, e.g., the specifics of the training data, the loss function, the hyperparameter values, etc. Given one or many pretrained models, it is a challenge to say anything about the expected performance or quality of the models. Here, we address this challenge by providing a detailed meta-analysis of hundreds of publicly available pretrained models. We examine norm-based capacity control metrics as well as power law based metrics from the recently-developed Theory of Heavy-Tailed Self Regularization. We find that norm based metrics correlate well with reported test accuracies for well-trained models, but that they often cannot distinguish well-trained versus poorly trained models. We also find that power law based metrics can do much better—quantitatively better at discriminating among series of well-trained models with a given architecture; and qualitatively better at discriminating well-trained versus poorly trained models. These methods can be used to identify when a pretrained neural network has problems that cannot be detected simply by examining training/test accuracies.

Analysis of Fin-Tube Evaporator Performance With Limited Experimental Data Using Artificial Neural Networks

2000 ◽  
Author(s):  
Arturo Pacheco-Vega ◽  
Mihir Sen ◽  
Rodney L. McClain
Keyword(s):  
Neural Network ◽  
Heat Rate ◽  
Network Models ◽  
Activation Function ◽  
Operating Conditions ◽  
Training Data ◽  
Neural Network Models ◽  
The Neural Network ◽  
Artificial Neural ◽  
Fin Tube

Abstract In the current study we consider the problem of accuracy in heat rate estimations from artificial neural network models of heat exchangers used for refrigeration applications. The network configuration is of the feedforward type with a sigmoid activation function and a backpropagation algorithm. Limited experimental measurements from a manufacturer are used to show the capability of the neural network technique in modeling the heat transfer in these systems. Results from this exercise show that a well-trained network correlates the data with errors of the same order as the uncertainty of the measurements. It is also shown that the number and distribution of the training data are linked to the performance of the network when estimating the heat rates under different operating conditions, and that networks trained from few tests may give large errors. A methodology based on the cross-validation technique is presented to find regions where not enough data are available to construct a reliable neural network. The results from three tests show that the proposed methodology gives an upper bound of the estimated error in the heat rates.

scholarly journals Multiple Ensemble Neural Network Models with Fuzzy Response Aggregation for Predicting COVID-19 Time Series: The Case of Mexico

Healthcare ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 181 ◽  
Author(s):  
Patricia Melin ◽  
Julio Cesar Monica ◽  
Daniela Sanchez ◽  
Oscar Castillo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Fuzzy Logic ◽  
Network Models ◽  
Neural Network Models ◽  
Validation Data ◽  
Data Set ◽  
Ensemble Neural Networks ◽  
Ensemble Neural Network

In this paper, a multiple ensemble neural network model with fuzzy response aggregation for the COVID-19 time series is presented. Ensemble neural networks are composed of a set of modules, which are used to produce several predictions under different conditions. The modules are simple neural networks. Fuzzy logic is then used to aggregate the responses of several predictor modules, in this way, improving the final prediction by combining the outputs of the modules in an intelligent way. Fuzzy logic handles the uncertainty in the process of making a final decision about the prediction. The complete model was tested for the case of predicting the COVID-19 time series in Mexico, at the level of the states and the whole country. The simulation results of the multiple ensemble neural network models with fuzzy response integration show very good predicted values in the validation data set. In fact, the prediction errors of the multiple ensemble neural networks are significantly lower than using traditional monolithic neural networks, in this way showing the advantages of the proposed approach.

scholarly journals Neural Sign Language Translation Based on Human Keypoint Estimation

2019 ◽  
Vol 9 (13) ◽  
pp. 2683 ◽  
Author(s):  
Sang-Ki Ko ◽  
Chang Jo Kim ◽  
Hyedong Jung ◽  
Choongsang Cho
Keyword(s):  
Neural Network ◽  
Sign Language ◽  
Language Translation ◽  
Network Models ◽  
Training Data ◽  
Translation System ◽  
Body Parts ◽  
Neural Network Models ◽  
Translation Model ◽  
Starting Point

We propose a sign language translation system based on human keypoint estimation. It is well-known that many problems in the field of computer vision require a massive dataset to train deep neural network models. The situation is even worse when it comes to the sign language translation problem as it is far more difficult to collect high-quality training data. In this paper, we introduce the KETI (Korea Electronics Technology Institute) sign language dataset, which consists of 14,672 videos of high resolution and quality. Considering the fact that each country has a different and unique sign language, the KETI sign language dataset can be the starting point for further research on the Korean sign language translation. Using the KETI sign language dataset, we develop a neural network model for translating sign videos into natural language sentences by utilizing the human keypoints extracted from the face, hands, and body parts. The obtained human keypoint vector is normalized by the mean and standard deviation of the keypoints and used as input to our translation model based on the sequence-to-sequence architecture. As a result, we show that our approach is robust even when the size of the training data is not sufficient. Our translation model achieved 93.28% (55.28%, respectively) translation accuracy on the validation set (test set, respectively) for 105 sentences that can be used in emergency situations. We compared several types of our neural sign translation models based on different attention mechanisms in terms of classical metrics for measuring the translation performance.

scholarly journals Forecasting variance of NiftyIT index with RNN and DNN

2022 ◽  
Vol 2161 (1) ◽  
pp. 012005
Author(s):  
C R Karthik ◽  
Raghunandan ◽  
B Ashwath Rao ◽  
N V Subba Reddy
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Analysis ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Network Models ◽  
Training Data ◽  
Complex Data ◽  
Neural Network Models ◽  
Series Analysis

Abstract A time series is an order of observations engaged serially in time. The prime objective of time series analysis is to build mathematical models that provide reasonable descriptions from training data. The goal of time series analysis is to forecast the forthcoming values of a series based on the history of the same series. Forecasting of stock markets is a thought-provoking problem because of the number of possible variables as well as volatile noise that may contribute to the prices of the stock. However, the capability to analyze stock market leanings could be vital to investors, traders and researchers, hence has been of continued interest. Plentiful arithmetical and machine learning practices have been discovered for stock analysis and forecasting/prediction. In this paper, we perform a comparative study on two very capable artificial neural network models i) Deep Neural Network (DNN) and ii) Long Short-Term Memory (LSTM) a type of recurrent neural network (RNN) in predicting the daily variance of NIFTYIT in BSE (Bombay Stock Exchange) and NSE (National Stock Exchange) markets. DNN was chosen due to its capability to handle complex data with substantial performance and better generalization without being saturated. LSTM model was decided, as it contains intermediary memory which can hold the historic patterns and occurrence of the next prediction depends on the values that preceded it. With both networks, measures were taken to reduce overfitting. Daily predictions of the NIFTYIT index were made to test the generalizability of the models. Both networks performed well at making daily predictions, and both generalized admirably to make daily predictions of the NiftyIT data. The LSTM-RNN outpaced the DNN in terms of forecasting and thus, grips more potential for making longer-term estimates.

scholarly journals Use of Modular Neural Network for Heart Disease

2010 ◽  
pp. 196-201
Author(s):  
Harsh Vazirani ◽  
Rahul Kala ◽  
Anupam Shukla ◽  
Ritu Tiwari
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Network Models ◽  
Training Data ◽  
Neural Network Models ◽  
Modular Neural Network ◽  
Medical Disease ◽  
Research Areas ◽  
Product Method ◽  
Testing Accuracy

The medical field is very versatile field and one of the interested research areas for the scientist. It deals with many medical disease problems starting with the diagnosis of the disease, preventing from the disease and treatment for the disease. There are various types of medical disease and accordingly various types of treatment methods. In this paper we mostly concern about the diagnosis of the heart disease. Mainly two types of the diagnosis method are used one is manual and other is automatic diagnosis which consists of diagnosis of disease with the help of intelligent expert system. In this paper the modular neural network is used to diagnosis the heart disease. The attributes are divided and given to the two neural network models Backpropagation Neural Network (BPNN) and Radial Basis Function Neural Network (RBFNN) for training and testing. The two integration techniques are used two integrate the results and provide the final training accuracy and testing accuracy. The modular neural network with probabilistic product method gave an accuracy of 87.02% over training data and 85.88% over testing accuracy and with probabilistic product method gave an accuracy of 89.72% over training data and 84.70% over testing accuracy, which was experimentally determined to be better than monolithic neural networks.

System Identification for Nonlinear Maneuvering of Ships Using Neural Network

2010 ◽  
Vol 54 (01) ◽  
pp. 1-14
Author(s):  
G. Rajesh ◽  
G. Giri Rajasekhar ◽  
S. K. Bhattacharyya
Keyword(s):  
Neural Network ◽  
System Identification ◽  
Simulated Data ◽  
Network Models ◽  
Training Data ◽  
Neural Network Models ◽  
The Neural Network ◽  
Marquardt Algorithm ◽  
Hydrodynamic Derivatives ◽  
Hidden Layer

This paper deals with the application of nonparametric system identification to the nonlinear maneuvering of ships using neural network method. The maneuvering equations contain linear as well as nonlinear terms, and one does not attempt to determine the parameters (or hydrodynamic derivatives) associated with nonlinear terms, rather all nonlinear terms are clubbed together to form one unknown time function per equation, which are sought to be represented by neural network coefficients. The time series used in training the network are obtained from simulated data of zigzag and spiral maneuvers. The neural network has one middle or hidden layer of neurons and the Levenberg-Marquardt algorithm is used to obtain the network coefficients. Using the best choices for number of hidden layer neurons, length of training data, convergence tolerance, and so forth, the performances of the proposed neural network models have been investigated and conclusions drawn.

scholarly journals Development of a Matlab(R) Toolbox for the Design of Grey-Box Neural Models

2006 ◽  
Vol 1 (2) ◽  
pp. 7 ◽  
Author(s):  
Gonzalo Acuña ◽  
Erika Pinto
Keyword(s):  
Neural Network ◽  
Gaussian Noise ◽  
Network Models ◽  
Neural Models ◽  
Neural Network Models ◽  
Matlab Toolbox ◽  
Validation Data ◽  
Stirred Reactor ◽  
One Step ◽  
Design Construction

A Matlab Toolbox is developed for the design, construction and validation of grey-box neural network models. This toolbox, available in www.diinf.usach.cl=gacuna has been tested in simulations with a continuously stirred reactor process. The grey-box model performs well for validation data with 5% additive gaussian noise for one-step-ahead (OSA) and model-predictive-output (MPO) estimations.

scholarly journals Adversarial Image Perturbation with a Genetic Algorithm

2021 ◽  
Author(s):  
Rok Kukovec ◽  
Špela Pečnik ◽  
Iztok Fister Jr. ◽  
Sašo Karakatič
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Prior Knowledge ◽  
Network Models ◽  
Visual Assessment ◽  
Training Process ◽  
Neural Network Models ◽  
The Neural Network ◽  
Statistical Metrics

The quality of image recognition with neural network models relies heavily on filters and parameters optimized through the training process. These filters are di˙erent compared to how humans see and recognize objects around them. The di˙erence in machine and human recognition yields a noticeable gap, which is prone to exploitation. The workings of these algorithms can be compromised with adversarial perturbations of images. This is where images are seemingly modified imperceptibly, such that humans see little to no di˙erence, but the neural network classifies t he m otif i ncorrectly. This paper explores the adversarial image modifica-tion with an evolutionary algorithm, so that the AlexNet convolutional neural network cannot recognize previously clear motifs while preserving the human perceptibility of the image. The ex-periment was implemented in Python and tested on the ILSVRC dataset. Original images and their recreated counterparts were compared and contrasted using visual assessment and statistical metrics. The findings s uggest t hat t he human eye, without prior knowledge, will hardly spot the di˙erence compared to the original images.

scholarly journals A Hybrid Short-term Traffic Flow Forecasting Method Based on Neural Networks Combined with K-Nearest Neighbor

2018 ◽  
Vol 30 (4) ◽  
pp. 445-456 ◽  
Author(s):  
Zhao Liu ◽  
Jianhua Guo ◽  
Jinde Cao ◽  
Yun Wei ◽  
Wei Huang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Traffic Flow ◽  
Nearest Neighbor ◽  
Network Models ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Short Term ◽  
Neural Network Models ◽  
Traffic Forecasting

It is critical to implement accurate short-term traffic forecasting in traffic management and control applications. This paper proposes a hybrid forecasting method based on neural networks combined with the K-nearest neighbor (K-NN) method for short-term traffic flow forecasting. The procedure of training a neural network model using existing traffic input-output data, i.e., training data, is indispensable for fine-tuning the prediction model. Based on this point, the K-NN method was employed to reconstruct the training data for neural network models while considering the similarity of traffic flow patterns. This was done through collecting the specific state vectors that were closest to the current state vectors from the historical database to enhance the relationship between the inputs and outputs for the neural network models. In this study, we selected four different neural network models, i.e., back-propagation (BP) neural network, radial basis function (RBF) neural network, generalized regression (GR) neural network, and Elman neural network, all of which have been widely applied for short-term traffic forecasting. Using real world traffic data, the  experimental results primarily show that the BP and GR neural networks combined with the K-NN method have better prediction performance, and both are sensitive to the size of the training data. Secondly, the forecast accuracies of the RBF and Elman neural networks combined with the K-NN method both remain fairly stable with the increasing size of the training data. In summary, the proposed hybrid forecasting  approach outperforms the conventional forecasting models, facilitating the implementation of short-term  traffic forecasting in traffic management and control applications.

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