Chaos Based Network Initialization Approach for Feed Forward Artificial Neural Networks

2020 ◽  
Vol 17 (1) ◽  
pp. 418-424
Author(s):  
Sarfaraz Masoood ◽  
M. N. Doja ◽  
Pravin Chandra
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Chaotic Map ◽  
Learning Abilities ◽  
Feed Forward ◽  
Chebyshev Chaotic Map ◽  
Random Initialization ◽  
Initialization Scheme ◽  
Weight Initialization ◽  
Artificial Neural

Weight initialization of sigmoidal feed forward artificial neural network (SFFANN) and the Convolutional neural networks (CNN) has been a known factor which affects the learning abilities of the neural network. The uniform random weight initialization approach has been quite often used as the conventional network weight initial technique, due to its simplicity. However, various researches have shown that the random technique may not be the ideal choice of weight initialization for these neural networks. In this work, we analyze two separate chaotic functions and explore the possibilities of these being used as the weight initialization methods against the conventional random initialization technique for SFANNs as well as for the CNNs. For the SFFANNs, this analysis were done over 8 function approximation problems chosen for experimentation. The mean test error values along with a two sample t-test results strongly suggest that the Chebyshev chaotic map based weight initialization technique outperforms the conventional random initialization technique for most of the problems under consideration and hence may be used as an alternative weight initialization technique for the SFFANNs. For the CNN experiment, the MNIST dataset was used for analyzing the performance of the random and the Chebyshev based initialization scheme. Results strongly support the use of the Chebyshev chaotic map based initialization scheme as an alternate to the conventional random initialization.

Contributions to Predict the Malfunction Probability of the Human-Machine-Environment System, Using Artificial Neural Networks

2015 ◽  
Vol 760 ◽  
pp. 771-776
Author(s):  
Daniel Constantin Anghel ◽  
Nadia Belu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Design Experiment ◽  
Manufacturing Processes ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Linear Relationships ◽  
The Neural Network ◽  
Artificial Neural

This paper presents the application of Artificial Neural Networks to predict the malfunction probability of the human-machine-environment system, in order to provide some guidance to designers of manufacturing processes. Artificial Neural Networks excel in gathering difficult non-linear relationships between the inputs and outputs of a system. We used, in this work, a feed forward neural network in order to predict the malfunction probability. The neural network is simulated with Matlab. The design experiment presented in this paper was realized at University of Pitesti, at the Faculty of Mechanics and Technology, Technology and Management Department.

scholarly journals Transmembrane Alpha Helix Prediction Using Neural Networks

2020 ◽  
pp. 102-104
Author(s):  
Saumitra Singh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Transmembrane Protein ◽  
Alpha Helix ◽  
Feed Forward ◽  
Protein Class ◽  
Artificial Neural

This paper presents a simple artificial neural network which classifies proteins into two classes from their sequences alone: the alpha helix transmembrane protein class and the non-alpha helix transmembrane protein class. The network described here has a simple feed-forward topology and a limited number of neurons which makes it very fast.

Health Insurance Claim Prediction Using Artificial Neural Networks

2020 ◽  
Vol 9 (3) ◽  
pp. 40-57
Author(s):  
Sam Goundar ◽  
Suneet Prakash ◽  
Pranil Sadal ◽  
Akashdeep Bhardwaj
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Insurance Company ◽  
Feed Forward ◽  
Medical Claims ◽  
Artificial Neural

A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. This amount needs to be included in the yearly financial budgets. Inappropriate estimating generally has negative effects on the overall performance of the business. This study presents the development of artificial neural network model that is appropriate for predicting the anticipated annual medical claims. Once the implementation of the neural network models was finished, the focus was to decrease the mean absolute percentage error by adjusting the parameters, such as epoch, learning rate, and neurons in different layers. Both feed forward and recurrent neural networks were implemented to forecast the yearly claims amount. In conclusion, the artificial neural network model that was implemented proved to be an effective tool for forecasting the anticipated annual medical claims for BSP Life. Recurrent neural network outperformed the feed forward neural network in terms of accuracy and computation power required to carry out the forecasting.

River flow forecasting using different artificial neural network algorithms and wavelet transform

2009 ◽  
Vol 36 (1) ◽  
pp. 26-38 ◽  
Author(s):  
Turgay Partal
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Back Propagation ◽  
Propagation Model ◽  
River Flows ◽  
Feed Forward ◽  
Feed Forward Back Propagation ◽  
Artificial Neural

In this study, the wavelet–neural network structure that combines wavelet transform and artificial neural networks has been employed to forecast the river flows of Turkey. Discrete wavelet transforms, which are useful to obtain to the periodic components of the measured data, have significantly positive effects on artificial neural network modeling performance. Generally, the feed-forward back-propagation method was studied with respect to artificial neural network applications to water resources data. In this study, the performance of generalized neural networks and radial basis neural networks were compared with feed-forward back-propagation methods. Six different models were studied for forecasting of monthly river flows. It was seen that the wavelet and feed-forward back-propagation model was superior to the other models in terms of selected performance criteria.

scholarly journals Implementasi Metode Backpropogation dengan Inisialisasi Bobot Nguyen Widrow untuk Peramalan Harga Saham

2019 ◽  
Vol 6 (1) ◽  
pp. 49
Author(s):  
Eliv Kurniawan ◽  
Hari Wibawanto ◽  
Djoko Adi Widodo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Stock Prices ◽  
Stock Price ◽  
Daily Data ◽  
Backpropagation Method ◽  
Weight Initialization ◽  
Artificial Neural

<p>Jaringan saraf tiruan merupakan suatu ilmu yang terus berkembang pesat hingga saat ini. Jaringan saraf tiruan merupakan suatu ilmu komputasi yang didasarkan dan terinspirasi dari cara kerja sistem saraf manusia. Sama halnya dengan sistem saraf manusia, jaringan saraf tiruan bekerja melalui proses pembelajaran terhadap data-data yang sudah ada untuk memformulakan keluaran dari data-data baru. Jaringan saraf tiruan dengan metode backpropagation mampu melakukan peramalan untuk data nonlinear seperti bentuk data harian harga saham. Salah satu algoritma inisialisasi bobot yang dapat meningkatkan waktu eksekusi adalah nguyen-widrow. Pada penelitian ini akan dilakukan implementasi metode backpropagation dengan inisialisasi bobot nguyen widrow untuk meramalkan harga saham. Proses implementasi melalui 3 tahapan, yaitu preprosesing data, pelatihan jaringan, dan pengujian jaringan. Hasil dari penelitian ini menunjukkan bahwa pelatihan jaringan saraf tiruan dengan jumlah dataset yang banyak membutuhkan perhitungan yang kompleks, sehingga jaringan saraf tiruan dengan arsitektur jaringan yang sederhana kurang efektif dan dapat terjebak pada titik lokal minimum. Hasil peramalan untuk harga close saham BBCA.JK memiliki nilai MAPE 0,85% dan untuk harga close saham AALI.JK memiliki nilai MAPE sebesar 1,84%.</p><p><em><strong>Abstract</strong></em></p><p><em>Artificial neural network is a hot topic and invite a lot of admiration in the last decade. Artificial Neural Network is one of the artificial representations of the humans brain who always try to simulate the learning process of the humans brain. Artificial neural network with backpropagation method is able to forecast nonlinear data such as daily data form stock price. One of the weight initialization algorithms that can be increase the execution time is nguyen-widrow. In this research will be implemented backpropagation method with nguyen widrow weight initialization to forecast stock prices. The process of implementation through 3 stages, that is preprosesing data, training, and testing or simulate. The results of this research indicate that the training of artificial neural networks with many datasets required a complex calculations, so the artificial neural network with simple architectures is less effective and can get stuck at minimum local points. The results forecasting for the close price of BBCA.JK have a MAPE value 0.85% and for the close price of AALI.JK have 1.84% of MAPE value</em></p>

scholarly journals Brain Tumor Classification Using Neural Network Based Methods

2012 ◽  
pp. 112-117 ◽  
Author(s):  
Kailash D. Kharat ◽  
Pradyumna P. Kulkarni
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Brain Tumor ◽  
Object Recognition ◽  
Magnetic Resonance ◽  
Tumor Classification ◽  
Brain Images ◽  
Feed Forward ◽  
Artificial Neural

MRI (Magnetic resonance Imaging) brain tumor images Classification is a difficult task due to the variance and complexity of tumors. This paper presents two Neural Network techniques for the classification of the magnetic resonance human brain images. The proposed Neural Network technique consists of three stages, namely, feature extraction, dimensionality reduction, and classification. In the first stage, we have obtained the features related with MRI images using discrete wavelet transformation (DWT). In the second stage, the features of magnetic resonance images (MRI) have been reduced using principles component analysis (PCA) to the more essential features. In the classification stage, two classifiers based on supervised machine learning have been developed. The first classifier based on feed forward artificial neural network (FF-ANN) and the second classifier based on Back-Propagation Neural Network. The classifiers have been used to classify subjects as normal or abnormal MRI brain images. Artificial Neural Networks (ANNs) have been developed for a wide range of applications such as function approximation, feature extraction, optimization, and classification. In particular, they have been developed for image enhancement, segmentation, registration, feature extraction, and object recognition and classification. Among these, object recognition and image classification is more important as it is a critical step for high-level processing such as brain tumor classification. Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), Hopfield, Cellular, and Pulse-Coupled neural networks have been used for image segmentation. These networks can be categorized into feed-forward (associative) and feedback (auto-associative) networks..

scholarly journals Modeling of Chlorine and Coagulant Dose in a Water Treatment Plant by Artificial Neural Networks

2019 ◽  
Vol 9 (3) ◽  
pp. 4176-4181
Author(s):  
A. S. Kote ◽  
D. V. Wadkar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Treatment ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Dose Model ◽  
Artificial Neural

Coagulation and chlorination are complex processes of a water treatment plant (WTP). Determination of coagulant and chlorine dose is time-consuming. Many times WTP operators in India determine the coagulant and chlorine dose approximately using their experience, which may lead to the use of excess or insufficient dose. Hence, there is a need to develop prediction models to determine optimum chlorine and coagulant doses. In this paper, artificial neural networks (ANN) are used for prediction due to their ability to learn and model non-linear and complex relationships. Separate ANN models for chlorine and coagulant doses are explored with radial basis neural network (RBFNN), feed-forward neural network (FFNN), cascade feed forward neural network (CFNN) and generalized regression neural network (GRNN). For modeling, daily water quality data of the last four years are collected from the plant laboratory of WTP in Maharashtra (India). In order to improve performance, these models are established by varying input variables, hidden nodes, training functions, spread factor, and epochs. The best models are selected based on the comparison of performance measures. It is observed that the best performing chlorine dose model using defined statistics is found to be RBFNN with R=0.999. Similarly, the CFNN coagulant dose model with Bayesian regularization (BR) training function provided excellent estimates with network architecture (2-40-1) and R=0.947. Based on the above models, two graphical user interfaces (GUIs) were developed for real-time prediction of chlorine and coagulant dose, which will be useful for plant operators and decision makers.

Health Insurance Claim Prediction Using Artificial Neural Networks

2022 ◽  
pp. 1174-1193
Author(s):  
Sam Goundar ◽  
Suneet Prakash ◽  
Pranil Sadal ◽  
Akashdeep Bhardwaj
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Medical Claims ◽  
Artificial Neural

A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. This amount needs to be included in the yearly financial budgets. Inappropriate estimating generally has negative effects on the overall performance of the business. This study presents the development of artificial neural network model that is appropriate for predicting the anticipated annual medical claims. Once the implementation of the neural network models was finished, the focus was to decrease the mean absolute percentage error by adjusting the parameters, such as epoch, learning rate, and neurons in different layers. Both feed forward and recurrent neural networks were implemented to forecast the yearly claims amount. In conclusion, the artificial neural network model that was implemented proved to be an effective tool for forecasting the anticipated annual medical claims for BSP Life. Recurrent neural network outperformed the feed forward neural network in terms of accuracy and computation power required to carry out the forecasting.

scholarly journals PREDIKSI HARGA EMAS MENGGUNAKAN FEED FORWARD NEURAL NETWORK DENGAN METODE EXTREME LEARNING MACHINE

2019 ◽  
Vol 8 (2) ◽  
pp. 171-183
Author(s):  
Nisa Afida Izati ◽  
Budi Warsito ◽  
Tatik Widiharih
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Extreme Learning Machine ◽  
Gold Price ◽  
Good Prediction ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Artificial Neural ◽  
Learning Machine

The prediction of gold price aims to find out the gold price in the future on the basis of historical data on gold prices in the past, so it can be used as a consideration by gold investors to investing in gold. Prediction methods that do not require assumptions, one of which is Artificial Neural Networks. In this study, using Artificial Neural Networks, Feed Forward Neural Network with Extreme Learning Machine (ELM). ELM is a non-iterative algorithm so ELM has advantages in process speed. The input weight and bias for this method are determined randomly. After that, to find the final weight using the Moore-Penrose Generalized Inverse calculation on the hidden layer output matrix. The best model selection criteria uses the Mean Absolute Percentage Error (MAPE). This study shows that the results of the training and testing process from the model 1 input neuron and 7 hidden neurons are very good, because it produces MAPE training = 0.6752% and MAPE testing = 0.8065%. Also gives a very good prediction result because it has MAPE = 0.5499% Keywords: gold price, Extreme Learning Machine, MAPE

Abstract 16866: Artificial Neural Networks as Prediction Tools: Predicting Permanent Pacemaker Implantation (PPMI) in Patients Undergoing Transcatheter Aortic Valve Replacement (TAVR) Utilizing a Shallow Feed-Forward Neural Network

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Anshul Parulkar ◽  
Wasiq Sheikh ◽  
Malik B Ahmed ◽  
Sachit Singal ◽  
Fabio V Lima ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Pacemaker Implantation ◽  
Paravalvular Leak ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Increased Risk ◽  
Test Sets ◽  
Artificial Neural

Introduction: Despite increasing TAVR volumes after a series of recent favorable clinical trials, adverse outcomes remain frequent, including new or worsened conduction disease requiring PPMI, life-threatening bleeding, paravalvular leak, and stroke. PPMI carries a reported incidence ranging from 8.8-14.6% and is of particular concern given the increased risk of mortality and rehospitalization. New techniques in signal processing may inform novel statistical approaches to better predict PPMI from a set of clinical variables. Artificial neural networks (ANN) comprise a family of algorithms that utilize non-linear activation functions to enable improved prediction of PPMI in TAVR patients. Objective: To examine the predictive utility of a feed-forward neural network in classifying PPM implantation in patients undergoing TAVR. Methods: Pre and post-operative data from a single institution were collected for all patients undergoing TAVR without prior pacemaker implantation from January 2016 to December 2019. Data was imported into Matlab, partitioned into training, validation, and test sets, and processed in a two-layer feed-forward neural network with sigmoid hidden and softmax output neurons. Performance data included confusion matrices and receiver operating characteristic (ROC) curves. Results: The total sample size for the cohort was 513 patients with a PPMI incidence of 8.6%. The training set contained 40 variables and 359 patients, while the validation and test sets contained 77 patients each. The final optimized model showed cross-entropy of 0.25 with 6 iterations and an area under ROC curve of 0.73. Overall model accuracy was 92.7% in the validation set and 88.3% in the test set. Conclusions: In summary, we show that feed-forward neural networks can be useful in processing multiple interdependent variables to aid clinical prediction. Our network demonstrated modest discriminatory ability in predicting the need for PPMI after TAVR.

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