Iris Recognition Based on Pulsed Coupled Neural Networks

2013 ◽  
Vol 380-384 ◽  
pp. 2637-2640
Author(s):  
Hai Ying Deng ◽  
Dong Ming Zhou ◽  
Ren Can Nie ◽  
Xiang Li ◽  
Hong Mei Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Iris Recognition ◽  
Series Method ◽  
Recognition Method ◽  
One Dimensional ◽  
Coupled Neural Networks ◽  
Time Series Method ◽  
The One

Based on Pulsed Coupled Neural Network Model, respectively to study the one-dimensional time series, row time series, column time series will keep some invariance by image migration. To apply this nature in iris recognition field, found that to extraction features using row time series method, which can resist rotation changes between the same samples, and keep enough details to distinguish between different samples. Compared with the traditional iris recognition method which using iris phase structure encoded, the results show that row time series method, is less sensitive for phase, and can resist rotation changes better, is effective.

A new fuzzy time series method based on an ARMA-type recurrent Pi-Sigma artificial neural network

2019 ◽  
Vol 24 (11) ◽  
pp. 8243-8252 ◽  
Author(s):  
Cem Kocak ◽  
Ali Zafer Dalar ◽  
Ozge Cagcag Yolcu ◽  
Eren Bas ◽  
Erol Egrioglu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Fuzzy Time Series ◽  
Series Method ◽  
Time Series Method ◽  
Artificial Neural

scholarly journals ESTIMATION OF THE LYAPUNOV SPECTRUM FROM ONE-DIMENSIONAL OBSERVATIONS USING NEURAL NETWORKS

2014 ◽  
pp. 30-34
Author(s):  
Vladimir Golovko
Keyword(s):  
Neural Network ◽  
Dynamical System ◽  
Neural Networks ◽  
Time Series ◽  
Lyapunov Spectrum ◽  
Network Approach ◽  
Neural Network Approach ◽  
One Dimensional ◽  
The Neural Network ◽  
Attractor Dynamics

This paper discusses the neural network approach for computing of Lyapunov spectrum using one dimensional time series from unknown dynamical system. Such an approach is based on the reconstruction of attractor dynamics and applying of multilayer perceptron (MLP) for forecasting the next state of dynamical system from the previous one. It allows for evaluating the Lyapunov spectrum of unknown dynamical system accurately and efficiently only by using one observation. The results of experiments are discussed.

scholarly journals Classification Modeling Method for Near-Infrared Spectroscopy of Tobacco Based on Multimodal Convolution Neural Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Lei Zhang ◽  
Xiangqian Ding ◽  
Ruichun Hou
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Infrared Spectroscopy ◽  
Convolutional Neural Network ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Two Dimensional ◽  
One Dimensional ◽  
Nirs Data ◽  
The One

The origin of tobacco is the most important factor in determining the style characteristics and intrinsic quality of tobacco. There are many applications for the identification of tobacco origin by near-infrared spectroscopy. In order to improve the accuracy of the tobacco origin classification, a near-infrared spectrum (NIRS) identification method based on multimodal convolutional neural networks (CNN) was proposed, taking advantage of the strong feature extraction ability of the CNN. Firstly, the one-dimensional convolutional neural network (1-D CNN) is used to extract and combine the pattern features of one-dimensional NIRS data, and then the extracted features are used for classification. Secondly, the one-dimensional NIRS data are converted into two-dimensional spectral images, and the structure features are extracted from two-dimensional spectral images by the two-dimensional convolutional neural network (2-D CNN) method. The classification is performed by the combination of global and local training features. Finally, the influences of different network structure parameters on model identification performance are studied, and the optimal CNN models are selected and compared. The multimodal NIR-CNN identification models of tobacco origin were established by using NIRS of 5,200 tobacco samples from 10 major tobacco producing provinces in China and 3 foreign countries. The classification accuracy of 1-D CNN and 2-D CNN models was 93.15% and 93.05%, respectively, which was better than the traditional PLS-DA method. The experimental results show that the application of 1-D CNN and 2-D CNN can accurately and reliably distinguish the NIRS data, and it can be developed into a new rapid identification method of tobacco origin, which has an important promotion value.

scholarly journals Fuzzy Time Series and Artificial Neural Network: Forecasting Exportation of Natural Rubber in Malaysia

2021 ◽  
Vol 6 (1) ◽  
pp. 22-30
Author(s):  
Siti Nor Nadrah Muhamad ◽  
Shafeina Hatieqa Sofean ◽  
Balkiah Moktar ◽  
Wan Nurshazelin Wan Shahidan
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Natural Rubber ◽  
Mean Square Error ◽  
Fuzzy Time Series ◽  
Mean Square ◽  
Series Method ◽  
Time Series Method ◽  
Artificial Neural

Natural rubber is one of the most important crops in Malaysia alongside palm oil, cocoa, paddy, and pineapple. Being a tropical country, Malaysia is one of the top five exporters and producers of rubber in the world. The purpose of this study is to find the forecasted value of the actual data of the number of exportations of natural rubber by using Fuzzy Time Series and Artificial Neural Network. This study is also conducted to determine the best model by making comparison between Fuzzy Time Series and Artificial Neural Network. Fuzzy Time Series has allowed to overcome a downside where the classical time series method cannot deal with forecasting problem in which values of time series are linguistic terms represented by fuzzy sets. Artificial Neural Network was introduced as one of the systematic tools of modelling which has been forecasting for about 20 years ago. The error measure that was used in this study to make comparisons were Mean Square Error, Root Mean Square Error and Mean Absolute Percentage Error. The results of this study showed that the fuzzy time series method has the smallest error value compared to artificial neural network which means it was more accurate compared to artificial neural network in forecasting exportation of natural rubber in Malaysia.

ANÁLISE DA INFLUÊNCIA DOS PREÇOS DO SETOR SUCROENERGÉTICO NA ÁREA DE CANA-DE-AÇÚCAR NO ESTADO DE SÃO PAULO

2017 ◽  
Vol 32 (3) ◽  
pp. 280
Author(s):  
Jaqueline Zani dos Santos ◽  
Maura Seiko Tsutsui Esperancini ◽  
Eduardo De Masi ◽  
Leidiane Coelho Carvalho
Keyword(s):  
Time Series ◽  
Transfer Function ◽  
Multivariate Time Series ◽  
Sao Paulo ◽  
Energy Market ◽  
São Paulo ◽  
Series Method ◽  
Paulo State ◽  
Time Series Method ◽  
The One

- O trabalho objetivou identificar se há influência dos preços do açúcar, do etanol e da própria cana-de-açúcar na área plantada de cana-de-açúcar no estado de São Paulo, no período de 1995 a 2015. Adotou-se a metodologia proposta por Box-Jenkins de Função Transferência, que se constitui num método multivariado de séries temporais e que apresenta vantagens em relação aos métodos tradicionais de estimação. Os resultados demonstraram que os preços influenciaram o aumento na área de cana-de-açúcar, em especial, o preço do açúcar. Foi evidenciado também a existência de bidirecionalidade de efeito, com a área afetando os preços, e estes influenciando a área. Concluindo o estudo, foram feitas análises de intervenções para os principais eventos ocorridos no mercado sucroenergético para os anos em estudo, onde apenas a área de cana-de-açúcar se mostrou significativa, sendo influenciada por tais intervenções.PALAVRAS - CHAVE: Mercado sucroenergético, Desregulamentação, Função Transferência.INFLUENCE OF SUGAR-ENERGY SECTOR PRICES ON THE AREA OF SUGAR CANE IN SÃO PAULO STATEABSTRACT - The objective of this study was to identify whether there was an influence of sugar, ethanol, and sugarcane prices on sugarcane planted area in Sao Paulo state, from 1995 to 2015. The methodology used was the one proposed by Box-Jenkins, Transfer Function, which is a multivariate time series method and has advantages over traditional methods of estimation. The results showed that all prices have influenced the increase in sugarcane area, in particular, the sugar’s price. There was verified the existence of two-way effects, in which, the area affected the prices and these influenced the area. To conclude the study, analyzes of interventions were carried out for the main occurred events in sugar-energy market in the years under study. From this analysis, only the sugarcane area was significant, being influenced by the interventions.KEYWORDS: Sugar and energy market, deregulation, Transfer Function.

Potential Indicators Based Neural Networks for Cash Forecasting of an ATM

2016 ◽  
pp. 1545-1563
Author(s):  
Partha Sarathi Mishra ◽  
Satchidananda Dehuri
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Computational Finance ◽  
Optimal Solutions ◽  
Series Method ◽  
Automated Teller Machine ◽  
Time Series Method ◽  
Past Data ◽  
High Degree ◽  
Cash Demand

Cash forecasting is one of the important tasks in the domain of computational finance. A number of tools have been developed by various groups of researchers and are being used by banks or corporate to identify future cash needs. However, due to the high degree of non-linearity of the problem and surrounded by many local optimal solutions, this paper propose a multi-layer locally tuned perceptron (MLTP) to forecast the future needs and at the same time reduce the users frustration. It uses a fine tuned MLTP to forecast a daily cash demand of an automated teller machine (ATM). Further, potential indicators are used to making the model robust in terms of its efficiency and accuracy. The accuracy is compared against a traditional time series method. Furthermore, it is validated using the past data collected from the SBI ATM of Bhadrak district of Odisha, India. The performance of the method is encouraging. This system can be scaled for all branches of a bank in an area by incorporating historical data from these branches.

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