Статті в журналах: "Recurrent Elman neural network"

1

Wutsqa, Dhoriva Urwatul, and Anisa Nurjanah. "Breast Cancer Classification Using Fuzzy Elman Recurrent Neural Network." Journal of Advanced Research in Dynamical and Control Systems 11, no. 11-SPECIAL ISSUE (November 20, 2019): 946–53. http://dx.doi.org/10.5373/jardcs/v11sp11/20193119.

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Mohana Sundaram, N., and S. N. Sivanandam. "A hybrid elman neural network predictor for time series prediction." International Journal of Engineering & Technology 7, no. 2.20 (April 18, 2018): 159. http://dx.doi.org/10.14419/ijet.v7i2.20.12799.

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Artificial Neural Networks have become popular in the world of prediction and forecasting due to their nonlinear nonparametric adaptive-learning property. They become an important tool in data analysis and data mining applications. Elman neural network due to its recurrent nature and dynamic processing capabilities can perform the prediction process with a good range of accuracy. In this paper an Elman recurrent Neural Network is hybridised with a time delay called a tap delay line for time series prediction process to improve its performance. The Elman neural network with the time delay inputs is trained tested and validated using the solar sun spot time series data that contains the monthly mean sunspot numbers for a 240 year period having 2899 data values. The results confirm that the proposed Elman network hybridised with time delay inputs can predict the time series with more accurately and effectively than the existing methods.

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Aribowo, Widi. "ELMAN-RECURRENT NEURAL NETWORK FOR LOAD SHEDDING OPTIMIZATION." SINERGI 24, no. 1 (January 14, 2020): 29. http://dx.doi.org/10.22441/sinergi.2020.1.005.

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Load shedding plays a key part in the avoidance of the power system outage. The frequency and voltage fluidity leads to the spread of a power system into sub-systems and leads to the outage as well as the severe breakdown of the system utility. In recent years, Neural networks have been very victorious in several signal processing and control applications. Recurrent Neural networks are capable of handling complex and non-linear problems. This paper provides an algorithm for load shedding using ELMAN Recurrent Neural Networks (RNN). Elman has proposed a partially RNN, where the feedforward connections are modifiable and the recurrent connections are fixed. The research is implemented in MATLAB and the performance is tested with a 6 bus system. The results are compared with the Genetic Algorithm (GA), Combining Genetic Algorithm with Feed Forward Neural Network (hybrid) and RNN. The proposed method is capable of assigning load releases needed and more efficient than other methods.

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Jiwa Permana, Agus Aan, and Widodo Prijodiprodjo. "Sistem Evaluasi Kelayakan Mahasiswa MagangMenggunakan Elman Recurrent Neural Network." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 8, no. 1 (January 31, 2014): 37. http://dx.doi.org/10.22146/ijccs.3494.

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AbstrakJaringan Syaraf Tiruan (JST) dapat digunakan untuk memecahkan permasalahan tertentu seperti prediksi, klasifikasi, pengolahan data, dan robotik.Berdasarkan paparan tersebut, sehingga dalam penelitian ini mencoba menerapkan JST untuk menangani permasalahan dalam program magang yang sedang dihadapi dalam upaya untuk meningkatkan kompetensi, pengalaman, serta melatih softskill mahasiswa.Sistem yang dikembangkan dapat digunakan untuk mengevaluasi kelayakan mahasiswa dalam program magang ke luar daerah dengan menerapkan Elman Recurrent Neural Network (ERNN), sehingga dapat memberikan informasi yang akurat kepada pihak jurusan untuk menentukan keputusan yang tepat.Struktur Elman dipilih karena dapat membuat iterasi jauh lebih cepat sehingga memudahkan proses konvergensi. Adapun metode pembelajaran yang digunakan adalah Backpropagation ThroughTime dengan model epochwise training mode. Sistem diimplementasikan dengan menggunakan bahasa pemrograman C# dengan basis data MySQL. Vektor input yang digunakan terdiri dari 11 variabel. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan akan cepat mengalami konvergen dan mampu mencapai nilai error paling optimal (minimum error) apabila menggunakan 1 hidden layer dengan jumlah neuron 20 unit. Akurasi terbaik dapat diperoleh dengan menggunakan LR sebesar 0.01 dan momentum 0.85 dimana akurasi rata-rata dalam pengujian mencapai 87.50%. Kata kunci—Evaluasi, Kelayakan, Jaringan Syaraf Tiruan (JST), Elman Recurrent Neural Network, Magang Abstract Artificial Neural Network (ANN) can be used to solve specific problems such as prediction, classification, data processing, and robotics. Based on the exposure, so in this study tried to apply neural networks to handle problems in apprentice program facing in an effort to increase the competence, experience and soft skills training students. The system developed can be used to evaluate the students in the apprentice program to other regions by applying the Elman Recurrent Neural Network (ERNN), so it can provide accurate information to the department to determine appropriate decisions. Elman structure was chosen because it can be create much more rapidly iterations so as to facilitate the convergence process. The learning method used is Backpropagation Through Time with model epochwise training mode. The system is implemented using the C # programming language with a MySQL database. Input vector used consists of 11 variables. The results showed that the developed system will rapidly converge and can reach optimal error value (minimum error) when using one hidden layer with 20 units number of neurons. Best accuracy can be obtained using the LR of 0.01 and momentum 0.85 which average accuracy reaches 87.50% in testing. Keywords—Evaluation, Feasibility, Artificial Neural Network (ANN), Elman Recurrent Neural Network, Apprenticeship

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You, Wen Xia, Jun Xiao Chang, Zi Heng Zhou, and Ji Lu. "Short-Term Load Forecasting Based on GA-Elman Model." Advanced Materials Research 986-987 (July 2014): 520–23. http://dx.doi.org/10.4028/www.scientific.net/amr.986-987.520.

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Elman Neural Network is a typical neural-network which shares the characteristics of multiple-layer and dynamic recurrent, and it’s more suitable than BP Neural Network when it’s applied to forecast the short-term load with periodicity and similarity. To solve the problem that Elman Neural Network lacks learning efficiency, GA-Elman model is established by optimizing the weights and thresholds using Genetic Algorithm. An example is then given to prove the effectiveness of GA-Elman model, using the load data of a certain region. Relative error and MSE have been considered as criterions to analyze the results of load forecasting. By comparing the results calculated by BP, Elman and GA-Elman model, the effectiveness of GA-Elman model is verified, which will improve the accuracy of short-term load forecasting.

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Wang, Jie, Jun Wang, Wen Fang, and Hongli Niu. "Financial Time Series Prediction Using Elman Recurrent Random Neural Networks." Computational Intelligence and Neuroscience 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/4742515.

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In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective function. By analyzing the proposed model with the linear regression, complexity invariant distance (CID), and multiscale CID (MCID) analysis methods and taking the model compared with different models such as the backpropagation neural network (BPNN), the stochastic time effective neural network (STNN), and the Elman recurrent neural network (ERNN), the empirical results show that the proposed neural network displays the best performance among these neural networks in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, TWSE, KOSPI, and Nikkei225 with the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. The experimental results show that this approach gives good performance in predicting the values from the stock market indices.

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Radjabaycolle, Jefri, and Reza Pulungan. "PREDIKSI PENGGUNAAN BANDWIDTH MENGGUNAKAN ELMAN RECURRENT NEURAL NETWORK." BAREKENG: Jurnal Ilmu Matematika dan Terapan 10, no. 2 (December 1, 2016): 127–35. http://dx.doi.org/10.30598/barekengvol10iss2pp127-135.

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Jaringan Syaraf Tiruan (JST) sering dipakai dalam menyelesaikan permasalahan tertentu seperti prediksi, klasifikasi, dan pengolahan data. Berdasarkan hal tersebut, dalam penelitian ini mencoba menerapkan JST untuk menangani permasalahan dalam prediksi penggunaan bandwidth. Sistem yang dikembangkan dapat digunakan untuk memprediksi pengunaan bandwidth dengan menerapkan Elman Recurrent Neural Network (ERNN). Struktur Elman dipilih karena dapat membuat iterasi jauh lebih cepat sehingga memudahkan proses konvergensi.. Vektor input yang digunakan menggunakan windows size. Hasil penelitian dengan menggunakan target error sebesar 0.001 menunjukkan nilai MSE terkecil yaitu pada windows size 11 dengan nilai 0.002833. Kemudian dengan menggunakan 13 neuron pada hidden layer diperoleh nilai error paling optimal (minimum error) sebesar 0.003725.

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Wang, Fang, Sai Tang, and Menggang Li. "Advantages of Combining Factorization Machine with Elman Neural Network for Volatility Forecasting of Stock Market." Complexity 2021 (May 22, 2021): 1–12. http://dx.doi.org/10.1155/2021/6641298.

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With a focus in the financial market, stock market dynamics forecasting has received much attention. Predicting stock market fluctuations is usually challenging due to the nonlinear and nonstationary time series of stock prices. The Elman recurrent network is renowned for its capability of dealing with dynamic information, which has made it a successful application to predicting. We developed a hybrid approach which combined Elman recurrent network with factorization machine (FM) technique, i.e., the FM-Elman neural network, to predict stock market volatility. In this paper, the Standard & Poor’s 500 Composite Stock Price (S&P 500) index, the Dow Jones industrial average (DJIA) index, the Shanghai Stock Exchange Composite (SSEC) index, and the Shenzhen Securities Component Index (SZI) were used to demonstrate the validity of our proposed FM-Elman model in time-series prediction. The results were compared with predictions obtained from the other two models which are basic BP neural network and the Elman neural network. Some experiments showed that the FM-Elman model outperforms others through different accuracy measures. Furthermore, the effects of volatility degree on prediction performance from different stock indexes were investigated. An interesting phenomenon had been found through some numerical experiments on the effects of different user-specified dimensions on the proposed FM-Elman neural network.

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9

Wang, Qiang. "Intelligent Identification of Flow Regime Based on a Novel Neural Network." Applied Mechanics and Materials 635-637 (September 2014): 1715–18. http://dx.doi.org/10.4028/www.scientific.net/amm.635-637.1715.

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A noveol neural network of Elman is typically dynamic recurrent neural network. A novel method of flow regime identification based on Elman neural network and wavelet packet decomposition is proposed in this paper. Above all, the collected pressure-difference fluctuation signals are decomposed by the four-layer wavelet packet, and the decomposed signals in various frequency bands are obtained within the frequency domain. Then the wavelet packet energy eigenvectors of flow regimes are established. At last the wavelet packet energy eigenvectors are input into Elman neural network and flow regime intelligent identification can be performed.

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10

Lin, Chih-Min, and Enkh-Amgalan Boldbaatar. "Autolanding Control Using Recurrent Wavelet Elman Neural Network." IEEE Transactions on Systems, Man, and Cybernetics: Systems 45, no. 9 (September 2015): 1281–91. http://dx.doi.org/10.1109/tsmc.2015.2389752.

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Lv, Xiao Ren, Xuan Luo, Shi Jie Wang, and Rui Nie. "Short-Term Prediction on the Time Series of PCP Speed Based on Elman Neural Network." Advanced Materials Research 569 (September 2012): 749–53. http://dx.doi.org/10.4028/www.scientific.net/amr.569.749.

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Elman neural network is a classical kind of recurrent neural network. It is well suitable to predict complicated nonlinear dynamics system like progressing cavity pump (PCP) speed due to its greater properties of calculation and adaptation to time-varying with the comparison of BP neural network. This paper provides one method to create, predict, and decide the model of PCP speed based on Elman neural network. At the same time, short-term prediction is made on time series of PCP speed using this model. The results of the experiment show that the model owns higher precision, steadier forecasting effect and more rapid convergence velocity, displaying that this kind of model based on Elman neural network is feasible and efficient to predict short-term PCP speed.

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12

Wei, Lin, Yongqing Wu, Hua Fu, and Yuping Yin. "Modeling and Simulation of Gas Emission Based on Recursive Modified Elman Neural Network." Mathematical Problems in Engineering 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/9013839.

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For the purpose of achieving more effective prediction of the absolute gas emission quantity, this paper puts forward a new model based on the hidden recurrent feedback Elman. The recursive part of classic Elman cannot be adjusted because it is fixed. To a certain extent, this drawback affects the approximation ability of the Elman, so this paper adds the correction factors in recursive part and uses the error feedback to determine the parameters. The stability of the recursive modified Elman neural network is proved in the sense of Lyapunov stability theory, and the optimal learning rate is given. With the historical data of mine actual monitoring to experiment and analysis, the results show that the recursive modified Elman neural network model can effectively predict the gas emission and improve the accuracy and efficiency of prediction compared with the classic Elman prediction model.

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13

Saghafifar, Mahmoud, Anjan Kundu, and Andrew Nafalski. "Dynamic magnetic hysteresis modelling using Elman recurrent neural network." International Journal of Applied Electromagnetics and Mechanics 13, no. 1-4 (December 17, 2002): 209–14. http://dx.doi.org/10.3233/jae-2002-323.

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Liu, Bo, Qilin Wu, and Qian Cao. "An Improved Elman Network for Stock Price Prediction Service." Security and Communication Networks 2020 (September 3, 2020): 1–9. http://dx.doi.org/10.1155/2020/8824430.

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The rapid development of edge computing drives the rapid development of stock market prediction service in terminal equipment. However, the traditional prediction service algorithm is not applicable in terms of stability and efficiency. In view of this challenge, an improved Elman neural network is proposed in this paper. Elman neural network is a typical dynamic recurrent neural network that can be used to provide the stock price prediction service. First, the prediction model parameters and build process are analysed in detail. Then, the historical data of the closing price of Shanghai composite index and the opening price of Shenzhen composite index are collected for training and testing, so as to predict the prices of the next trading day. Finally, the experiment results validate that it is effective to predict the short-term future stock price by using the improved Elman neural network model.

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Dada, Emmanuel Gbenga, Hurcha Joseph Yakubu, and David Opeoluwa Oyewola. "Artificial Neural Network Models for Rainfall Prediction." European Journal of Electrical Engineering and Computer Science 5, no. 2 (April 2, 2021): 30–35. http://dx.doi.org/10.24018/ejece.2021.5.2.313.

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Rainfall prediction is an important meteorological problem that can greatly affect humanity in areas such as agriculture production, flooding, drought, and sustainable management of water resources. The dynamic and nonlinear nature of the climatic conditions have made it impossible for traditional techniques to yield satisfactory accuracy for rainfall prediction. As a result of the sophistication of climatic processes that produced rainfall, using quantitative techniques to predict rainfall is a very cumbersome task. The paper proposed four non-linear techniques such as Artificial Neural Networks (ANN) for rainfall prediction. ANN has the capacity to map different input and output patterns. The Feed Forward Neural Network (FFNN), Cascade Forward Neural Network (CFNN), Recurrent Neural Network (RNN), and Elman Neural Network (ENN) were used to predict rainfall. The dataset used for this work contains some meteorological variables such as temperature, wind speed, humidity, rainfall, visibility, and others for the year 2015-2019. Simulation results indicated that of all the proposed Neural Network (NN) models, the Elman NN model produced the best performance. We also found out that Elman NN has the best performance for the year 2018 (having the lowest RMSE, MSE, and MAE of 6.360, 40.45, and 0.54 respectively). The results indicated that NN algorithms are robust, dependable, and reliable algorithms that can be used for daily, monthly, or yearly rainfall prediction.

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Wang, He Yi, and Xu Chang Yang. "Elman's Recurrent Neural Network Applied to Forecasting Algal Dynamic Variation in Gonghu Bay." Advanced Materials Research 779-780 (September 2013): 1352–58. http://dx.doi.org/10.4028/www.scientific.net/amr.779-780.1352.

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This paper describes the training, validation and application of recurrent neural network (RNN) models to computing the algal dynamic variation at three sites in Gonghu Bay of Lake Taihu in summer. The input variables of Elmans RNN were selected by means of the canonical correspondence analysis (CCA) and Chl_a concentration as output variable. Sequentially, the conceptual models for Elmans RNN were established and the Elman models were trained and validated on daily data set. The values of Chl_a concentration computed by the models were closely related to their respective values measured at the three sites. The correlation coefficient (R2) between the predicted Chl_a concentration by the model and the observed value were 0.86-0.92. And sensitivity analysis was performed to clarify the algal dynamic variation to the change of environmental factors. The results show that the CCA can efficiently ascertain appropriate input variables for Elmans RNN, the Elmans RNN can precisely forecast the Chl_a concentration at three different sites in Gonghu Bay of Lake Taihu in summer and sensitivity analysis validated the algal dynamic variation to the change of environmental factors which were selected by CCA.

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Fan, Bo, Zhixin Yang, Wei Xu, and Xianbo Wang. "Rotor Resistance Online Identification of Vector Controlled Induction Motor Based on Neural Network." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/831839.

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Rotor resistance identification has been well recognized as one of the most critical factors affecting the theoretical study and applications of AC motor’s control for high performance variable frequency speed adjustment. This paper proposes a novel model for rotor resistance parameters identification based on Elman neural networks. Elman recurrent neural network is capable of performing nonlinear function approximation and possesses the ability of time-variable characteristic adaptation. Those influencing factors of specified parameter are analyzed, respectively, and various work states are covered to ensure the completeness of the training samples. Through signal preprocessing on samples and training dataset, different input parameters identifications with one network are compared and analyzed. The trained Elman neural network, applied in the identification model, is able to efficiently predict the rotor resistance in high accuracy. The simulation and experimental results show that the proposed method owns extensive adaptability and performs very well in its application to vector controlled induction motor. This identification method is able to enhance the performance of induction motor’s variable-frequency speed regulation.

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Wysocki, Antoni, and Maciej Ławryńczuk. "Elman neural network for modeling and predictive control of delayed dynamic systems." Archives of Control Sciences 26, no. 1 (March 1, 2016): 117–42. http://dx.doi.org/10.1515/acsc-2016-0007.

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The objective of this paper is to present a modified structure and a training algorithm of the recurrent Elman neural network which makes it possible to explicitly take into account the time-delay of the process and a Model Predictive Control (MPC) algorithm for such a network. In MPC the predicted output trajectory is repeatedly linearized on-line along the future input trajectory, which leads to a quadratic optimization problem, nonlinear optimization is not necessary. A strongly nonlinear benchmark process (a simulated neutralization reactor) is considered to show advantages of the modified Elman neural network and the discussed MPC algorithm. The modified neural model is more precise and has a lower number of parameters in comparison with the classical Elman structure. The discussed MPC algorithm with on-line linearization gives similar trajectories as MPC with nonlinear optimization repeated at each sampling instant.

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Salman, Afan Galih, and Yen Lina Prasetio. "Implementasi Jaringan Syaraf Tiruan Recurrent Dengan Metode Pembelajaran Gradient Descent Adaptive Learning Rate Untuk Pendugaan Curah Hujan Berdasarkan Peubah Enso." ComTech: Computer, Mathematics and Engineering Applications 1, no. 2 (December 1, 2010): 418. http://dx.doi.org/10.21512/comtech.v1i2.2384.

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The use of technology of technology Artificial Neural Network (ANN) in prediction of rainfall can be done using the learning approach. ANN prediction accuracy measured by the coefficient of determination (R2) and Root Mean Square Error (RMSE).This research employ a recurrent optimized heuristic Artificial Neural Network (ANN) Recurrent Elman gradient descent adaptive learning rate approach using El-Nino Southern Oscilation (ENSO) variable, namely Wind, Southern Oscillation Index (SOI), Sea Surface Temperatur (SST) dan Outgoing Long Wave Radiation (OLR) to forecast regional monthly rainfall. The patterns of input data affect the performance of Recurrent Elman neural network in estimation process. The first data group that is 75% training data and 25% testing data produce the maximum R2 69.2% at leap 0 while the second data group that is 50% training data & 50% testing data produce the maximum R2 53.6%.at leap 0 Our result on leap 0 is better than leap 1,2 or 3.

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Kodama, Osamu, LukĂˇĹˇ Pichl, and Taisei Kaizoji. "REGIME CHANGE AND TREND PREDICTION FOR BITCOIN TIME SERIES DATA." CBU International Conference Proceedings 5 (September 23, 2017): 384–88. http://dx.doi.org/10.12955/cbup.v5.954.

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Bitcoin time series dataset recording individual transactions denominated in Euro at the COINBASE market between April 23, 2015 and August 15, 2016 is analyzed. Markov switching model is applied to classify the regions of varying volatility represented by three hidden state regimes using univariate autoregressive model and dependent mixture model. Causality extraction and price prediction of daily BTCEUR exchange rates is performed by means of a recurrent neural network using the standard Elman model. Strong correlations is found between the normalized mean squared error of the Elman network (out-of-sample 5-day-ahead prediction) and the realized volatility (sum of minute returns squared throughout the trading day). The present approach is calibrated using simulated regime change in standard econometric models. Our results clearly demonstrate the applicability of recurrent neural networks to causality extraction even in the case of highly volatile cryptocurrency exchange rate time series data.

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Nawi, Nazri Mohd, Abdullah Khan, M. Z. Rehman, Haruna Chiroma, and Tutut Herawan. "Weight Optimization in Recurrent Neural Networks with Hybrid Metaheuristic Cuckoo Search Techniques for Data Classification." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/868375.

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Recurrent neural network (RNN) has been widely used as a tool in the data classification. This network can be educated with gradient descent back propagation. However, traditional training algorithms have some drawbacks such as slow speed of convergence being not definite to find the global minimum of the error function since gradient descent may get stuck in local minima. As a solution, nature inspired metaheuristic algorithms provide derivative-free solution to optimize complex problems. This paper proposes a new metaheuristic search algorithm called Cuckoo Search (CS) based on Cuckoo bird’s behavior to train Elman recurrent network (ERN) and back propagation Elman recurrent network (BPERN) in achieving fast convergence rate and to avoid local minima problem. The proposed CSERN and CSBPERN algorithms are compared with artificial bee colony using BP algorithm and other hybrid variants algorithms. Specifically, some selected benchmark classification problems are used. The simulation results show that the computational efficiency of ERN and BPERN training process is highly enhanced when coupled with the proposed hybrid method.

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Cao, Shengxian, Yu Wang, and Zhenhao Tang. "Adaptive Elman Model of Gene Regulation Network Based on Time Series Data." Current Bioinformatics 14, no. 6 (July 16, 2019): 551–61. http://dx.doi.org/10.2174/1574893614666190126145431.

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Background:Time series expression data of genes contain relations among different genes, which are difficult to model precisely. Slime-forming bacteria is one of the three major harmful bacteria types in industrial circulating cooling water systems.Objective:This study aimed at constructing gene regulation network(GRN) for slime-forming bacteria to understand the microbial fouling mechanism.Methods:For this purpose, an Adaptive Elman Neural Network (AENN) to reveal the relationships among genes using gene expression time series is proposed. The parameters of Elman neural network were optimized adaptively by a Genetic Algorithm (GA). And a Pearson correlation analysis is applied to discover the relationships among genes. In addition, the gene expression data of slime-forming bacteria by transcriptome gene sequencing was presented.Results:To evaluate our proposed method, we compared several alternative data-driven approaches, including a Neural Fuzzy Recurrent Network (NFRN), a basic Elman Neural Network (ENN), and an ensemble network. The experimental results of simulated and real datasets demonstrate that the proposed approach has a promising performance for modeling Gene Regulation Networks (GRNs). We also applied the proposed method for the GRN construction of slime-forming bacteria and at last a GRN for 6 genes was constructed.Conclusion:The proposed GRN construction method can effectively extract the regulations among genes. This is also the first report to construct the GRN for slime-forming bacteria.

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Chandra, Rohitash. "Memetic cooperative coevolution of Elman recurrent neural networks." Soft Computing 18, no. 8 (October 24, 2013): 1549–59. http://dx.doi.org/10.1007/s00500-013-1160-1.

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Alamsyah, Alamsyah, Budi Prasetiyo, M. Faris Al Hakim, and Fadli Dony Pradana. "Prediction of COVID-19 Using Recurrent Neural Network Model." Scientific Journal of Informatics 8, no. 1 (May 10, 2021): 98–103. http://dx.doi.org/10.15294/sji.v8i1.30070.

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The COVID-19 case that infected humans was first discovered in China at the end of 2019. Since then, COVID-19 has spread to almost all countries in the world. To overcome this problem, it takes a quick effort to identify humans infected with COVID-19 more quickly. One of the alternative diagnoses for potential COVID-19 disease is Recurrent Neural Network (RNN). In this paper, RNN is implemented using the Elman network and applied to the COVID-19 dataset from Kaggle. The dataset consists of 70% training data and 30% test data. The learning parameters used were the maximum epoch, learning late, and hidden nodes. The research results show the percentage of accuracy is 88.

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Tantriawan, H., I. S. Sitanggang, L. Syaufina, and H. Harsa. "Temporal prediction of carbon monoxide using the Elman Recurrent Neural Network." IOP Conference Series: Earth and Environmental Science 203 (December 10, 2018): 012009. http://dx.doi.org/10.1088/1755-1315/203/1/012009.

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Wutsqa, Dhoriva Urwatul, Martina Ayun Pamungkas, and Retno Subekti. "Black-Litterman Model with Views Prediction Using Elman Recurrent Neural Network." Universal Journal of Accounting and Finance 9, no. 6 (December 2021): 1297–311. http://dx.doi.org/10.13189/ujaf.2021.090609.

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Escudero, Pedro, Willian Alcocer, and Jenny Paredes. "Recurrent Neural Networks and ARIMA Models for Euro/Dollar Exchange Rate Forecasting." Applied Sciences 11, no. 12 (June 18, 2021): 5658. http://dx.doi.org/10.3390/app11125658.

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Analyzing the future behaviors of currency pairs represents a priority for governments, financial institutions, and investors, who use this type of analysis to understand the economic situation of a country and determine when to sell and buy goods or services from a particular location. Several models are used to forecast this type of time series with reasonable accuracy. However, due to the random behavior of these time series, achieving good forecasting performance represents a significant challenge. In this paper, we compare forecasting models to evaluate their accuracy in the short term using data on the EUR/USD exchange rate. For this purpose, we used three methods: Autoregressive Integrated Moving Average (ARIMA), Recurrent Neural Network (RNN) of the Elman type, and Long Short-Term Memory (LSTM). The analyzed period spanned from 2 January 1998, to 31 December 2019, and was divided into training and validation datasets. We performed forecasting calculations to predict windows with six different forecasting horizons. We found that the window of one month with 22 observations better matched the validation dataset in the short term compared to the other windows. Theil’s U coefficients calculated for this window were 0.04743, 0.002625, and 0.001808 for the ARIMA, Elman, and LSTM networks, respectively. LSTM provided the best forecast in the short term, while Elman provided the best forecast in the long term.

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Yan, Ji Hong, and P. X. Wang. "Prognosis of Blade Material Fatigue Using Elman Neural Networks." Applied Mechanics and Materials 10-12 (December 2007): 558–62. http://dx.doi.org/10.4028/www.scientific.net/amm.10-12.558.

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Prognosis of major components such as blades, rotors, valves of steam turbine is crucial to reducing operating and maintenance costs. Prognostic strategies can assist to detect, classify and predict developing faults, guarantee reliable, efficient and continuous operation of electric plants, and may even result in saving lives. In this paper, a recurrent neural network based strategy was developed for blade material degradation assessment and fatigue damage propagation prediction. Two Elman Neural Networks were developed for fatigue severity assessment and trend prediction correspondingly. The performance of the proposed prognostic methodology was evaluated by using blade material fatigue data collected from a material testing system. The prognostic method is found to be a reliable and robust material fatigue predictor.

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Hidayat, Rahmat, and Besse Helmi Mustawinar. "PEMODELAN JUMLAH WISATAWAN DENGAN AUTOREGRESSIVE INTEGRATED MOVING AVERAGE DAN RECURRENT ARTIFICIAL NEURAL NETWORK." Mathline : Jurnal Matematika dan Pendidikan Matematika 7, no. 1 (February 23, 2022): 53–65. http://dx.doi.org/10.31943/mathline.v7i1.262.

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Tourism has become a priority area for Indonesia's economic development. Tourism is expected to be one of the main drivers of Indonesia's economic growth through job and business creation, foreign currency earnings and infrastructure development. In addition, tourism can be used to introduce the identity and culture of the community. Therefore, tourism development will continue and increase through the expansion and utilization of national tourism resources and possibilities. In this study, the number of foreign tourist arrivals will be predicted using the ARIMA model and the Elman-RNN model, given that the pattern of tourist arrivals is not always linear. The data used is the data from the survey results of the Central Statistics Agency. The data is divided into two parts, namely in-sample data and out-sample data. Of the two models, the model of the Elman-RNN network is the best model with the smallest MAPE and RSME values.

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Hošovský, Alexander, Ján Piteľ, and Kamil Židek. "Enhanced Dynamic Model of Pneumatic Muscle Actuator with Elman Neural Network." Abstract and Applied Analysis 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/906126.

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To make effective use of model-based control system design techniques, one needs a good model which captures system’s dynamic properties in the range of interest. Here an analytical model of pneumatic muscle actuator with two pneumatic artificial muscles driving a rotational joint is developed. Use of analytical model makes it possible to retain the physical interpretation of the model and the model is validated using open-loop responses. Since it was considered important to design a robust controller based on this model, the effect of changed moment of inertia (as a representation of uncertain parameter) was taken into account and compared with nominal case. To improve the accuracy of the model, these effects are treated as a disturbance modeled using the recurrent (Elman) neural network. Recurrent neural network was preferred over feedforward type due to its better long-term prediction capabilities well suited for simulation use of the model. The results confirm that this method improves the model performance (tested for five of the measured variables: joint angle, muscle pressures, and muscle forces) while retaining its physical interpretation.

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Li, Lin, Xiaolei Yu, Zhenlu Liu, Zhimin Zhao, Chao Wu, Ke Zhang, and Shanhao Zhou. "Optimization of RFID reading performance based on YOLOv3 and Elman neural network." Journal of Intelligent & Fuzzy Systems 42, no. 3 (February 2, 2022): 2581–94. http://dx.doi.org/10.3233/jifs-211838.

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As a non-contact automatic identification technology, Radio Frequency Identification (RFID) is of great significance to improve the simultaneous identification of multi-target. This paper designs a more efficient and accurate multi-tag reading performance measurement system based on the fusion of YOLOv3 and Elman neural network. In the machine vision subsystem, multi-tag images are collected by dual CCD and detected by neural network algorithm. The reading distance of 3D distributed multi-tag is measured by laser ranging to evaluate the reading performance of RFID system. Firstly, the multi-tag are detected by YOLOv3, which realizes the measurement of 3D coordinates, improves the prediction accuracy, enhances the recognition ability of small targets, and improves the accuracy of 3D coordinate detection. Secondly, the relationship between the 3D coordinates and the corresponding reading distance of RFID multi-tag are modelled by Elman recurrent neural network. Finally, the reading performance of RFID multi-tag is optimized. Compared with the state-of-the-arts, the multi-tag detection rate of YOLOv3 is 17.4% higher and the time is 3.27 times higher than that of the previous template matching algorithm. In terms of reading performance, the MAPE of Elman neural network is 1.46 %, which is at least 21.43 % higher than other methods. In running time, Elman only needs 1.69s, which is at least 28.40% higher than others. Thus, the system not only improves the accuracy, but also improves the speed, which provides a new insight for the measurement and optimization of RFID performance.

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Ali, Yasir Hassan, Roslan Abd Rahman, and Raja Ishak Raja Hamzah. "Artificial Neural Network Model for Monitoring Oil Film Regime in Spur Gear Based on Acoustic Emission Data." Shock and Vibration 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/106945.

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The thickness of an oil film lubricant can contribute to less gear tooth wear and surface failure. The purpose of this research is to use artificial neural network (ANN) computational modelling to correlate spur gear data from acoustic emissions, lubricant temperature, and specific film thickness (λ). The approach is using an algorithm to monitor the oil film thickness and to detect which lubrication regime the gearbox is running either hydrodynamic, elastohydrodynamic, or boundary. This monitoring can aid identification of fault development. Feed-forward and recurrent Elman neural network algorithms were used to develop ANN models, which are subjected to training, testing, and validation process. The Levenberg-Marquardt back-propagation algorithm was applied to reduce errors. Log-sigmoid and Purelin were identified as suitable transfer functions for hidden and output nodes. The methods used in this paper shows accurate predictions from ANN and the feed-forward network performance is superior to the Elman neural network.

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H., Adheed, and Sulaiman Ahmed. "Elman Recurrent Neural Network Application in Adaptive Beamforming of Smart Antenna System." International Journal of Computer Applications 129, no. 11 (November 17, 2015): 38–43. http://dx.doi.org/10.5120/ijca2015907041.

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Tian, Ye, Yue-Ping Xu, Zongliang Yang, Guoqing Wang, and Qian Zhu. "Integration of a Parsimonious Hydrological Model with Recurrent Neural Networks for Improved Streamflow Forecasting." Water 10, no. 11 (November 14, 2018): 1655. http://dx.doi.org/10.3390/w10111655.

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This study applied a GR4J model in the Xiangjiang and Qujiang River basins for rainfall-runoff simulation. Four recurrent neural networks (RNNs)—the Elman recurrent neural network (ERNN), echo state network (ESN), nonlinear autoregressive exogenous inputs neural network (NARX), and long short-term memory (LSTM) network—were applied in predicting discharges. The performances of models were compared and assessed, and the best two RNNs were selected and integrated with the lumped hydrological model GR4J to forecast the discharges; meanwhile, uncertainties of the simulated discharges were estimated. The generalized likelihood uncertainty estimation method was applied to quantify the uncertainties. The results show that the LSTM and NARX better captured the time-series dynamics than the other RNNs. The hybrid models improved the prediction of high, median, and low flows, particularly in reducing the bias of underestimation of high flows in the Xiangjiang River basin. The hybrid models reduced the uncertainty intervals by more than 50% for median and low flows, and increased the cover ratios for observations. The integration of a hydrological model with a recurrent neural network considering long-term dependencies is recommended in discharge forecasting.

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Федоров, Євген Євгенович, Марина Володимирівна Чичужко та Владислав Олегович Чичужко. "ПІДХОДИ ДО СТВОРЕННЯ ПРОГРАМНОГО АГЕНТА НА ОСНОВІ МЕТАЕВРИСТИК І ШТУЧНИХ НЕЙРОННИХ МЕРЕЖ". RADIOELECTRONIC AND COMPUTER SYSTEMS, № 1 (23 березня 2019): 58–65. http://dx.doi.org/10.32620/reks.2019.1.06.

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In this article, has been developed a software agent based on meta-heuristics and artificial neural networks. The analysis of existing classes of agents and the selected reactive agent with internal state, which is well suited for partially observable, dynamic and non-episodic media, was carried out, and this agent has an internal state that preserves the state of the environment, obtained on the basis of the history of acts of perception, in the form of structured data. Were proposed approaches to create an agent based on meta-heuristics and an agent based on an artificial neural network. The development of reactive agents with internal state, based on the PSO (particle swarm optimization) metaheuristics, which are related to individual particles and to a whole swarm and interact by messages was proposed. Also, has been proposed an approach to the creation of a reactive agent with an internal state based on the Elman recurrent neural network. The agent-based approach allows combining different areas of artificial intelligence, digital signal processing, mathematical modeling, and game theory. The proposed agents were implemented using the JADE (Java Agent Development Framework) toolkit, which is one of the most popular tools for the creation of agent systems. A numerical study was made to determine the parameters of the swarm PSO metaheuristics and the Elman recurrent neural network. As a purpose function, the Rastrigin test function has been used. The number of visits to the website of DonNTU was used as an input sample for the Elman network. The minimum average square error forecast was the criterion for choosing the structure of a network model. 10 hiding neurons were used to predict the number of visits to the website page, since, with increasing of hidden neurons number, the change in the error value is small. To determine the number of particles in the swarm, a series of experiments was conducted, the results of which are presented by graphs. The proposed approaches can be used in intelligent computer systems.

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Sultana, Zakia, Md Ashikur Rahman Khan, and Nusrat Jahan. "Early Breast Cancer Detection Utilizing Artificial Neural Network." WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 18 (March 18, 2021): 32–42. http://dx.doi.org/10.37394/23208.2021.18.4.

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Breast cancer is one of the most dangerous cancer diseases for women in worldwide. A Computeraided diagnosis system is very helpful for radiologist for diagnosing micro calcification patterns earlier and faster than typical screening techniques. Maximum breast cancer cells are eventually form a lump or mass called a tumor. Moreover, some tumors are cancerous and some are not cancerous. The cancerous tumors are called malignant and non-cancerous tumors are called benign. The benign tumors are not dangerous to health. But the unchecked malignant tumors have the ability to spread in other organs of the body. For that early detection of benign and malignant tumor is important for confining the death of breast cancer. In these research study different neural networks such as, Multilayer Perceptron (MLP) Neural Network, Jordan/Elman Neural Network, Modular Neural Network (MNN), Generalized Feed-Forward Neural Network (GFFNN), Self-Organizing Feature Map (SOFM) Neural Network, Support Vector Machine (SVM) Neural Network, Probabilistic Neural Network (PNN) and Recurrent Neural Network (RNN) are used for classifying breast cancer tumor. And compare the results of these networks to find the best neural network for detecting breast cancer. The networks are tested on Wisconsin breast cancer (WBC) database. Finally, the comparing result showed that Probabilistic Neural Network shows the best detection result than other networks.

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Hu, John Wei-Shan, Yi-Chung Hu, and Ricky Ray-Wen Lin. "Applying Neural Networks to Prices Prediction of Crude Oil Futures." Mathematical Problems in Engineering 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/959040.

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The global economy experienced turbulent uneasiness for the past five years owing to large increases in oil prices and terrorist’s attacks. While accurate prediction of oil price is important but extremely difficult, this study attempts to accurately forecast prices of crude oil futures by adopting three popular neural networks methods including the multilayer perceptron, the Elman recurrent neural network (ERNN), and recurrent fuzzy neural network (RFNN). Experimental results indicate that the use of neural networks to forecast the crude oil futures prices is appropriate and consistent learning is achieved by employing different training times. Our results further demonstrate that, in most situations, learning performance can be improved by increasing the training time. Moreover, the RFNN has the best predictive power and the MLP has the worst one among the three underlying neural networks. This finding shows that, under ERNNs and RFNNs, the predictive power improves when increasing the training time. The exceptional case involved BPNs, suggesting that the predictive power improves when reducing the training time. To sum up, we conclude that the RFNN outperformed the other two neural networks in forecasting crude oil futures prices.

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Aribowo, Widi. "Slime Mould Algorithm Training Neural Network in Automatic Voltage Regulator." Trends in Sciences 19, no. 3 (January 20, 2022): 2145. http://dx.doi.org/10.48048/tis.2022.2145.

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The research is proposed a new method of artificial intelligence (AI) to control automatic voltage regulators. A neural network has improved using a metaheuristic method, namely the slime mould algorithm (SMA). SMA has an algorithm based on the mode of slime mold in nature. SMA has characteristics that use adaptive weights to simulate the process to generate feedback from the movement of bio-oscillator-based slime molds in foraging, exploring, and exploiting areas. The performance of the proposed method is focused on speed and rotor angle. To know the competence and potency of the proposed method, a comparison with feed-forward backpropagation neural networks (FFBNN), cascade-forward backpropagation neural networks (CFBNN), Elman-recurrent neural networks (E-RNN), focused time delay neural network (FTDNN), and Distributed Time Delay Neural Network (DTDNN) method are applied. It can be concluded that the proposed method has the best ability. The Proposed method has ability to reduce the overshoot speed with an average value of 0.78 % and the overshoot rotor angle with an average value of 2.134 %.

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Azizian, Davood, and Mehdi Bigdeli. "A new cast-resin transformer thermal model based on recurrent neural networks." Archives of Electrical Engineering 66, no. 1 (March 1, 2017): 17–28. http://dx.doi.org/10.1515/aee-2017-0002.

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Abstract Thermal modeling in the transient condition is very important for cast-resin dry-type transformers. In the present research, two novel dynamic thermal models have been introduced for the cast-resin dry-type transformer. These models are based on two artificial neural networks: the Elman recurrent networks (ELRN) and the nonlinear autoregressive model process with exogenous input (NARX). Using the experimental data, the introduced neural network thermal models have been trained. By selecting a typical transformer, the trained thermal models are validated using additional experimental results and the traditional thermal models. It is shown that the introduced neural network based thermal models have a good performance in temperature prediction of the winding and the cooling air in the cast-resin dry-type transformer. The introduced thermal models are more accurate for the temperature analysis of this transformer and they will be trained easily. Finally, the trained and validated thermal models are employed to evaluate the life-time and the reliability of a typical cast-resin dry-type transformer.

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Gorianto, Frisca Olivia, and I. Gede Santi Astawa. "Breast Cancer Classification Using Artificial Neural Network and Feature Selection." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 8, no. 2 (December 1, 2019): 113. http://dx.doi.org/10.24843/jlk.2019.v08.i02.p01.

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Breast cancer is still one of the leading causes of death in the world. Prevention can be done if the cancer can be recognized early on whether the cancer is malignant or benign. In this study, a comparison of malignant and benign cancer classifications was performed using two artificial neural network methods, which are the Feed-Forward Backpropagation method and the Elman Recurrent Neural Network method, before and after the feature selection of the data. The result of the study produced that Feed-Forward Backpropagation method using 2 hidden layers is better after the feature selection was performed on the data with an accuracy value of 99,26%.

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Achanta, Sivanand, and Suryakanth V. Gangashetty. "Deep Elman recurrent neural networks for statistical parametric speech synthesis." Speech Communication 93 (October 2017): 31–42. http://dx.doi.org/10.1016/j.specom.2017.08.003.

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Aribowo, Widi, Bambang Suprianto, I. Gusti Putu Asto Buditjahjanto, Mahendra Widyartono, and Miftahur Rohman. "An Improved Neural Network Based on Parasitism – Predation Algorithm for an Automatic Voltage Regulator." ECTI Transactions on Electrical Engineering, Electronics, and Communications 19, no. 2 (June 30, 2021): 136–44. http://dx.doi.org/10.37936/ecti-eec.2021192.241628.

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The parasitism – predation algorithm (PPA) is an optimization method that duplicates the interaction of mutualism between predators (cats), parasites (cuckoos), and hosts (crows). The study employs a combination of the PPA methods using the cascade-forward backpropagation neural network. This hybrid method employs an automatic voltage regulator (AVR) on a single machine system, with the performance measurement focusing on speed and the rotor angle. The performance of the proposed method is compared with the feed-forward backpropagation neural network (FFBNN), cascade-forward backpropagation neural network (CFBNN), Elman recurrent neural network (E-RNN), focused time-delay neural network (FTDNN), and distributed time-delay neural network (DTDNN). The results show that the proposed method exhibits the best speed and rotor angle performance. The PPA-CFBNN method has the ability to reduce the overshoot of the speed by 1.569% and the rotor angle by 0.724%.

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Stepchenko, Arthur, and Jurij Chizhov. "NDVI Short-Term Forecasting Using Recurrent Neural Networks." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 3 (June 16, 2015): 180. http://dx.doi.org/10.17770/etr2015vol3.167.

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In this paper predictions of the Normalized Difference Vegetation Index (NDVI) data recorded by satellites over Ventspils Municipality in Courland, Latvia are discussed. NDVI is an important variable for vegetation forecasting and management of various problems, such as climate change monitoring, energy usage monitoring, managing the consumption of natural resources, agricultural productivity monitoring, drought monitoring and forest fire detection. Artificial Neural Networks (ANN) are computational models and universal approximators, which are widely used for nonlinear, non-stationary and dynamical process modeling and forecasting. In this paper Elman Recurrent Neural Networks (ERNN) are used to make one-step-ahead prediction of univariate NDVI time series.

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Bettiza, Martaleli. "An Analysis on Wind Speed Forecasting Result with the Elman Recurrent Neural Network Method." E3S Web of Conferences 324 (2021): 05002. http://dx.doi.org/10.1051/e3sconf/202132405002.

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Weather factors in the archipelago have an important role in sea transportation. Weather factors, especially wind speed and wave height, become the determinants of sailing permits besides transportation’s availability, routes, and fuel. Wind speed is also a potential source of renewable energy in the archipelago. Accurate wind speed forecasting is very useful for marine transportation and development of wind power technology. One of the methods in the artificial neural network field, Elman Recurrent Neural Network (ERNN), is used in this study to forecast wind speed. Wind speed data in 2019 from measurements at the Badan Meteorolog Klimatologi dan Geofisika (BMKG) at Hang Nadim Batam station were used in the training and testing process. The forecasting results showed an accuracy rate of 88.28% on training data and 71.38% on test data. The wide data range with the randomness and uncertainty of wind speed is the cause of low accuracy. The data set is divided into the training set and the testing set in several ratio schemas. The division of this data set considered to have contributed to the MAPE value. The observation data and data division carried out in different seasons, with varying types of wind cycles. Therefore, the forecasting results obtained in the training process are 17% better than the testing data.

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Lin, Chih-Min, and Enkh-Amgalan Boldbaatar. "Fault Accommodation Control for a Biped Robot Using a Recurrent Wavelet Elman Neural Network." IEEE Systems Journal 11, no. 4 (December 2017): 2882–93. http://dx.doi.org/10.1109/jsyst.2015.2409888.

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Suhartono, Suhartono, and A. J. Endharta. "PERAMALAN KONSUMSI LISTRIK JANGKA PENDEK DENGAN ARIMA MUSIMAN GANDA DAN ELMAN-RECURRENT NEURAL NETWORK." JUTI: Jurnal Ilmiah Teknologi Informasi 7, no. 4 (July 1, 2009): 183. http://dx.doi.org/10.12962/j24068535.v7i4.a88.

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Kusnanti, Eka Alifia, Dian C. Rini Novitasari, Fajar Setiawan, Aris Fanani, Mohammad Hafiyusholeh, and Ghaluh Indah Permata Sari. "Predicting Velocity and Direction of Ocean Surface Currents using Elman Recurrent Neural Network Method." Journal of Information Systems Engineering and Business Intelligence 8, no. 1 (April 26, 2022): 21–30. http://dx.doi.org/10.20473/jisebi.8.1.21-30.

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Background: Ocean surface currents need to be monitored to minimize accidents at ship crossings. One way to predict ocean currents—and estimate the danger level of the sea—is by finding out the currents’ velocity and their future direction. Objective: This study aims to predict the velocity and direction of ocean surface currents. Methods: This research uses the Elman recurrent neural network (ERNN). This study used 3,750 long-term data and 72 short-term data. Results: The evaluation with Mean Absolute Percentage Error (MAPE) achieved the best results in short-term predictions. The best MAPE of the U currents (east to west) was 14.0279% with five inputs; the first and second hidden layers were 50 and 100, and the learning rate was 0.3. While the best MAPE of the V currents (north to south) was 3.1253% with five inputs, the first and second hidden layers were 20 and 50, and the learning rate was 0.1. The ocean surface currents’ prediction indicates that the current state is from east to south with a magnitude of around 169,5773°-175,7127° resulting in a MAPE of 0.0668%. Conclusion: ERNN is more effective than single exponential smoothing and RBFNN in ocean current prediction studies because it produces a smaller error value. In addition, the ERNN method is good for short-term ocean surface currents but is not optimal for long-term current predictions. Keywords: MAPE, ERNN, ocean currents, ocean currents’ velocity, ocean currents’ directions

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Awang, Norhashidah, Ng Kar Yong, and Soo Yin Hoeng. "Forecasting ozone concentration levels using Box-Jenkins ARIMA modelling and artificial neural networks: A comparative study." MATEMATIKA 33, no. 2 (December 27, 2017): 119. http://dx.doi.org/10.11113/matematika.v33.n2.900.

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An accurate forecasting of tropospheric ozone (O3) concentration is beneficial for strategic planning of air quality. In this study, various forecasting techniques are used to forecast the daily maximum O3 concentration levels at a monitoring station in the Klang Valley, Malaysia. The Box-Jenkins autoregressive integrated moving-average (ARIMA) approach and three types of neural network models, namely, back-propagation neural network, Elman recurrent neural network and radial basis function neural network are considered. The daily maximum data, spanning from 1 January 2011 to 7 August 2011, was obtained from the Department of Environment, Malaysia. The performance of the four methods in forecasting future values of ozone concentrations is evaluated based on three criteria, which are root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The findings show that the Box-Jenkins approach outperformed the artificial neural network methods.

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Szkoła, Jarosław, Krzysztof Pancerz, and Jan Warchoł. "Recurrent Neural Networks in Computer-Based Clinical Decision Support for Laryngopathies: An Experimental Study." Computational Intelligence and Neuroscience 2011 (2011): 1–8. http://dx.doi.org/10.1155/2011/289398.

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The main goal of this paper is to give the basis for creating a computer-based clinical decision support (CDS) system for laryngopathies. One of approaches which can be used in the proposed CDS is based on the speech signal analysis using recurrent neural networks (RNNs). RNNs can be used for pattern recognition in time series data due to their ability of memorizing some information from the past. The Elman networks (ENs) are a classical representative of RNNs. To improve learning ability of ENs, we may modify and combine them with another kind of RNNs, namely, with the Jordan networks. The modified Elman-Jordan networks (EJNs) manifest a faster and more exact achievement of the target pattern. Validation experiments were carried out on speech signals of patients from the control group and with two kinds of laryngopathies.

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Boldbaatar, Enkh-Amgalan, Lo-Yi Lin, and Chih-Min Lin. "Breast Tumor Classification Using Fast Convergence Recurrent Wavelet Elman Neural Networks." Neural Processing Letters 50, no. 3 (January 25, 2019): 2037–52. http://dx.doi.org/10.1007/s11063-018-9931-4.

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