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Open Access Article
1 - An efficient one-layer recurrent neural network for solving a class of nonsmooth optimization problems
Mohammad Javad Ebadi Alireza Hosseini Hossein JafariConstrained optimization problems have a wide range of applications in science, economics, and engineering. In this paper, a neural network model is proposed to solve a class of nonsmooth constrained optimization problems with a nonsmooth convex objective function subje MoreConstrained optimization problems have a wide range of applications in science, economics, and engineering. In this paper, a neural network model is proposed to solve a class of nonsmooth constrained optimization problems with a nonsmooth convex objective function subject to nonlinear inequality and affine equality constraints. It is a one-layer non-penalty recurrent neural network based on the differential inclusion. Unlike most of the existing neural network models, there is neither a penalty parameter nor a penalty function in its structure. It has less complexity which leads to the easier implementation of the model for solving optimization problems. The equivalence of optimal solutions set of the main optimization problem and the equilibrium points set of the model is proven. Moreover, the global convergence and the stability of the introduced neural network are shown. Some examples including the L1-norm minimization problem are given and solved by the proposed model to illustrate its performance and effectiveness. Manuscript profile -
Open Access Article
2 - A Recurrent Neural Network to Identify Efficient Decision Making Units in Data Envelopment Analysis
A. Ghomashi G. R. Jahanshahloo F. Hosseinzadeh LotfiIn this paper we present a recurrent neural network model to recognize efficient Decision Making Units(DMUs) in Data Envelopment Analysis(DEA). The proposed neural network model is derived from an unconstrained minimization problem. In theoretical aspect, it is shown th MoreIn this paper we present a recurrent neural network model to recognize efficient Decision Making Units(DMUs) in Data Envelopment Analysis(DEA). The proposed neural network model is derived from an unconstrained minimization problem. In theoretical aspect, it is shown that the proposed neural network is stable in the sense of lyapunov and globally convergent. The proposed model has a single-layer structure. Simulation shows that the proposed model is effective to identify efficient DMUs in DEA. Manuscript profile -
Open Access Article
3 - Modeling of Qaleh Rudkhan river sediment rate prediction, using LSTM neural network
Mahbobeh Shadabi bejand Ebrahim AmiriBackground and Objective: Proper estimation of the amount of sediment flowing in rivers is important as a data base for many river engineering designs and processes. Qaleh Rudkhan River is one of the most important water basins in the west of Gilan province. The most im MoreBackground and Objective: Proper estimation of the amount of sediment flowing in rivers is important as a data base for many river engineering designs and processes. Qaleh Rudkhan River is one of the most important water basins in the west of Gilan province. The most important branches of the basin are two branches named Gasht Rudkhan and Ghaleh Rudkhan. The river (Qaleh Rudkhan) is made up of two branches (Heydaralat) and (Nazaralat). Therefore, the purpose of this study was to model the prediction of sediment rate in Qaleh Rudkhan River using long short-term memory neural network (LSTM). Material and Methodology: In this research, the recorded Debi-sediment statistics related to the statistical period of 1381 to 1395 has been used. These statistics include daily instantaneous Debi in cubic meter per second and daily instantaneous sediment in ton per day, which are measured simultaneously. The data used to model the artificial neural network are Debi-sediment values the accuracy of the predictions was evaluated with three error criteria. Findings: The three criteria considered are AFE, FFE and n-AFE, respectively. Discussion and Conclusion: Among these criteria, the FFE criterion showed that the correlation between the model output and the measured sediment data is appropriate. As a result, the LSTM model has the appropriate accuracy to predict the amount of sediment in the two rivers of Qala-e-Rudkhan. Manuscript profile -
Open Access Article
4 - Extended Aartificial Neural Networks Approach and Fractional Volterra Integro-Differential Equations
احمد جعفریان رحیم صانعی فرد -
Open Access Article
5 - A Recurrent Neural Network for Solving Strictly Convex Quadratic Programming Problems
A. Ghomashi M. Abbasi -
Open Access Article
6 - Designing Path for Robot Arm Extensions Series with the Aim of Avoiding Obstruction with Recurring Neural Network
ramin khodaie محمد اسمعیل اکبری -
Open Access Article
7 - Soft Error Rate Estimation of Logic Circuits Using Recurrent Neural Networks
Rasoul Farjaminezhad saeed safari Amir Masoud Eftekhari Moghadam -
Open Access Article
8 - Evaluation of the Performance of Feedforward and Recurrent Neural Networks in Active Cancellation of Sound Noise
Mehrshad Salmasi Homayoun Mahdavi-NasabActive noise control is based on the destructive interference between the primary noise and generated noise from the secondary source. An antinoise of equal amplitude and opposite phase is generated and combined with the primary noise. In this paper, performance of the MoreActive noise control is based on the destructive interference between the primary noise and generated noise from the secondary source. An antinoise of equal amplitude and opposite phase is generated and combined with the primary noise. In this paper, performance of the neural networks is evaluated in active cancellation of sound noise. For this reason, feedforward and recurrent neural networks are designed and trained. After training, performance of the feedforwrad and recurrent networks in noise attenuation are compared. We use Elman network as a recurrent neural network. For simulations, noise signals from a SPIB database are used. In order to compare the networks appropriately, equal number of layers and neurons are considered for the networks. Moreover, training and test samples are similar. Simulation results show that feedforward and recurrent neural networks present good performance in noise cancellation. As it is seen, the ability of recurrent neural network in noise attenuation is better than feedforward network. Manuscript profile -
Open Access Article
9 - یک مدل شبکه عصبی بازگشتی برای حل مدل CCR در تحلیل پوششی داده ها
معصومه عباسی عباس قماشیدر این مقاله ما یک شبکه عصبی برای حل مدل CCR در تحلیل پوششی داده ها (DEA) معرفی می کنیم. مدل شبکه عصبی پیشنهادی از یک مسئله مینیمم سازی نامقید حاصل می شود. از دیدگاه تئوری، نشان داده می شود که شبکه عصبی پیشنهادی به مفهوم لیاپانف پایدار و همگرای عمومی به جواب بهینه مدل C Moreدر این مقاله ما یک شبکه عصبی برای حل مدل CCR در تحلیل پوششی داده ها (DEA) معرفی می کنیم. مدل شبکه عصبی پیشنهادی از یک مسئله مینیمم سازی نامقید حاصل می شود. از دیدگاه تئوری، نشان داده می شود که شبکه عصبی پیشنهادی به مفهوم لیاپانف پایدار و همگرای عمومی به جواب بهینه مدل CCR می باشد. مدل پیشنهادی ساختار تک لایه دارد. با یک مثال عددی موثر بودن مدل پیشنهادی برای حل مدل CCR در DEA نشان داده می شود. Manuscript profile -
Open Access Article
10 - Neural Network Model Based on The Control for Transient Stability and Low-Frequency Oscillation in Power System Operation
Amir Bagheran Sharbaf Ali Asghar Shojaei -
Open Access Article
11 - Persian Speech Recognition Through the Combination of ANN/HMM
Ladan Khosravani pour Ali Farrokhi -
Open Access Article
12 - Novel Adaptive Recurrent Neural Controller based on VSC HVDC Damping Controller to Improve Power System Stability
Naser Taheri -
Open Access Article
13 - Comparison of different machine learning models in stock market index forecasting
maryam sohrabi Seyed Mozaffar mirbargkar Ebrahim Chirani SINA KHERADYARPredicting time series of financial markets is a challenging issue in the field of specialized studies of time series and has attracted the attention of many researchers. Due to the presence of big data, this issue has led to the growth of developments in the field of m MorePredicting time series of financial markets is a challenging issue in the field of specialized studies of time series and has attracted the attention of many researchers. Due to the presence of big data, this issue has led to the growth of developments in the field of machine learning models. Due to the importance of this issue, in this study, by using the comparison of different machine learning models such as random forest approaches, support vector machine, artificial neural network and deep learning-based recurrent neural networks to investigate the ability of different machine learning models in prediction. The total index of Tehran Stock Exchange during the period 2013 to 2020 has been discussed. The prediction results of 1, 3 and 6 day courses for the out-of-sample period show that the machine learning method based on the long short-term memory (LSTM) network, a recurrent neural networks, has a better result compared to other models. Manuscript profile -
Open Access Article
14 - The Assessment of the optimal Deep Learning Algorithm on Stock Price Prediction (Long Short-Term Memory Approach)
Amir Sharif far Maryam Khalili Araghi Iman Raeesi Vanani Mirfeiz FallahForecasting stock prices plays an important role in setting a trading strategy or determining the appropriate timing for buying or selling a stock. Deep Learning (DL) is a type of Artificial Neural Network (ANN) that consists of multiple processing layers and enables hi MoreForecasting stock prices plays an important role in setting a trading strategy or determining the appropriate timing for buying or selling a stock. Deep Learning (DL) is a type of Artificial Neural Network (ANN) that consists of multiple processing layers and enables high-level abstraction to model data. The key advantage of DL models is extracting the good features of input data automatically using a general-purpose learning procedure which is suitable for dynamic time series such as stock price.In this research the ability of Long Short-Term Memory (LSTM) to predict the stock price is studied; moreover, the factors that have significant effects on the stock price is classified and legal and natural person trading is introduced as an important factor which has influence on the stock price. Price data, technical indexes and legal and natural person trading is used as an input data for running the model. The results obtained from LSTM with Dropout layer are better and more stable than simple form of LSTM and RNN models. Manuscript profile -
Open Access Article
15 - Presenting a Model for Predicting Stock Market Trends by Detecting Fractal Patterns Based on Elliott Wave Theory Using Deep Learning Method
Masoud Nadem Yahya Kamyabi esfandiar malekianToday, artificial intelligence has made a big change in the recognition of chart patterns in technical analysis. Although, the emergence of new and complex analytical methods in technical analysis has provided a new challenge for artificial intelligence methods. One of MoreToday, artificial intelligence has made a big change in the recognition of chart patterns in technical analysis. Although, the emergence of new and complex analytical methods in technical analysis has provided a new challenge for artificial intelligence methods. One of the popular and complex technical analysis methods is Elliott Wave Theory. On the other hand, the speed of progress of artificial intelligence methods is such that a more powerful method is introduced every time. One of the new and powerful artificial intelligence methods is the deep learning method. Therefore, in this research, a model has been presented to predict the trend of the stock market through the detection of fractal patterns based on Elliott wave theory using deep learning method. In this research, 15 Elliott wave patterns were considered, and then 1002 samples of stock price charts of companies listed on Tehran Stock Exchange were collected and labeled for patterns, and finally entered as input into deep learning algorithm using recurrent neural network model for recognition. In this research, RapidMiner 9.9 software was used and accuracy criteria were used to determine the power of the model. Based on the results, the accuracy of developed model in recognizing patterns is 61%. Manuscript profile -
Open Access Article
16 - A Method for Solving Nonsmooth Pseudoconvex Optimization
Maryam Bala Seyed Ghasir Aghileh Heydari Mohammad Ali Badamchizadeh
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