A VPRNN Model with Fixed-Time Convergence for Time-Varying Nonlinear Equation

Following the idea of using first-order time derivatives, this paper presents a general recurrent neural network (RNN) model for online inversion of time-varying matrices. Different kinds of activation functions are investigated to guarantee the global exponential convergence of the neural model to the exact inverse of a given time-varying matrix. The robustness of the proposed neural model is also studied with respect to different activation functions and various implementation errors. Simulation results, including the application to kinematic control of redundant manipulators, substantiate the theoretical analysis and demonstrate the efficacy of the neural model on time-varying matrix inversion, especially when using a power-sigmoid activation function.

A recurrent neural network (RNN) model is developed for simulating and predicting shear behavior of both a fine-grained residual soil and a dune sand. The RNN model with one hidden layer of 20 nodes appears very effective in modeling complex soil behavior, due to its feedback connections from a hidden layer to an input layer. A dynamic gradient descent learning algorithm is used to train the network. By training part of the experimental data, which include strain-controlled undrained tests and stress-controlled drained tests performed on a residual Hawaiian volcanic soil, the network is able to capture significant variability of shear behavior existing in the residual soil. The unusual characteristics that the denser soil samples dilate under a higher stress level and the looser soil samples contract under a lower stress level are well represented by the RNN model. The RNN model also shows encouraging results in simulation and prediction of behavior of a dune sand which experienced loading-unloading-reloading conditions. Excellent agreements between the measured data and the modeling results are observed in both stress-strain behavior and volumetric-change characteristics. As compared with a traditional model, the RNN model shows more effectiveness and less effort.Key words: neural network, modeling, soil behavior, shear tests, simulation, prediction.

Artificial intelligence is a promising futuristic concept in the field of science and technology, and is widely used in new industries. The deep-learning technology leads to performance enhancement and generalization of artificial intelligence technology. The global leader in the field of information technology has declared its intention to utilize the deep-learning technology to solve environmental problems such as climate change, but few environmental applications have so far been developed. This study uses deep-learning technologies in the environmental field to predict the status of pro-environmental consumption. We predicted the pro-environmental consumption index based on Google search query data, using a recurrent neural network (RNN) model. To verify the accuracy of the index, we compared the prediction accuracy of the RNN model with that of the ordinary least square and artificial neural network models. The RNN model predicts the pro-environmental consumption index better than any other model. We expect the RNN model to perform still better in a big data environment because the deep-learning technologies would be increasingly sophisticated as the volume of data grows. Moreover, the framework of this study could be useful in environmental forecasting to prevent damage caused by climate change.

Network security field had gained research community attention in the last decade due to its growing importance. This paper addresses directly one vital problem in that field is “Intrusion Detection System” (IDS). As much as many researchers tackle this problem, many challenges arise while converting this research to reliable automatic system. The biggest challenge is to make the system works with low false alarm with new unseen threats. In this paper, we address this challenge by building a descriptive model using different models of deep Recurrent Neural Network (RNNs). (RNN) models has the ability to generalize the knowledge that can be used to identify seen and unseen threats. This generalization comes from RNN capabilities to define in its terms the normal behavior and the deviation accepted to be normal. Four different models of RNN were tested on a benchmark dataset, NSL-KDD, which is a standard test dataset for network intrusion. The proposed system showed superiority over other previously developed systems according to the standard measurements: accuracy, recall, precision and f-measure.

Accurate production forecasting is crucial for effective reservoir management and field development planning. Traditional methods; such as numerical simulation, material balance equations coupled with well models, and advanced decline curve analysis (DCA) require extensive data and involve tradeoffs between accuracy and complexity. Recently, deep learning techniques, specifically artificial neural network (ANN) and recurrent neural network (RNN) models have demonstrated promising performance in this domain. This study applies ANN and RNN for production forecasting and compares their effectiveness with traditional DCA models.An actual production dataset for a gas field containing 13 wells is used to compare ANN and RNN models to traditional decline curve analysis models. After thorough data cleaning and preparation, the dataset was divided into training (historical) and test data sets. The training data was used to build ANN and RNN models in addition to estimating the model parameters for traditional DCA models. Traditional DCA models that were investigated include Arps (exponential, hyperbolic, and harmonic), Duong, Power Law Exponential Decline (PLE), and Stretched Exponential Decline (SEPD). Finally, the prediction of different models was evaluated and compared using the testing dataset. The comparison also included long, medium, and short-term prediction.Based on the comparison of daily gas production rate forecast, the ANN model demonstrated notable improvements over Arps models for the majority of wells, with a mean absolute percent error (MAPE) of 3.9% compared to 9.1%. However, across all wells, the average MAPE was slightly lower at 6.3% versus 8.1%. Conversely, the RNN model exhibited varied performance compared to Arps models. It showed poorer long-term performance (13% versus 4.5%), similar performance in the medium term (4.4% versus 4.2%), and superior performance in the short term (2.7% versus 4.2%). Furthermore, among DCA models specifically developed for unconventional wells, SEPD exhibited nearly identical performance to Arps (8.3% compared to 8.1%). In contrast, Duong model demonstrated the poorest performance with MAPE of 21.9%.This study provides insights into model analysis and introduces guidelines in using artificial and recurrent neural networks for predicting production rates. The ANN and RNN models were developed through careful testing with different designs and techniques. All the models in this research were created using Python scripts and powerful open-source libraries like SciPy and TensorFlow.

The aims of this study are to create an artificial neural network (ANN) model using non-specific water quality parameters and to examine the accuracy of three different ANN architectures: General Regression Neural Network (GRNN), Backpropagation Neural Network (BPNN) and Recurrent Neural Network (RNN), for prediction of dissolved oxygen (DO) concentration in the Danube River. The neural network model has been developed using measured data collected from the Bezdan monitoring station on the Danube River. The input variables used for the ANN model are water flow, temperature, pH and electrical conductivity. The model was trained and validated using available data from 2004 to 2008 and tested using the data from 2009. The order of performance for the created architectures based on their comparison with the test data is RNN > GRNN > BPNN. The ANN results are compared with multiple linear regression (MLR) model using multiple statistical indicators. The comparison of the RNN model with the MLR model indicates that the RNN model performs much better, since all predictions of the RNN model for the test data were within the error of less than ± 10 %. In case of the MLR, only 55 % of predictions were within the error of less than ± 10 %. The developed RNN model can be used as a tool for the prediction of DO in river waters.

The redundant manipulator as a ubiquitous and essential component of robots, acts as a momentous role in mechanized production. The motion control of a redundant manipulator is an essential problem that must be handled. Consequently, for the repetitive motion control of redundant manipulators, a recurrent neural network (RNN) model for the double-index (DI) scheme is researched in this paper. The DI scheme sets the minimum kinetic energy (MKE) and minimum joint-angle offset (MJAO) as optimization indexes, for which the time-dependent weights are designed, and takes the dynamic equations of redundant manipulators and joint limit constraints into consideration as well. Then, the DI scheme is reformulated as a quadratic programming (QP) problem. Besides, the RNN model based on the iteration of related parameters is derived to solve the QP problem, through which joint data (i.e., joint angles, joint velocities, and joint accelerations) are obtained to drive the motion of the redundant manipulator. To verify the effectiveness of the DI scheme for motion control of redundant manipulator, simulation results of the DI scheme solved by the RNN model, the minimum acceleration norm (MAN) scheme and MKE scheme are compared.

The object of the study is methods based on neural networks for analyzing the tonality of a corpus of texts. To achieve the goal set in the work, it is necessary to solve the following tasks: study the theoretical material for learning deep neural networks and their features in relation to natural language processing; study the documentation of the Tensorflow library; develop models of convolutional and recurrent neural networks; to develop the implementation of linear and non-linear classification methods on bag of words and Word2Vec models; to compare the accuracy and other quality indicators of implemented neural network models with classical methods. Tensorboard is used for learning visualization. The work shows the superiority of classifiers based on deep neural networks over classical classification methods, even if the Word2Vec model is used for vector representations of words. The model of recurrent neural network with LSTM blocks has the highest accuracy for this corpus of texts.

Recurrent Neural Network (RNN) functions can be used to model highly nonlinear driver circuits. In this paper, RNN modeling technique is extended to multiple ports to model both the power supply noise and the ground noise accurately. RNN driver models can be extended to multiple ports to capture sensitive effects like Simultaneous Switching Noise (SSN) accurately when multiple driver circuits are switching. A comparison study is performed on test cases between RNN models and transistor level driver circuits for accuracy and computational speed-up is performed on few test cases. Results show that RNN driver models have huge computational speed-up over transistor level driver circuits.

Indonesia consists of various tribes with their respective regional languages, one of which is the Besemah tribe in South Sumatra Province with its language culture, namely the Besemah Language. Until now, the Besemah Language is still used by the Besemah tribe, but over time the number of Besemah Language speakers has decreased, plus most of the wider community does not know what the Besemah Language is. Machine Learning is a part of artificial intelligence that is often used to solve various problems. Machine Learning involves the use of computers and mathematical algorithms that use data to make predictions in the future. Machine translation is a tool that can convert one language to another. This study aims to collect datasets in the form of sentences and words from the Besemah Language, then create a Besemah Language translation machine to Indonesian and vice versa. In the research conducted, the approach used is Experimental Research in Machine Learning. Experimental research in machine learning for language translation is a research approach that involves designing and implementing a series of experiments to test and validate the performance of the language translation model. In this study, Neural Machine Translation (NMT) technology was applied with the Recurrent Neural Network (RNN) approach. The results of the study showed that the val_accuracy value for the Besemah-Indonesian translation was 0.8469 and for Indonesia-Besemah was 0.8492, in the translation trial conducted using the RNN model, 100 epochs, batch size 64, and validation split of 0.2.

Analysis of TEC variations and prediction of TEC by RNN during Indonesian earthquakes occurred from 2004 to 2024 and comparison with IRI-2020 model

In this paper, we propose new listwise learning-to-rank models that mitigate the shortcomings of existing ones. Existing listwise learning-to-rank models are generally derived from the classical Plackett-Luce model, which has three major limitations. (1) Its permutation probabilities overlook ties, i.e., a situation when more than one document has the same rating with respect to a query. This can lead to imprecise permutation probabilities and inefficient training because of selecting documents one by one. (2) It does not favor documents having high relevance. (3) It has a loose assumption that sampling documents at different steps is independent. To overcome the first two limitations, we model ranking as selecting documents from a candidate set based on unique rating levels in decreasing order. The number of steps in training is determined by the number of unique rating levels. We propose a new loss function and associated four models for the entire sequence of weighted classification tasks by assigning high weights to the selected documents with high ratings for optimizing Normalized Discounted Cumulative Gain (NDCG). To overcome the final limitation, we further propose a novel and efficient way of refining prediction scores by combining an adapted Vanilla Recurrent Neural Network (RNN) model with pooling given selected documents at previous steps. We encode all of the documents already selected by an RNN model. In a single step, we rank all of the documents with the same ratings using the last cell of the RNN multiple times. We have implemented our models using three settings: neural networks, neural networks with gradient boosting, and regression trees with gradient boosting. We have conducted experiments on four public datasets. The experiments demonstrate that the models notably outperform state-of-the-art learning-to-rank models.

Deep learning models, including recurrent neural network (RNN) and gated recurrent unit (GRU), were used to construct the clinical prognostic prediction models for peritoneal dialysis (PD) patients based on routine clinical data. The performance of the RNN and GRU were compared with logistic regression (LR), which is commonly used in medical researches. The possible underlining clinical implications based on the result from the GRU model were also investigated. We used the clinical data from the PD center of Peking University Third Hospital as the data source. Both the baseline data at the beginning of dialysis, and the follow-up and prognostic data of the patients were used by the RNN and GRU prediction models. The hyper-parameters were tuned based on the 10-fold cross-validation. The risk prediction performance of each model was evaluated via area under the receiver operation characteristic curve (AUROC), recall rate and F1-score on the testset. A total of 656 patients with the 261 occurrences of death were included in the experiment. The total number of all diagnostic records were 13 091. The results on the testset showed that the AUROC of the LR model, RNN model, and GRU model was 0.701 4, 0.786 0, and 0.814 7, respectively. The predictive performances of the GRU and RNN models were significantly better than that of the LR model. The performances of the GRU and RNN models assessed by recall rate and F1-score were also significantly better than that of the LR model, in which the GRU model reached the best performance. In addition, the recall rates were different among different causes of death or by different prediction time windows. The recurrent neural network model, especially the GRU model, is more effective in predicting PD patients' prognosis as compared with the LR model. This new model may be helpful for clinicians to provide timely intervention, thus improving the quality of care of PD.

Predictive control of reactor network model using machine learning for hydrogen-rich gas and biochar poly-generation by biomass waste gasification in supercritical water

Component based enterprise systems are becoming extremely complex in which the availability and usability are influenced intensively by the system's anomalies. Anomaly prediction is highly important for ensuring a system's stability, which aims at preventing anomaly from occurring through pre-failure warning. However, due to the system's complex nature and the noise from monitoring, capturing pre-failure symptoms is a challenging problem. In this paper, we present a sequential and an averaged recurrent neural networks (RNN) models for distributed systems and component based systems. Specifically, we use cycle representation to capture cyclical system behaviors, which can be used to improve prediction accuracy. The anomaly data used in the experiments is collected from RUBis, IBM System S, and the component based system of enterprise T. The experimental results show that our proposed methods can achieve high prediction accuracy with satisfying lead time. Our recurrent neural networks model also demonstrates time efficiency for monitoring large-scale systems.