Machine translation of English speech: Comparison of multiple algorithms

Abstract In order to improve the efficiency of the English translation, machine translation is gradually and widely used. This study briefly introduces the neural network algorithm for speech recognition. Long short-term memory (LSTM), instead of traditional recurrent neural network (RNN), was used as the encoding algorithm for the encoder, and RNN as the decoding algorithm for the decoder. Then, simulation experiments were carried out on the machine translation algorithm, and it was compared with two other machine translation algorithms. The results showed that the back-propagation (BP) neural network had a lower word error rate and spent less recognition time than artificial recognition in recognizing the speech; the LSTM–RNN algorithm had a lower word error rate than BP–RNN and RNN–RNN algorithms in recognizing the test samples. In the actual speech translation test, as the length of speech increased, the LSTM–RNN algorithm had the least changes in the translation score and word error rate, and it had the highest translation score and the lowest word error rate under the same speech length.

Download Full-text

Neural Networks for Fingerprint Recognition

Neural Computation ◽

10.1162/neco.1993.5.3.402 ◽

1993 ◽

Vol 5 (3) ◽

pp. 402-418 ◽

Cited By ~ 70

Author(s):

Pierre Baldi ◽

Yves Chauvin

Keyword(s):

Neural Network ◽

Neural Networks ◽

Data Base ◽

Error Rate ◽

Fingerprint Recognition ◽

Network Algorithm ◽

The Neural Network ◽

Neural Network Algorithm

After collecting a data base of fingerprint images, we design a neural network algorithm for fingerprint recognition. When presented with a pair of fingerprint images, the algorithm outputs an estimate of the probability that the two images originate from the same finger. In one experiment, the neural network is trained using a few hundred pairs of images and its performance is subsequently tested using several thousand pairs of images originated from a subset of the database corresponding to 20 individuals. The error rate currently achieved is less than 0.5%. Additional results, extensions, and possible applications are also briefly discussed.

Download Full-text

Analysis of DES Plaintext Recovery Based on BP Neural Network

Security and Communication Networks ◽

10.1155/2019/9580862 ◽

2019 ◽

Vol 2019 ◽

pp. 1-5

Author(s):

Sijie Fan ◽

Yaqun Zhao

Keyword(s):

Neural Network ◽

Error Rate ◽

Bp Neural Network ◽

Layer By Layer ◽

Network Simulator ◽

Analysis Model ◽

Backpropagation Neural Network ◽

Network Algorithms ◽

The Neural Network ◽

Neural Network Algorithm

Backpropagation neural network algorithms are one of the most widely used algorithms in the current neural network algorithm. It uses the output error rate to estimate the error rate of the direct front layer of the output layer, so that we can get the error rate of each layer through the layer-by-layer backpropagation. The purpose of this paper is to simulate the decryption process of DES with backpropagation algorithm. By inputting a large number of plaintext and ciphertext pairs, a neural network simulator for the decryption of the target cipher is constructed, and the ciphertext given is decrypted. In this paper, how to modify the backpropagation neural network classifier and apply it to the process of building the regression analysis model is introduced in detail. The experimental results show that the final result of restoring plaintext of the neural network model built in this paper is ideal, and the fitting rate is higher than 90% compared with the true plaintext.

Download Full-text

A neural network algorithm for cloud fraction estimation using NASA-Aura OMI VIS radiance measurements

Atmospheric Measurement Techniques ◽

10.5194/amt-6-2301-2013 ◽

2013 ◽

Vol 6 (9) ◽

pp. 2301-2309 ◽

Cited By ~ 6

Author(s):

G. Saponaro ◽

P. Kolmonen ◽

J. Karhunen ◽

J. Tamminen ◽

G. de Leeuw

Keyword(s):

Neural Network ◽

Back Propagation ◽

Cloud Fraction ◽

Electromagnetic Spectrum ◽

Data Sets ◽

Ozone Monitoring Instrument ◽

Imaging Spectrometer ◽

The Neural Network ◽

Neural Network Algorithm ◽

Learning Machine

Abstract. The discrimination of cloudy from cloud-free pixels is required in almost any estimate of a parameter retrieved from satellite data in the ultraviolet (UV), visible (VIS) or infrared (IR) parts of the electromagnetic spectrum. In this paper we report on the development of a neural network (NN) algorithm to estimate cloud fractions using radiances measured at the top of the atmosphere with the NASA-Aura Ozone Monitoring Instrument (OMI). We present and discuss the results obtained from the application of two different types of neural networks, i.e., extreme learning machine (ELM) and back propagation (BP). The NNs were trained with an OMI data sets existing of six orbits, tested with three other orbits and validated with another two orbits. The results were evaluated by comparison with cloud fractions available from the MODerate Resolution Imaging Spectrometer (MODIS) flying on Aqua in the same constellation as OMI, i.e., with minimal time difference between the OMI and MODIS observations. The results from the ELM and BP NNs are compared. They both deliver cloud fraction estimates in a fast and automated way, and they both performs generally well in the validation. However, over highly reflective surfaces, such as desert, or in the presence of dust layers in the atmosphere, the cloud fractions are not well predicted by the neural network. Over ocean the two NNs work equally well, but over land ELM performs better.

Download Full-text

Advertising Click-Through Rate Prediction Based on CNN-LSTM Neural Network

Computational Intelligence and Neuroscience ◽

10.1155/2021/3484104 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Danqing Zhu

Keyword(s):

Neural Network ◽

Prediction Model ◽

Short Term Memory ◽

Feature Learning ◽

Rate Prediction ◽

Network Algorithm ◽

The Neural Network ◽

Single Structure ◽

Neural Network Algorithm ◽

Click Through Rate

In the era of big data information, how to effectively predict and analyze the click-through rate of information advertising is the key for enterprises in various fields to seek returns. The point rate prediction of advertising is one of the core contents of advertising calculation. The traditional shallow prediction model cannot meet the nonlinear relationship of data processing, and the manual processing of data information extraction method is very resource consuming. To solve the above problems, this paper proposes a CNN-LSTM (convolutional neural network-long short-term memory) convolution hybrid neural network algorithm to predict the click-through rate of advertisements. According to the neural network algorithm, the prediction model is constructed, and the effective features are extracted in the process of model establishment, and the prediction analysis is carried out according to the simplified LSTM neural network time serialization features. CNN convolution neural network is used to train the prediction model. This paper analyzes the characteristics of traditional prediction methods and the corresponding solutions and carries out feature learning and prediction model construction for advertising click-through rate prediction. Then, the unknown behavior of advertising users is judged and predicted. The results show that, compared with the single structure network of traditional prediction model, the prediction effect based on CNN-LSTM neural network algorithm has higher accuracy.

Download Full-text

Hysteresis Modeling and Compensation of Fast Steering Mirrors with Hysteresis Operator Based Back Propagation Neural Networks

Micromachines ◽

10.3390/mi12070732 ◽

2021 ◽

Vol 12 (7) ◽

pp. 732

Author(s):

Kairui Cao ◽

Guanglu Hao ◽

Qingfeng Liu ◽

Liying Tan ◽

Jing Ma

Keyword(s):

Neural Network ◽

Back Propagation ◽

Hysteresis Model ◽

Cooperative Movement ◽

Hysteresis Operator ◽

The Neural Network ◽

Hysteresis Nonlinearity ◽

Hysteresis Modeling ◽

Hysteresis Characteristics ◽

Inverse Hysteresis

Fast steering mirrors (FSMs), driven by piezoelectric ceramics, are usually used as actuators for high-precision beam control. A FSM generally contains four ceramics that are distributed in a crisscross pattern. The cooperative movement of the two ceramics along one radial direction generates the deflection of the FSM in the same orientation. Unlike the hysteresis nonlinearity of a single piezoelectric ceramic, which is symmetric or asymmetric, the FSM exhibits complex hysteresis characteristics. In this paper, a systematic way of modeling the hysteresis nonlinearity of FSMs is proposed using a Madelung’s rules based symmetric hysteresis operator with a cascaded neural network. The hysteresis operator provides a basic hysteresis motion for the FSM. The neural network modifies the basic hysteresis motion to accurately describe the hysteresis nonlinearity of FSMs. The wiping-out and congruency properties of the proposed method are also analyzed. Moreover, the inverse hysteresis model is constructed to reduce the hysteresis nonlinearity of FSMs. The effectiveness of the presented model is validated by experimental results.

Download Full-text

Rethinking the Random Cropping Data Augmentation Method Used in the Training of CNN-Based SAR Image Ship Detector

Remote Sensing ◽

10.3390/rs13010034 ◽

2020 ◽

Vol 13 (1) ◽

pp. 34

Author(s):

Rong Yang ◽

Robert Wang ◽

Yunkai Deng ◽

Xiaoxue Jia ◽

Heng Zhang

Keyword(s):

Neural Network ◽

Data Augmentation ◽

Back Propagation ◽

Detection Performance ◽

Training Data ◽

Sar Image ◽

Optical Images ◽

The Neural Network ◽

Effective Training ◽

Standard Configuration

The random cropping data augmentation method is widely used to train convolutional neural network (CNN)-based target detectors to detect targets in optical images (e.g., COCO datasets). It can expand the scale of the dataset dozens of times while consuming only a small amount of calculations when training the neural network detector. In addition, random cropping can also greatly enhance the spatial robustness of the model, because it can make the same target appear in different positions of the sample image. Nowadays, random cropping and random flipping have become the standard configuration for those tasks with limited training data, which makes it natural to introduce them into the training of CNN-based synthetic aperture radar (SAR) image ship detectors. However, in this paper, we show that the introduction of traditional random cropping methods directly in the training of the CNN-based SAR image ship detector may generate a lot of noise in the gradient during back propagation, which hurts the detection performance. In order to eliminate the noise in the training gradient, a simple and effective training method based on feature map mask is proposed. Experiments prove that the proposed method can effectively eliminate the gradient noise introduced by random cropping and significantly improve the detection performance under a variety of evaluation indicators without increasing inference cost.

Download Full-text

Safety Warning Model of Coal Face Based on FCM Fuzzy Clustering and GA-BP Neural Network

Symmetry ◽

10.3390/sym13061082 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1082

Author(s):

Fanqiang Meng

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Early Warning ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Coal Face ◽

Early Warning Model ◽

Working Face ◽

The Neural Network ◽

Warning Model

Risk and security are two symmetric descriptions of the uncertainty of the same system. If the risk early warning is carried out in time, the security capability of the system can be improved. A safety early warning model based on fuzzy c-means clustering (FCM) and back-propagation neural network was established, and a genetic algorithm was introduced to optimize the connection weight and other properties of the neural network, so as to construct the safety early warning system of coal mining face. The system was applied in a coal face in Shandong, China, with 46 groups of data as samples. Firstly, the original data were clustered by FCM, the input space was fuzzy divided, and the samples were clustered into three categories. Then, the clustered data was used as the input of the neural network for training and prediction. The back-propagation neural network and genetic algorithm optimization neural network were trained and verified many times. The results show that the early warning model can realize the prediction and early warning of the safety condition of the working face, and the performance of the neural network model optimized by genetic algorithm is better than the traditional back-propagation artificial neural network model, with higher prediction accuracy and convergence speed. The established early warning model and method can provide reference and basis for the prediction, early warning and risk management of coal mine production safety, so as to discover the hidden danger of working face accident as soon as possible, eliminate the hidden danger in time and reduce the accident probability to the maximum extent.

Download Full-text

Application of ANN Back-Propagation for Fracture Design Parameters in Extra-Low Cycle Rotating Bending Fatigue

Materials Science Forum ◽

10.4028/www.scientific.net/msf.610-613.450 ◽

2009 ◽

Vol 610-613 ◽

pp. 450-453

Author(s):

Hong Yan Duan ◽

You Tang Li ◽

Jin Zhang ◽

Gui Ping He

Keyword(s):

Neural Network ◽

Neural Network Model ◽

Back Propagation ◽

Training Data ◽

Design Parameters ◽

Ann Model ◽

Bending Fatigue ◽

The Neural Network ◽

Rotating Bending Fatigue ◽

Rotating Bending

The fracture problems of ecomaterial (aluminum alloyed cast iron) under extra-low cycle rotating bending fatigue loading were studied using artificial neural networks (ANN) in this paper. The training data were used in the formation of training set of ANN. The ANN model exhibited excellent in results comparison with the experimental results. It was concluded that predicted fracture design parameters by the trained neural network model seem more reasonable compared to approximate methods. It is possible to claim that, ANN is fairly promising prediction technique if properly used. Training ANN model was introduced at first. And then the Training data for the development of the neural network model was obtained from the experiments. The input parameters, notch depth, the presetting deflection and tip radius of the notch, and the output parameters, the cycle times of fracture were used during the network training. The neural network architecture is designed. The ANN model was developed using back propagation architecture with three layers jump connections, where every layer was connected or linked to every previous layer. The number of hidden neurons was determined according to special formula. The performance of system is summarized at last. In order to facilitate the comparisons of predicted values, the error evaluation and mean relative error are obtained. The result show that the training model has good performance, and the experimental data and predicted data from ANN are in good coherence.

Download Full-text

Stock Market Prediction Using Artificial Neural Networks

Advanced Engineering Forum ◽

10.4028/www.scientific.net/aef.6-7.1055 ◽

2012 ◽

Vol 6-7 ◽

pp. 1055-1060 ◽

Cited By ~ 4

Author(s):

Yang Bing ◽

Jian Kun Hao ◽

Si Chang Zhang

Keyword(s):

Neural Network ◽

Prediction Models ◽

Learning Algorithm ◽

Stock Exchange ◽

Back Propagation ◽

Composite Index ◽

Network Models ◽

Back Propagation Neural Network ◽

Neural Network Models ◽

The Neural Network

In this study we apply back propagation Neural Network models to predict the daily Shanghai Stock Exchange Composite Index. The learning algorithm and gradient search technique are constructed in the models. We evaluate the prediction models and conclude that the Shanghai Stock Exchange Composite Index is predictable in the short term. Empirical study shows that the Neural Network models is successfully applied to predict the daily highest, lowest, and closing value of the Shanghai Stock Exchange Composite Index, but it can not predict the return rate of the Shanghai Stock Exchange Composite Index in short terms.

Download Full-text

Estimation of Flow Rate Through Analysis of Pipe Vibration

Acta Mechanica et Automatica ◽

10.2478/ama-2018-0045 ◽

2018 ◽

Vol 12 (4) ◽

pp. 294-300 ◽

Cited By ~ 1

Author(s):

Santhosh K. Venkata ◽

Bhagya R. Navada

Keyword(s):

Neural Network ◽

Fluid Flow ◽

Flow Rate ◽

Back Propagation ◽

Vibration Signal ◽

Vibration Sensor ◽

Percentage Error ◽

Fluid Flow Rate ◽

Bee Colony ◽

Neural Network Algorithm

Abstract In this paper, implementation of soft sensing technique for measurement of fluid flow rate is reported. The objective of the paper is to design an estimator to physically measure the flow in pipe by analysing the vibration on the walls of the pipe. Commonly used head type flow meter causes obstruction to the flow and measurement would depend on the placement of these sensors. In the proposed technique vibration sensor is bonded on the pipe of liquid flow. It is observed that vibration in the pipe varies with the control action of stem. Single axis accelerometer is used to acquire vibration signal from pipe, signal is passed from the sensor to the system for processing. Basic techniques like filtering, amplification, and Fourier transform are used to process the signal. The obtained transform is trained using neural network algorithm to estimate the fluid flow rate. Artificial neural network is designed using back propagation with artificial bee colony algorithm. Designed estimator after being incorporated in practical setup is subjected to test and the result obtained shows successful estimation of flow rate with the root mean square percentage error of 0.667.

Download Full-text