Journal articles: 'Neural network model of identification'

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Bunrit, Supaporn, Thuttaphol Inkian, Nittaya Kerdprasop, and Kittisak Kerdprasop. "Text-Independent Speaker Identification Using Deep Learning Model of Convolution Neural Network." International Journal of Machine Learning and Computing 9, no. 2 (April 2019): 143–48. http://dx.doi.org/10.18178/ijmlc.2019.9.2.778.

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Yang, Judy X., Lily D. Li, and Mohammad G. Rasul. "A Conceptual Artificial Neural Network Model in Warehouse Receiving Management." International Journal of Machine Learning and Computing 11, no. 2 (March 2021): 130–36. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1025.

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The purpose of this research is to explore a suitable Artificial Neural Network (ANN) method applying to warehouse receiving management. A conceptual ANN model is proposed to perform identification and counting of components. The proposed model consists of a standard image library, an ANN system to present objects for identification from the real-time images and to count the number of objects in the image. The authors adopted four basic mechanical design shapes as the attributes of images for shape analysis and pre-defined features; the joint probability from Bayes theorem and image pixel values for object counting is applied in this research. Compared to other ANNs, the proposed conceptual model is straightforward to perform classification and counting. The model is tested by employing a mini image dataset which is industrial enterprise relevant. The initial result shows that the proposed model has achieved an accuracy rate of 80% in classification and a 97% accuracy rate in counting. The development of the model is associated with a few challenges, including exploring algorithms to enhance the accuracy rate for component identification and testing the model in a larger dataset.

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Ye, Feng, and Jun Yang. "A Deep Neural Network Model for Speaker Identification." Applied Sciences 11, no. 8 (April 16, 2021): 3603. http://dx.doi.org/10.3390/app11083603.

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Speaker identification is a classification task which aims to identify a subject from a given time-series sequential data. Since the speech signal is a continuous one-dimensional time series, most of the current research methods are based on convolutional neural network (CNN) or recurrent neural network (RNN). Indeed, these methods perform well in many tasks, but there is no attempt to combine these two network models to study the speaker identification task. Due to the spectrogram that a speech signal contains, the spatial features of voiceprint (which corresponds to the voice spectrum) and CNN are effective for spatial feature extraction (which corresponds to modeling spectral correlations in acoustic features). At the same time, the speech signal is in a time series, and deep RNN can better represent long utterances than shallow networks. Considering the advantage of gated recurrent unit (GRU) (compared with traditional RNN) in the segmentation of sequence data, we decide to use stacked GRU layers in our model for frame-level feature extraction. In this paper, we propose a deep neural network (DNN) model based on a two-dimensional convolutional neural network (2-D CNN) and gated recurrent unit (GRU) for speaker identification. In the network model design, the convolutional layer is used for voiceprint feature extraction and reduces dimensionality in both the time and frequency domains, allowing for faster GRU layer computation. In addition, the stacked GRU recurrent network layers can learn a speaker’s acoustic features. During this research, we tried to use various neural network structures, including 2-D CNN, deep RNN, and deep LSTM. The above network models were evaluated on the Aishell-1 speech dataset. The experimental results showed that our proposed DNN model, which we call deep GRU, achieved a high recognition accuracy of 98.96%. At the same time, the results also demonstrate the effectiveness of the proposed deep GRU network model versus other models for speaker identification. Through further optimization, this method could be applied to other research similar to the study of speaker identification.

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Scott, Gary M., and W. Harmon Ray. "Neural Network Process Models Based on Linear Model Structures." Neural Computation 6, no. 4 (July 1994): 718–38. http://dx.doi.org/10.1162/neco.1994.6.4.718.

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The KBANN (Knowledge-Based Artificial Neural Networks) approach uses neural networks to refine knowledge that can be written in the form of simple propositional rules. This idea is extended by presenting the MANNIDENT (Multivariable Artificial Neural Network Identification) algorithm by which the mathematical equations of linear dynamic process models determine the topology and initial weights of a network, which is further trained using backpropagation. This method is applied to the task of modeling a nonisothermal chemical reactor in which a first-order exothermic reaction is occurring. This method produces statistically significant gains in accuracy over both a standard neural network approach and a linear model. Furthermore, using the approximate linear model to initialize the weights of the network produces statistically less variation in model fidelity. By structuring the neural network according to the approximate linear model, the model can be readily interpreted.

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Lane, Vicki R., and Susanne G. Scott. "The neural network model of organizational identification." Organizational Behavior and Human Decision Processes 104, no. 2 (November 2007): 175–92. http://dx.doi.org/10.1016/j.obhdp.2007.04.004.

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Chen, H. M., G. Z. Qi, J. C. S. Yang, and F. Amini. "Neural Network for Structural Dynamic Model Identification." Journal of Engineering Mechanics 121, no. 12 (December 1995): 1377–81. http://dx.doi.org/10.1061/(asce)0733-9399(1995)121:12(1377).

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Varma, Teena. "Camera Model Identification using Convolutional Neural Network." International Journal for Research in Applied Science and Engineering Technology 9, no. 3 (March 31, 2021): 618–22. http://dx.doi.org/10.22214/ijraset.2021.33305.

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Zhang, Shuang, Gang Jin, Jing Xiao, Shu Li, Yu Ping Qin, Jin Hua Liu, Tao An, and Wei Fan Zhong. "Generalized Constraint Neural Network Model System Parameter Identification." Advanced Materials Research 143-144 (October 2010): 1207–12. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.1207.

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By analyzing and deducing generalized constraint neural network (GCNN) with model some present theories, the identification method of the m-input n-output (MINO) and multiple-input multiple–output (MIMO) systems is acquired. It is possible to improve the transparency of the black box through the practical test. This identification method is useful to enhance identification of GCNN model’s parameters, moreover, the identification ability of the neural network black box system model is further made better.

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Wang, Jianfeng, Yiqun Liu, Liang Ding, Jun Li, Haibo Gao, Yuhan Liang, and Tianyao Sun. "Neural Network Identification of a Racing Car Tire Model." Journal of Engineering 2018 (May 29, 2018): 1–11. http://dx.doi.org/10.1155/2018/4143794.

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In order to meet the demands of small race car dynamics simulation, a new method of parameter identification in the Magic Formula tire model is presented in this work, based on an analysis of the Magic Formula tire model structure. A high-precision tire model used for vehicle dynamics simulation is established via this method. It is difficult for students to build a high-precision tire model because of the complexity of widely used tire models such as Magic Formula and UniTire. At a pure side slip condition, building a lateral force model is an example, which illustrate the utilization of a multilayer feed-forward neural network to build an intelligent tire model conveniently. In order to fully understand the difference between the two models, a two-degrees-of-freedom (2 DOF) vehicle model is established. The advantages, disadvantages, and applicable scope of the two tire models are discussed after comparing the simulation results of the 2 DOF model with the Magic Formula and intelligent tire model.

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Fei, Qing Guo, Ai Qun Li, Chang Qing Miao, and Zhi Jun Li. "Structural Damage Identification Using Wavelet Packet Analysis and Neural Network." Key Engineering Materials 324-325 (November 2006): 205–8. http://dx.doi.org/10.4028/www.scientific.net/kem.324-325.205.

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This paper describes a study on damage identification using wavelet packet analysis and neural networks. The identification procedure could be divided into three steps. First, structure responses are decomposed into wavelet packet components. Then, the component energies are used to define damage feature and to train neural network models. Finally, in combination with the feature of the damaged structure response, the trained models are employed to determine the occurrence, the location and the qualification of the damage. The emphasis of this study is put on multi-damage case. Relevant issues are studied in detail especially the selection of training samples for multi-damage identification oriented neural network training. A frame model is utilized in the simulation cases to study the sampling techniques and the multi-damage identification. Uniform design is determined to be the most suitable sampling technique through simulation results. Identifications of multi-damage cases of the frame including different levels of damage at various locations are investigated. The results show that damages are successfully identified in all cases.

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Nassif, Ali Bou, Noha Alnazzawi, Ismail Shahin, Said A. Salloum, Noor Hindawi, Mohammed Lataifeh, and Ashraf Elnagar. "A Novel RBFNN-CNN Model for Speaker Identification in Stressful Talking Environments." Applied Sciences 12, no. 10 (May 11, 2022): 4841. http://dx.doi.org/10.3390/app12104841.

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Speaker identification systems perform almost ideally in neutral talking environments. However, these systems perform poorly in stressful talking environments. In this paper, we present an effective approach for enhancing the performance of speaker identification in stressful talking environments based on a novel radial basis function neural network-convolutional neural network (RBFNN-CNN) model. In this research, we applied our approach to two distinct speech databases: a local Arabic Emirati-accent dataset and a global English Speech Under Simulated and Actual Stress (SUSAS) corpus. To the best of our knowledge, this is the first work that addresses the use of an RBFNN-CNN model in speaker identification under stressful talking environments. Our speech identification models select the finest speech signal representation through the use of Mel-frequency cepstral coefficients (MFCCs) as a feature extraction method. A comparison among traditional classifiers such as support vector machine (SVM), multilayer perceptron (MLP), k-nearest neighbors algorithm (KNN) and deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN), was conducted. The results of our experiments show that speaker identification performance in stressful environments based on the RBFNN-CNN model is higher than that with the classical and deep machine learning models.

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Shamseldin, Mohamed A. "Deep Neural Network System Identification for Servomechanism System." DESIGN, CONSTRUCTION, MAINTENANCE 2 (May 11, 2022): 125–32. http://dx.doi.org/10.37394/232022.2022.2.18.

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This paper presents a systematic technique for designing the input signal to identify the one-stage servomechanism system. Sources of nonlinearities such as friction and backlash consider an obstacle to obtaining an accurate model. Also, most such systems suffer from a lack of system parameters data. So, this study establishes a model using the black-box modeling approach; simulations are performed based on real-time data collected by LabVIEW software and processed using MATLAB System Identification toolbox. The input signal for the servomechanism system driver is a pseudo-random binary sequence that considers violent excitation in the frequency interval and the output signal is the corresponding stage speed measured by rotary encoder. The candidate models were obtained using linear least squares, nonlinear least squares, and Deep Neural Network (DNN). The validation results proved that the identified model based on DNN has the smallest mean square errors compared to other candidate models.

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Sasaki, Minoru, Takuya Murase, Yoshihiro Inoue, and Nobuharu Ukita. "Neural Networks-Based Identification and Control of a Large Flexible Antenna." ISRN Mechanical Engineering 2011 (October 1, 2011): 1–8. http://dx.doi.org/10.5402/2011/213582.

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This paper presents identification and control of a 10-m antenna via accelerometers and angle encoder data. Artificial neural networks can be used effectively for the identification and control of nonlinear dynamical system such as a large flexible antenna with a friction drive system. Some identification results are shown and compared with the results of conventional prediction error method. And we use a neural network inverse model to control the large flexible antenna. In the neural network inverse model, a neural network is trained, using supervised learning, to develop an inverse model of the antenna. The network input is the process output, and the network output is the corresponding process input. The control results show the validation of the ANN approach for identification and control of the 10-m flexible antenna.

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Chun, Teo Hong, Ummi Raba'ah Hashim, Sabrina Ahmad, Lizawati Salahuddin, Ngo Hea Choon, Kasturi Kanchymalay, and Nur Haslinda Ismail. "Identification of wood defect using pattern recognition technique." International Journal of Advances in Intelligent Informatics 7, no. 2 (April 19, 2021): 163. http://dx.doi.org/10.26555/ijain.v7i2.588.

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This study proposed a classification model for timber defect classification based on an artificial neural network (ANN). Besides that, the research also focuses on determining the appropriate parameters for the neural network model in optimizing the defect identification performance, such as the number of hidden layers nodes and the number of epochs in the neural network. The neural network's performance is compared with other standard classifiers such as Naïve Bayes, K-Nearest Neighbours, and J48 Decision Tree in finding their significant differences across the multiple timber species. The classifier's performance is measured based on the F-measure due to the imbalanced dataset of the timber species. The experimental results show that the proposed classification model based on the neural network outperforms the other standard classifiers in detecting many types of defects across multiple timber species with an F-measure of 84.01%. This research demonstrates that ANN can accurately classify the defects across multiple species while defining appropriate parameters (hidden layers and epochs) for the neural network model in optimizing defect identification performance.

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Zhang, Jing Jun, Bing An Han, and Rui Zen Gao. "Multi-Body Model Identification of Vehicle Semi-Active Suspension Based on Genetic Neural Network." Applied Mechanics and Materials 121-126 (October 2011): 4069–73. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.4069.

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A multi-body vehicle dynamics model was established using ADAMS and a multilayer feed forward neural network of series parallel structure was built by Matlab in this study. The weights and threshold of neural networks which has built was optimizes by GA. This method was used in identifying multi-body vehicle dynamics model. The results show that the maximum error of identification is less than 0.05% and the network convergence rapidly. The designed genetic neural network could replace the vehicle semi-active suspension systems using in neural network adaptive control which can avoid the difficulty of establishing accurately mathematical model and the poor effective of traditional identification methods for the vehicle semi-active suspension.

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Akishin, P. G., P. Akritas, I. Antoniou, and V. V. Ivanov. "Identification of discrete chaotic maps with singular points." Discrete Dynamics in Nature and Society 6, no. 3 (2001): 147–56. http://dx.doi.org/10.1155/s1026022601000164.

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We investigate the ability of artificial neural networks to reconstruct discrete chaotic maps with singular points. We use as a simple test model the Cusp map. We compare the traditional Multilayer Perceptron, the Chebyshev Neural Network and the Wavelet Neural Network. The numerical scheme for the accurate determination of a singular point is also developed. We show that combining a neural network with the numerical algorithm for the determination of the singular point we are able to accurately approximate discrete chaotic maps with singularities.

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P., Vijay Babu, and Senthil Kumar R. "Performance Evaluation of Brain Tumor Identification and Examination Using MRI Images with Innovative Convolution Neural Networks and Comparing the Accuracy with RNN Algorithm." ECS Transactions 107, no. 1 (April 24, 2022): 12405–14. http://dx.doi.org/10.1149/10701.12405ecst.

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The main aim of the paper is to find the accuracy for brain tumor detection using the Innovative CNN and RNN algorithms. The paper addresses the design and implementation of brain tumor detection with an accurate prediction. Materials and Methods: Innovative Convolutional Neural Networks and Recurrent Neural Networks are used for finding the accuracy of brain tumor detection. Data models were trained with the neural network algorithms where the brain tumor model adopts the data models and gives responses by adopting those effectively. The model checks patterns for providing the responses to the users by using a pattern matching module. Accuracy calculation was done by using neural network algorithms. Results: The accuracy of Innovative Convolutional Neural Network in brain tumor detection is more significantly improved which is more than 95% (approx.) than the Recurrent Neural Networks. Conclusion: Based on Independent T-test analysis using SPSS statistical software, the innovative Convolutional Neural Network algorithm is significant and has more accuracy compared to Recurrent Neural Networks.

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Dou, Zhen Hai, and Ya Jing Wang. "Identification of Complex System Based on Neural Network." Advanced Materials Research 433-440 (January 2012): 4342–47. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.4342.

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In order to conquer the difficulty of building up the mathematics model of some complex system, model identification method based on neural network is put forward. By this method, according to actual sample datum, the complex model of crude oil heating furnace is identified at appropriate quantity of net layers and notes. The identification results show that output of model can basically consistent with the actual output and their mean squared error (MSE) almost is 0. Therefore, model identification method based on neural network is an effective method in complex system identification.

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Shevtsova, Natalia, and James A. Reggia. "A Neural Network Model of Lateralization during Letter Identification." Journal of Cognitive Neuroscience 11, no. 2 (March 1999): 167–81. http://dx.doi.org/10.1162/089892999563300.

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The causes of cerebral lateralization of cognitive and other functions are currently not well understood. To investigate one aspect of function lateralization, a bihemispheric neural network model for a simple visual identification task was developed that has two parallel interacting paths of information processing. The model is based on commonly accepted concepts concerning neural connectivity, activity dynamics, and synaptic plasticity. A combination of both unsupervised (Hebbian) and supervised (Widrow-Hoff) learning rules is used to train the model to identify a small set of letters presented as input stimuli in the left visual hemifield, in the central position, and in the right visual hemifield. Each visual hemifield projects onto the contralateral hemisphere, and the two hemispheres interact via a simulated corpus callosum. The contribution of each individual hemisphere to the process of input stimuli identification was studied for a variety of underlying asymmetries. The results indicate that multiple asymmetries may cause lateralization. Lateralization occurred toward the side having larger size, higher excitability, or higher learning rate parameters. It appeared more intensively with strong inhibitory callosal connections, supporting the hypothesis that the corpus callosum plays a functionally inhibitory role. The model demonstrates clearly the dependence of lateralization on different hemisphere parameters and suggests that computational models can be useful in better understanding the mechanisms underlying emergence of lateralization.

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Karanayil, Baburaj, Muhammed Fazlur Rahman, and Colin Grantham. "Identification of Induction Motor Parameters in Industrial Drives with Artificial Neural Networks." Advances in Fuzzy Systems 2009 (2009): 1–10. http://dx.doi.org/10.1155/2009/241809.

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This paper presents a new method of online estimation of the stator and rotor resistance of the induction motor in the indirect vector-controlled drive, with artificial neural networks. The back propagation algorithm is used for training of the neural networks. The error between the rotor flux linkages based on a neural network model and a voltage model is back propagated to adjust the weights of the neural network model for the rotor resistance estimation. For the stator resistance estimation, the error between the measured stator current and the estimated stator current using neural network is back propagated to adjust the weights of the neural network. The performance of the stator and rotor resistance estimators and torque and flux responses of the drive, together with these estimators, is investigated with the help of simulations for variations in the stator and rotor resistance from their nominal values. Both types of resistance are estimated experimentally, using the proposed neural network in a vector-controlled induction motor drive. Data on tracking performances of these estimators are presented. With this approach, the rotor resistance estimation was found to be insensitive to the stator resistance variations both in simulation and experiment.

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Shyamalagowri, M., and R. Rajeswari. "Neural Network Predictive Controller Based Nonlinearity Identification Case Study: Nonlinear Process Reactor - CSTR." Advanced Materials Research 984-985 (July 2014): 1326–34. http://dx.doi.org/10.4028/www.scientific.net/amr.984-985.1326.

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In the last decades, a substantial amount of research has been carried out on identification of nonlinear processes. Dynamical systems can be better represented by nonlinear models, which illustrate the global behavior of the nonlinear process reactor over the entire range. CSTR is highly nonlinear chemical reactor. A compact and resourceful model which approximates both linear and nonlinear component of the process is of highly demand. Process modeling is an essential constituent in the growth of sophisticated model-based process control systems. Driven by the contemporary economical needs, developments in process design point out that deliberate operation requires better models. The neural network predictive controller is very efficient to identify complex nonlinear systems with no complete model information. Closed loop method is preferred because it is sensitive to disturbances, no need identify the transfer function model of an unstable system. In this paper identification nonlinearities for a nonlinear process reactor CSTR is approached using neural network predictive controller. KEYWORDS Continuous Stirred Tank Reactor, Multi Input Multi Output, Neural Networks, Chebyshev Neural Networks, Predictive Controller.

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Li, Xiangyu, Chunhua Yuan, and Bonan Shan. "System Identification of Neural Signal Transmission Based on Backpropagation Neural Network." Mathematical Problems in Engineering 2020 (August 12, 2020): 1–8. http://dx.doi.org/10.1155/2020/9652678.

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The identification method of backpropagation (BP) neural network is adopted to approximate the mapping relation between input and output of neurons based on neural firing trajectory in this paper. In advance, the input and output data of neural model is used for BP neural network learning, so that the identified BP neural network can present the transfer characteristics of the model, which makes the network precisely predict the firing trajectory of the neural model. In addition, the method is applied to identify electrophysiological experimental data of real neurons, so that the output of the identified BP neural network can not only accurately fit the neural firing trajectories of neurons participating in the network training but also predict the firing trajectories and spike moments of neurons which are not involved in the training process with high accuracy.

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Amir, Mounir, Mourad Zergoug, and Aissa Amrouche. "Identification Parameters with Neural Network for Preisach Hysteresis Model." Applied Mechanics and Materials 541-542 (March 2014): 487–93. http://dx.doi.org/10.4028/www.scientific.net/amm.541-542.487.

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The description of hysteresis is one of the classical problems in magnetic materials. The progress in its solution determines the reliability of modeling and the quality of design of a wide range of devices, the proposed approach has been applied to model the behavior of many samples and the results show the robustness and efficiency of Neural Network to model the phenomenon of hysteresis loop. The goal of this study is to optimize the parameters of hysteresis Loop by Preisach model with the Neural Network, the method developed is based on an analysis of two distribution functions. The modified Lorentzian function and Gaussian function have been analyzed. The implemented software and performances of the distributions are presented.

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Jiang, Xiaomo, Sankaran Mahadevan, and Yong Yuan. "Fuzzy stochastic neural network model for structural system identification." Mechanical Systems and Signal Processing 82 (January 2017): 394–411. http://dx.doi.org/10.1016/j.ymssp.2016.05.030.

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Constantin, Pavlitov, Chen Hao, Gorbounov Yassen, Georgiev Tzanko, Xing Wang, and Xiao-shu Zan. "Artificial neural network identification model of SRM 12-8." Procedia Earth and Planetary Science 1, no. 1 (September 2009): 1301–11. http://dx.doi.org/10.1016/j.proeps.2009.09.201.

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Manikandan, K. "GENDER IDENTIFICATION USING FUZZY LOGIC AND NEURAL NETWORK MODEL." International Journal of Advanced Research 7, no. 6 (June 30, 2019): 582–88. http://dx.doi.org/10.21474/ijar01/9257.

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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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Xu, Jing, and Xiu Li Wang. "A Structural Identification Method Based on Recurrent Neural Network and Auto-Regressive and Moving Average Model." Applied Mechanics and Materials 256-259 (December 2012): 2261–65. http://dx.doi.org/10.4028/www.scientific.net/amm.256-259.2261.

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The work presented a structural identification method based on recurrent neural network and auto-regressive and moving average model. The proposed approach involves two steps. The first step is to build a recurrent neural network to map the complex nonlinear relation between the excitations and responses of the structure-unknown system by on-line learning . The second step is to propose a procedure to determine the modal parameters of the structure from the trained neural networks. The dynamic characteristics of the structure are directly evaluated from the weighting matrices of the trained recurrent neural network. Furthermore, a illustrative example demonstrates the feasibility of using the proposed method to identify modal parameters of structure-unknown systems.

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Zhao, Ziyu, Xiaoxia Yang, Zhedong Ge, Hui Guo, and Yucheng Zhou. "Wood microscopic image identification method based on convolution neural network." BioResources 16, no. 3 (May 24, 2021): 4986–99. http://dx.doi.org/10.15376/biores.16.3.4986-4999.

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To prevent the illegal trade of precious wood in circulation, a wood species identification method based on convolutional neural network (CNN), namely PWoodIDNet (Precise Wood Specifications Identification) model, is proposed. In this paper, the PWoodIDNet model for the identification of rare tree species is constructed to reduce network parameters by decomposing convolutional kernel, prevent overfitting, enrich the diversity of features, and improve the performance of the model. The results showed that the PWoodIDNet model can effectively improve the generalization ability, the characterization ability of detail features, and the recognition accuracy, and effectively improve the classification of wood identification. PWoodIDNet was used to analyze the identification accuracy of microscopic images of 16 kinds of wood, and the identification accuracy reached 99%, which was higher than the identification accuracy of several existing classical convolutional neural network models. In addition, the PWoodIDNet model was analyzed to verify the feasibility and effectiveness of the PWoodIDNet model as a wood identification method, which can provide a new direction and technical solution for the field of wood identification.

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Rodiah, Sarifuddin Madenda, Diana Tri Susetianingtias, Fitrianingsih, Dea Adlina, and Rini Arianty. "Retinal biometric identification using convolutional neural network." Computer Optics 45, no. 6 (November 2021): 865–72. http://dx.doi.org/10.18287/2412-6179-co-890.

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Authentication is needed to enhance and protect the system from vulnerabilities or weaknesses of the system. There are still many weaknesses in the use of traditional authentication methods such as PINs or passwords, such as being hacked. New methods such as system biometrics are used to deal with this problem. Biometric characteristics using retinal identification are unique and difficult to manipulate compared to other biometric characteristics such as iris or fingerprints because they are located behind the human eye thus they are difficult to reach by normal human vision. This study uses the characteristics of the retinal fundus image blood vessels that have been segmented for its features. The dataset used is sourced from the DRIVE dataset. The preprocessing stage is used to extract its features to produce an image of retinal blood vessel segmentation. The image resulting from the segmentation is carried out with a two-dimensional image transformation such as the process of rotation, enlargement, shifting, cutting, and reversing to increase the quantity of the sample of the retinal blood vessel segmentation image. The results of the image transformation resulted in 189 images divided with the details of the ratio of 80 % or 151 images as training data and 20 % or 38 images as validation data. The process of forming this research model uses the Convolutional Neural Network method. The model built during the training consists of 10 iterations and produces a model accuracy value of 98 %. The results of the model's accuracy value are used for the process of identifying individual retinas in the retinal biometric system.

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Hassan, Sk Mahmudul, Michal Jasinski, Zbigniew Leonowicz, Elzbieta Jasinska, and Arnab Kumar Maji. "Plant Disease Identification Using Shallow Convolutional Neural Network." Agronomy 11, no. 12 (November 24, 2021): 2388. http://dx.doi.org/10.3390/agronomy11122388.

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Various plant diseases are major threats to agriculture. For timely control of different plant diseases in effective manner, automated identification of diseases are highly beneficial. So far, different techniques have been used to identify the diseases in plants. Deep learning is among the most widely used techniques in recent times due to its impressive results. In this work, we have proposed two methods namely shallow VGG with RF and shallow VGG with Xgboost to identify the diseases. The proposed model is compared with other hand-crafted and deep learning-based approaches. The experiments are carried on three different plants namely corn, potato, and tomato. The considered diseases in corns are Blight, Common rust, and Gray leaf spot, diseases in potatoes are early blight and late blight, and tomato diseases are bacterial spot, early blight, and late blight. The result shows that our implemented shallow VGG with Xgboost model outperforms different deep learning models in terms of accuracy, precision, recall, f1-score, and specificity. Shallow Visual Geometric Group (VGG) with Xgboost gives the highest accuracy rate of 94.47% in corn, 98.74% in potato, and 93.91% in the tomato dataset. The models are also tested with field images of potato, corn, and tomato. Even in field image the average accuracy obtained using shallow VGG with Xgboost are 94.22%, 97.36%, and 93.14%, respectively.

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Sarada, N., and K. Thirupathi Rao. "A Neural Network Architecture Using Separable Neural Networks for the Identification of “Pneumonia” in Digital Chest Radiographs." International Journal of e-Collaboration 17, no. 1 (January 2021): 89–100. http://dx.doi.org/10.4018/ijec.2021010106.

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In recent years, convolutional neural networks had a wide impact in the fields of medical image processing. Image semantic segmentation and image classification have been the main challenges in this field. These two techniques have been seeing a lot of improvement in medical surgeries which are being carried out by robots and autonomous machines. This work will be working on a convolutional model to detect pneumonia in a given chest x-ray scan. In addition to the convolution model, the proposed model consists of deep separable convolution kernels which replace few convolutional layers; one main advantage is these take in a smaller number of parameters and filters. The described model will be more efficient, robust, and fine-tuned than previous models developed using convolutional neural networks. The authors also benchmarked the present model with the CheXnet model, which almost predicts over 16 abnormalities in the given chest-x-rays.

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Zhang, Min, and Guohua Geng. "Adverse Drug Event Detection Using a Weakly Supervised Convolutional Neural Network and Recurrent Neural Network Model." Information 10, no. 9 (September 4, 2019): 276. http://dx.doi.org/10.3390/info10090276.

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Social media and health-related forums, including the expression of customer reviews, have recently provided data sources for adverse drug reaction (ADR) identification research. However, in the existing methods, the neglect of noise data and the need for manually labeled data reduce the accuracy of the prediction results and greatly increase manual labor. We propose a novel architecture named the weakly supervised mechanism (WSM) convolutional neural network (CNN) long-short-term memory (WSM-CNN-LSTM), which combines the strength of CNN and bi-directional long short-term memory (Bi-LSTM). The WSM applies the weakly labeled data to pre-train the parameters of the model and then uses the labeled data to fine-tune the initialized network parameters. The CNN employs a convolutional layer to study the characteristics of the drug reviews and active features at different scales, and then the feed-forward and feed-back neural networks of the Bi-LSTM utilize these salient features to output the regression results. The experimental results effectively demonstrate that our model marginally outperforms the comparison models in ADR identification and that a small quantity of labeled samples results in an optimal performance, which decreases the influence of noise and reduces the manual data-labeling requirements.

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TISDALE, E. ROBERT, and WALTER J. KARPLUS. "SYSTEM IDENTIFICATION WITH ARTIFICIAL NEURAL NETWORKS." International Journal of Pattern Recognition and Artificial Intelligence 06, no. 01 (April 1992): 93–111. http://dx.doi.org/10.1142/s0218001492000059.

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System identification is the term scientists and engineers use to refer to the process of building mathematical models of dynamical systems based on observed data. This paper approaches system identification as a pattern recognition problem. We use computers to simulate the system response for a variety of different mathematical models. For each distinct system model, simulated system responses tend to remain segregated in one or more amorphous regions of system response space despite (1) large variations in system parameters, (2) experimental errors, and (3) noise. The actual system response is classified with the model corresponding to the region of system response space where it is found. The classifier is an Artificial Neural Network (ANN) which implements a Generalized Vector Quantizer (GVQ). A small number of simple but powerful discriminant functions facilitate the correct classification of most of the responses in any given region. The required distribution of discriminants among the regions evolves automatically as they learn their respective functions.

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He, Ping, Yong Li, Shoulong Chen, Hoghua Xu, Lei Zhu, and Lingyan Wang. "Core looseness fault identification model based on Mel spectrogram-CNN." Journal of Physics: Conference Series 2137, no. 1 (December 1, 2021): 012060. http://dx.doi.org/10.1088/1742-6596/2137/1/012060.

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Abstract In order to realize transformer voiceprint recognition, a transformer voiceprint recognition model based on Mel spectrum convolution neural network is proposed. Firstly, the transformer core looseness fault is simulated by setting different preloads, and the sound signals under different preloads are collected; Secondly, the sound signal is converted into a spectrogram that can be trained by convolutional neural network, and then the dimension is reduced by Mel filter bank to draw Mel spectrogram, which can generate spectrogram data sets under different preloads in batch; Finally, the data set is introduced into convolutional neural network for training, and the transformer voiceprint fault recognition model is obtained. The results show that the training accuracy of the proposed Mel spectrum convolution neural network transformer identification model is 99.91%, which can well identify the core loosening faults.

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Nguyen, Viet-Hung, Minh-Tuan Nguyen, Jeongsik Choi, and Yong-Hwa Kim. "NLOS Identification in WLANs Using Deep LSTM with CNN Features." Sensors 18, no. 11 (November 20, 2018): 4057. http://dx.doi.org/10.3390/s18114057.

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Identifying channel states as line-of-sight or non-line-of-sight helps to optimize location-based services in wireless communications. The received signal strength identification and channel state information are used to estimate channel conditions for orthogonal frequency division multiplexing systems in indoor wireless local area networks. This paper proposes a joint convolutional neural network and recurrent neural network architecture to classify channel conditions. Convolutional neural networks extract the feature from frequency-domain characteristics of channel state information data and recurrent neural networks extract the feature from time-varying characteristics of received signal strength identification and channel state information between packet transmissions. The performance of the proposed methods is verified under indoor propagation environments. Experimental results show that the proposed method has a 2% improvement in classification performance over the conventional recurrent neural network model.

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Zhu, Chang Jun, Li Ping Wu, and Sha Li. "Flood Forecasting Research Based on the Chaotic BP Neural Network Model." Key Engineering Materials 439-440 (June 2010): 411–16. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.411.

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In view of the problem that the predictive results of flow quantity are not ideal for the predictive models at present. Based on the chaos identification to the flood system, chaos BP neural network model are developed combined chaos theory and BP neural netwok, flood sequences are disposed by phase-space reconstruction to be as training sample. Network structure can be determined by Matlab toolbox. The established chaos BP model is used to predict the phenomenon of peak value for Huayuankou hydrometric station in 2006. The results show that the predictive model combined chaos theory and BP neural network, has certain reference value to improve flood forecasting accuracy as a new attempt.

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Yin, Rongwang, Qingyu Li, Peichao Li, and Detang Lu. "Parameter Identification of Multistage Fracturing Horizontal Well Based on PSO-RBF Neural Network." Scientific Programming 2020 (July 3, 2020): 1–11. http://dx.doi.org/10.1155/2020/6810903.

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In order to more accurately identify multistage fracturing horizontal well (MFHW) parameters and address the heterogeneity of reservoirs and the randomness of well-production data, a new method based on the PSO-RBF neural network model is proposed. First, the GPU parallel program is used to calculate the bottomhole pressure of a multistage fracturing horizontal well. Second, most of the above pressure data are imported into the RBF neural network model for training. In the training process, the optimization function of the global optimal solution of the PSO algorithm is employed to optimize the parameters of the RBF neural network, and eventually, the required PSO-RBF neural network model is established. Third, the resulting neural network is tested using the remaining data. Finally, a field case of a multistage fracturing horizontal well is studied by using the presented PSO-RBF neural network model. The results show that in most cases, the proposed model performs better than other models, with the highest correlation coefficient, the lowest mean, and absolute error. This proves that the PSO-RBF neural network model can be applied effectively to horizontal well parameter identification. The proposed model has great potential to improve the prediction accuracy of reservoir physical parameters.

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Gong, Rui Kun, Ping Ting Liu, Yu Han Gong, and Chong Hao Wang. "Image Identification Based on the Compound Model of Wavelet Transform and RBF Neural Networks." Applied Mechanics and Materials 513-517 (February 2014): 4152–55. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.4152.

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The image definition identification method based on the composite model of wavelet transform and neural networks is stronger in image edge character extraction, nonlinear process, self-adapted study and pattern recognition. The paper puts forward an evaluation method of image definition based on the focusing mechanism of simulating persons eyes by neural networks and on the composite model of wavelet transformation and neural networks. The wavelet component statistics obtained by the wavelet transform are taken as the inputs of the 5 layer RBF neural network model. The model identifies the image definition applying the steepest descent method of the additional momentum in a variable step size to adjust the network weights. The compound model is first trained by 75 images from the training set, and then is tested by 102 images from the testing set. The results show that this is a very effective identification method which can obtain a higher recognition rate.

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Aalizadeh, Bagher. "Comparison of neural network and neurofuzzy identification of vehicle handling under uncertainties." Transactions of the Institute of Measurement and Control 41, no. 15 (June 21, 2019): 4230–39. http://dx.doi.org/10.1177/0142331219854572.

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In this study, two adaptive neural network and neurofuzzy identification models are proposed to identify vehicle handling under uncertainties. These models are used to identify vehicle handling in different road friction coefficients and velocities. These two identification models modify their weights to cope with uncertainties using back propagation of error as a learning algorithm. However, an adaptive model has some limitations to identify real systems. The ability of adaptation is not the same for all identification models; some models are more robust to cope with a specific uncertainty or a wider range of uncertainties. In this study, adaptiveness of two identification models are compared under two different uncertainties. First, a precise model in CARSIM software is simulated and a set of input/output data of vehicle response are collected. Then an initial three-layer neural network is trained in MATLAB software. In addition, a Neurofuzzy model is also trained in ANFIS (adaptive neurofuzzy inference system) toolbox of MATLAB software. Then this trained model is applied to the vehicle in different maneuvers, velocities and road friction coefficients. Results show that proposed neural network identifies the vehicle handling more efficiently than neurofuzzy model in conditions that are away from training condition. However, proposed neurofuzzy model is more precise and accurate than neural network in the training condition.

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Solan, Zach, and Eytan Ruppin. "Similarity in Perception: A Window to Brain Organization." Journal of Cognitive Neuroscience 13, no. 1 (January 1, 2001): 18–30. http://dx.doi.org/10.1162/089892901564144.

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This paper presents a neural model of similarity perception in identification tasks. It is based on self-organizing maps and population coding and is examined through five different identification experiments. Simulating an identification task, the neural model generates a confusion matrix that can be compared directly with that of human subjects. The model achieves a fairly accurate match with the pertaining experimental data both during training and thereafter. To achieve this fit, we find that the entire activity in the network should decline while learning the identification task, and that the population encoding of the specific stimuli should become sparse as the network organizes. Our results, thus, suggest that a self-organizing neural model employing population coding can account for identification processing while suggesting computational constraints on the underlying cortical networks.

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Basak, Jayanta. "Learning Hough Transform: A Neural Network Model." Neural Computation 13, no. 3 (March 1, 2001): 651–76. http://dx.doi.org/10.1162/089976601300014501.

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A single-layered Hough transform network is proposed that accepts image coordinates of each object pixel as input and produces a set of outputs that indicate the belongingness of the pixel to a particular structure (e.g., a straight line). The network is able to learn adaptively the parametric forms of the linear segments present in the image. It is designed for learning and identification not only of linear segments in two-dimensional images but also the planes and hyperplanes in the higher-dimensional spaces. It provides an efficient representation of visual information embedded in the connection weights. The network not only reduces the large space requirement, as in the case of classical Hough transform, but also represents the parameters with high precision.

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Gong, Rui Kun, Ya Nan Zhang, Chong Hao Wang, and Li Jing Zhao. "Application of the Compound Model of BP Neural Networks and Wavelet Transform in Image Definition Identification." Advanced Materials Research 605-607 (December 2012): 2265–69. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.2265.

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First, the background, significance and general implementation of the image definition identification are introduced. Then, basic theory of wavelet transform and neural network is expounded. An identification method of image definition based on the composite model of wavelet analysis and neural network is suggested．The two—dimensional discrete wavelet transformation is used to filter image signal and extract its brim character which is input into BP neural network for identification. 4 layers of BP neural network are constructed to perform image definition identification. The compound model is first trained by 90 images from the training set, and then is tested by 87 images from the testing set. The results show that this is a very effective identification method which can obtain a higher recognition rate.

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BAHRAMI, MOHAMMAD, and KEITH E. TAIT. "MODEL REFERENCE DIRECT ADAPTIVE CONTROL OF NONLINEAR PLANTS USING NEURAL NETWORKS." International Journal of Neural Systems 05, no. 01 (March 1994): 77–82. http://dx.doi.org/10.1142/s0129065794000098.

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A learning scheme for multilayer feedforward neural networks used as direct adaptive controllers of nonlinear plants is suggested. This scheme is a supervised steepest descent one that does not require backpropagation of the error. Using a neural network controller trained with this method does not require the identification stage and this makes it superior to the other methodologies. Methods for using neural networks in plant control suggested in the literature are discussed and compared with the proposed system. The structure of the network and the training method used are explained. Simulations based on model reference control of some nonlinear plants show satisfactory performance.

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Kyrylenko, O. M. "Development of a method of re-identification of a person." Optoelectronic Information-Power Technologies 41, no. 1 (May 2, 2022): 25–32. http://dx.doi.org/10.31649/1681-7893-2021-41-1-25-32.

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The review of OSNet neural network architecture is made for the purpose of training of own models of re-identification of the person. The structure of the neural network was also considered. Existing data sets for model training are investigated. Models were trained using PyTorch. The obtained own models were tested on the validation databases Market-1501 and DukeMTMC-reID. The results of learning neural network models are presented. The results are obtained in comparison with existing analogues.

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Moldovan, Liviu, Horațiu-Ștefan Grif, and Adrian Gligor. "ANN Based Inverse Dynamic Model of the 6-PGK Parallel Robot Manipulator." International Journal of Computers Communications & Control 11, no. 1 (November 16, 2015): 90. http://dx.doi.org/10.15837/ijccc.2016.1.1962.

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<p>This paper presents an inverse dynamic model estimation based on an artificial neural network of a complete new parallel robot manipulator prototype 6- PGK with six degrees of freedom, built at Petru Maior University of Tirgu-Mures. The model estimation of the parallel robot manipulator is performed with a feedforward artificial neural network. In the control engineering domain there are control structures that need the direct or inverse model of the process for ensuring the process control at the imposed performances. Usually, the determination of the direct/inverse mathematical model is a difficult or impossible task to be achieved. In these cases different non-parametric or parametric, off-line or on-line identification methods are used. A solution that may support the on-line parametric methods is represented by the feedforward artificial neural networks. By implementing feedforward artificial neural networks as a nonlinear autoregressive model with exogenous inputs, the authors investigate the possibility of choosing the optimum parameters that characterize the neural network so that it approximates as better as possible the model of the 6-PGK prototype robot. Finally an innovative algorithm is developed for obtaining the optimal configuration parameters set of the feedforward artificial neural network. The proposed algorithm helps in setting the optimal parameters of the neural network that offer high opportunities to provide satisfactory identification of the robot model. Experimental results obtained by a structure derived from the proposed solution demonstrate a good approximation related to the studied system, which is characterized by nonlinearities and high complexity.</p>

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Hou, Run Min, Rong Zhong Liu, Yuan Long Hou, and Qiang Gao. "High-Power AC Servo System Identification Research Based on Wavelet Neural Network." Applied Mechanics and Materials 220-223 (November 2012): 997–1002. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.997.

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As a result of the non-linear characteristics and the uncertain disturbances in high-power AC servo system, it is difficult to construct an accurate mathematical model. In order to solve this problem, this article proposes a system identification method based on wavelet neural network. It makes full use of the advantages of the wavelet which combines neural network good time-frequency localization property and volatility of wavelet function and the nonlinear mapping capacity, self-learning and adaptive capacity of neural networks to solve the problem of non-unique RBF neural network approximation function expression. The simulation results show that the convergence rate, robustness and approximation accuracy of this method are better than the traditional neural network.

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Zhang, Hao Qian. "BP Neural Network and its Improved Algorithm in the Power System Transformer Fault Diagnosis." Applied Mechanics and Materials 418 (September 2013): 200–204. http://dx.doi.org/10.4028/www.scientific.net/amm.418.200.

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According to the measured gas content in power transformers, we use BP neural network to accomplish the pattern recognition of transformer fault. The recognition effect of BP network pattern was studied from the aspects of adding over-fitting operation and genetic algorithm. Four kinds of neural network models, BP model BP & over-fitted identification model GABP model and GABP & over-fitted identification model, were constructed respectively, making the pattern recognition effect further enhanced.

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Luo, Junli, Kai Lu, Yueqi Zhong, Boping Zhang, and Huizhu Lv. "Cashmere and wool identification based on convolutional neural network." Journal of Engineered Fibers and Fabrics 16 (January 2021): 155892502110050. http://dx.doi.org/10.1177/15589250211005088.

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Wool fiber and cashmere fiber are similar in physical and morphological characteristics. Thus, the identification of these two fibers has always been a challenging proposition. This study identifies five kinds of cashmere and wool fibers using a convolutional neural network model. To this end, image preprocessing was first performed. Then, following the VGGNet model, a convolutional neural network with 13 weight layers was established. A dataset with 50,000 fiber images was prepared for training and testing this newly established model. In the classification layer of the model, softmax regression was used to calculate the probability value of the input fiber image for each category, and the category with the highest probability value was selected as the prediction category of the fiber. In this experiment, the total identification accuracy of samples in the test set is close to 93%. Among these five fibers, Mongolian brown cashmere has the highest identification accuracy, reaching 99.7%. The identification accuracy of Chinese white cashmere is the lowest at 86.4%. Experimental results show that our model is an effective approach to the identification of multi-classification fiber.

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Nema, Anjali, and Anshul Khurana. "Real Time Object Identification Using Neural Network with Caffe Model." International Journal of Computer Sciences and Engineering 7, no. 5 (May 31, 2019): 175–82. http://dx.doi.org/10.26438/ijcse/v7i5.175182.

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Journal articles on the topic 'Neural network model of identification'

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