Modeling of Piezoelectric Actuators Based on Bayesian Regularization Back Propagation Neural Network

Piezoelectric actuators have been received much attention for the advantages of high precision, no wear and rapid response, etc. However, the intrinsic hysteresis behavior of the piezoelectric materials seriously degraded the output performance of piezoelectric actuators. In this paper, to decrease such nonlinear effects and further improve the output performances of piezoelectric actuators, a modified nonlinear autoregressive moving average with exogenous inputs model, which could describe the rate-dependent hysteresis features of piezoelectric actuators was investigated. In the experiment, the different topologies of the proposed back propagation neural network algorithm were compared and the optimal topology was selected considering both the tracking precision and the structure complexity. The experimental results validated that the modified nonlinear autoregressive moving average with exogenous inputs model featured the hysteresis characteristics description ability with high precision, and the predicted motion matched well with the real trajectory. Then, the initial parameters of the back propagation neural network algorithm were further optimized by particle swarm optimization algorithm. The experimental results also verified that the proposed model based on particle swarm optimization–back propagation neural network algorithm was more accurate than that identified through the conventional back propagation neural network algorithm, and has a better predicting performance.

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Economic benefit of shale gas exploitation based on back propagation neural network

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189279 ◽

2020 ◽

Vol 39 (6) ◽

pp. 8823-8830

Author(s):

Jiafeng Li ◽

Hui Hu ◽

Xiang Li ◽

Qian Jin ◽

Tianhao Huang

Keyword(s):

Neural Network ◽

Shale Gas ◽

Bp Neural Network ◽

Large Scale ◽

Linear Prediction ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Economic Benefits ◽

Gas Well ◽

Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

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An Effective Customer Churn Prediction Model using Adaptive Gain with Back Propagation Neural Network in Cloud Computing Environment

Journal of Research on the Lepidoptera ◽

10.36872/lepi/v51i1/301034 ◽

2020 ◽

Vol 51 (1) ◽

pp. 386-399

Author(s):

JEYAKARTHIC M

Keyword(s):

Neural Network ◽

Cloud Computing ◽

Prediction Model ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Churn Prediction ◽

Computing Environment ◽

Cloud Computing Environment ◽

Customer Churn ◽

Customer Churn Prediction

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An Anatomy of a Hybrid Color Descriptor with a Neural Network Model to Enhance the Retrieval Accuracy of an Image Retrieval System

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666191122113801 ◽

2019 ◽

Vol 13 ◽

Author(s):

Shikha Bhardwaj ◽

Gitanjali Pandove ◽

Pawan Kumar Dahiya

Keyword(s):

Neural Network ◽

Deep Learning ◽

Image Retrieval ◽

Hybrid System ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Retrieval Accuracy ◽

Color Descriptor ◽

Benchmark Datasets ◽

Color Moment

Background: In order to retrieve a particular image from vast repository of images, an efficient system is required and such an eminent system is well-known by the name Content-based image retrieval (CBIR) system. Color is indeed an important attribute of an image and the proposed system consist of a hybrid color descriptor which is used for color feature extraction. Deep learning, has gained a prominent importance in the current era. So, the performance of this fusion based color descriptor is also analyzed in the presence of Deep learning classifiers. Method: This paper describes a comparative experimental analysis on various color descriptors and the best two are chosen to form an efficient color based hybrid system denoted as combined color moment-color autocorrelogram (Co-CMCAC). Then, to increase the retrieval accuracy of the hybrid system, a Cascade forward back propagation neural network (CFBPNN) is used. The classification accuracy obtained by using CFBPNN is also compared to Patternnet neural network. Results: The results of the hybrid color descriptor depict that the proposed system has superior results of the order of 95.4%, 88.2%, 84.4% and 96.05% on Corel-1K, Corel-5K, Corel-10K and Oxford flower benchmark datasets respectively as compared to many state-of-the-art related techniques. Conclusion: This paper depict an experimental and analytical analysis on different color feature descriptors namely, Color moment (CM), Color auto-correlogram (CAC), Color histogram (CH), Color coherence vector (CCV) and Dominant color descriptor (DCD). The proposed hybrid color descriptor (Co-CMCAC) is utilized for the withdrawal of color features with Cascade forward back propagation neural network (CFBPNN) is used as a classifier on four benchmark datasets namely Corel-1K, Corel-5K and Corel-10K and Oxford flower.

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