Construction of a prediction model for the structural stability of a surface grinder using backpropagation neural network

2007 ◽  
Vol 37 (11-12) ◽  
pp. 1093-1104 ◽  
Author(s):  
R. M. Hwang ◽  
K. C. Cha
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Structural Stability ◽  
Backpropagation Neural Network

scholarly journals Optimization of Optical Machine Structure by Backpropagation Neural Network Based on Particle Swarm Optimization and Bayesian Regularization Algorithms

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 2998
Author(s):  
Xinyong Zhang ◽  
Liwei Sun
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Prediction Model ◽  
Bayesian Regularization ◽  
Backpropagation Neural Network ◽  
Swarm Optimization ◽  
Modal Frequency ◽  
Supporting Structure ◽  
First Order ◽  
Machine Structure

Fit of the highly nonlinear functional relationship between input variables and output response is important and challenging for the optical machine structure optimization design process. The backpropagation neural network method based on particle swarm optimization and Bayesian regularization algorithms (called BMPB) is proposed to solve this problem. A prediction model of the mass and first-order modal frequency of the supporting structure is developed using the supporting structure as an example. The first-order modal frequency is used as the constraint condition to optimize the lightweight design of the supporting structure’s mass. Results show that the prediction model has more than 99% accuracy in predicting the mass and the first-order modal frequency of the supporting structure, and converges quickly in the supporting structure’s mass-optimization process. The supporting structure results demonstrate the advantages of the method proposed in the article in terms of high accuracy and efficiency. The study in this paper provides an effective method for the optimized design of optical machine structures.

scholarly journals RVLBPNN: A Workload Forecasting Model for Smart Cloud Computing

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Yao Lu ◽  
John Panneerselvam ◽  
Lu Liu ◽  
Yan Wu
Keyword(s):  
Neural Network ◽  
Cloud Computing ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Service Providers ◽  
Cloud Service ◽  
Backpropagation Neural Network ◽  
Environmental Implications ◽  
Cloud Service Providers ◽  
Cloud Workloads

Given the increasing deployments of Cloud datacentres and the excessive usage of server resources, their associated energy and environmental implications are also increasing at an alarming rate. Cloud service providers are under immense pressure to significantly reduce both such implications for promoting green computing. Maintaining the desired level of Quality of Service (QoS) without violating the Service Level Agreement (SLA), whilst attempting to reduce the usage of the datacentre resources is an obvious challenge for the Cloud service providers. Scaling the level of active server resources in accordance with the predicted incoming workloads is one possible way of reducing the undesirable energy consumption of the active resources without affecting the performance quality. To this end, this paper analyzes the dynamic characteristics of the Cloud workloads and defines a hierarchy for the latency sensitivity levels of the Cloud workloads. Further, a novel workload prediction model for energy efficient Cloud Computing is proposed, named RVLBPNN (Rand Variable Learning Rate Backpropagation Neural Network) based on BPNN (Backpropagation Neural Network) algorithm. Experiments evaluating the prediction accuracy of the proposed prediction model demonstrate that RVLBPNN achieves an improved prediction accuracy compared to the HMM and Naïve Bayes Classifier models by a considerable margin.

scholarly journals Prediction of springback in local bending of hull plates using an optimized backpropagation neural network

2021 ◽  
Vol 12 (2) ◽  
pp. 777-789
Author(s):  
Binjiang Xu ◽  
Lei Li ◽  
Zhao Wang ◽  
Honggen Zhou ◽  
Di Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Prediction Model ◽  
Optimal Solution ◽  
Backpropagation Neural Network ◽  
Processing Efficiency ◽  
Model Based ◽  
Assembly Accuracy ◽  
Bending Process ◽  
Springback Prediction

Abstract. Springback is an inevitable problem in the local bending process of hull plates, which leads to low processing efficiency and affects the assembly accuracy. Therefore, the prediction of the springback effect, as a result of the local bending of hull plates, bears great significance. This paper proposes a springback prediction model based on a backpropagation neural network (BPNN), considering geometric and process parameters. Genetic algorithm (GA) and improved particle swarm optimization (PSO) algorithms are used to improve the global search capability of BPNN, which tends to fall into local optimal solutions, in order to find the global optimal solution. The result shows that the proposed springback prediction model, based on the BPNN optimized by genetic algorithm, is faster and offers smaller prediction error on the springback due to local bending.

Stock Market Prediction using Data Mining Techniques

2020 ◽  
pp. 310-313
Author(s):  
Karunesh Makker ◽  
Prince Patel ◽  
Hrishikesh Roy ◽  
Sonali Borse
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Prediction Model ◽  
Efficient Market Hypothesis ◽  
Deterministic System ◽  
Vast Number ◽  
Factors Influencing ◽  
Number Of Factors ◽  
Using Data ◽  
Robust Prediction

Stock market is a very volatile in-deterministic system with vast number of factors influencing the direction of trend on varying scales and multiple layers. Efficient Market Hypothesis (EMH) states that the market is unbeatable. This makes predicting the uptrend or downtrend a very challenging task. This research aims to combine multiple existing techniques into a much more robust prediction model which can handle various scenarios in which investment can be beneficial. Existing techniques like sentiment analysis or neural network techniques can be too narrow in their approach and can lead to erroneous outcomes for varying scenarios. By combing both techniques, this prediction model can provide more accurate and flexible recommendations. Embedding Technical indicators will guide the investor to minimize the risk and reap better returns.

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