scholarly journals Stability Prediction Model of Roadway Surrounding Rock Based on Concept Lattice Reduction and a Symmetric Alpha Stable Distribution Probability Neural Network

2018 ◽  
Vol 8 (11) ◽  
pp. 2164 ◽  
Author(s):  
Yang Liu ◽  
Yicheng Ye ◽  
Qihu Wang ◽  
Xiaoyun Liu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Stable Distribution ◽  
Probabilistic Neural Network ◽  
High Reliability ◽  
Concept Lattice ◽  
Engineering Application ◽  
Surrounding Rock ◽  
Lattice Reduction ◽  
Alpha Stable Distribution

To combat the uncertainty of the multiple factors affecting roadway surrounding rock stability, five initial indexes are selected for reduction according to concept lattice theory: rock quality designation (RQD), uniaxial compressive strength (Rc), the integrity coefficient of rock mass, groundwater seepage, and joint condition. The aim of this study is to compute correlation coefficients among various indexes and verify the effectiveness of lattice reduction. Alpha stable distribution is used to replace the commonly used Gauss distribution in probabilistic neural networks. A prediction model for the stability of roadway surrounding rock is then established based on a concept lattice and improved probabilistic neural network. 100 groups of training sample data are plugged into this model one by one to examine its rationality. The established model is employed for engineering application prediction with ten indiscriminate sample groups from the Jianlinshan mining area of the Daye iron mine, revealing accuracy of up to 90%. This demonstrates that our prediction model based on a concept lattice and improved probabilistic neural network has high reliability and applicability.

scholarly journals Multiattribute probabilistic neural network for near-surface field engineering application

2021 ◽  
Vol 40 (11) ◽  
pp. 794-804
Author(s):  
Ahmad Ramdani ◽  
Thomas Finkbeiner ◽  
Viswasanthi Chandra ◽  
Pankaj Khanna ◽  
Sherif Hanafy ◽  
...  
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Engineering Application ◽  
Engineering Project ◽  
Blind Test ◽  
Near Surface ◽  
Surface Reflection ◽  
The Core ◽  
Rock Formations ◽  
Multiattribute Analysis

Unconfined compressive strength (UCS) is an important rock parameter required in the engineering design of structures built on top or within the interior of rock formations. In a site investigation project, UCS is typically obtained discretely (through point-to-point measurement) and interpolated. This method is less than optimal to resolve meter-scale UCS variations of heterogenous rock such as carbonate formations in which property changes occur within data spacing. We investigate the geotechnical application of multiattribute analysis based on near-surface reflection seismic data to probe rock formations for their strength attributes at meter-scale variability. Two Late Jurassic outcrops located in central Saudi Arabia serve as testing sites: the Hanifa Formation in Wadi Birk and the Jubaila Formation in Wadi Laban. The study uses core and 2D seismic profiles acquired in both sites, from which we constrain UCS, acoustic velocity, density, and gamma-ray values. A positive linear correlation between UCS and acoustic impedance along the core indicates that seismic attributes can be utilized as a method to laterally extrapolate the UCS away from the core location. Seismic colored inversion serves as input for neural network multiattribute analysis and is validated with a blind test. Results from data at both outcrop sites indicate a high degree of consistency with an absolute UCS error of approximately 5%. We also demonstrate the applicability of predicted UCS profiles to interpret mechanical stratigraphy and map lateral UCS heterogeneities. These findings provide a less expensive alternative to constrain UCS from limited core data on a field-scale site engineering project.

Quantity Prediction of Mixed Freshwater Fish Based on Passive Underwater Acoustic Signals

2020 ◽  
Vol 63 (6) ◽  
pp. 1805-1811
Author(s):  
Qunzi Tu ◽  
Yongwen Yang ◽  
Hanying Huang ◽  
Lu Li ◽  
Shanbai Xiong ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Linear Regression ◽  
Prediction Model ◽  
Freshwater Fish ◽  
Probabilistic Neural Network ◽  
Recognition Rate ◽  
Acoustic Signals ◽  
Support Vector ◽  
Underwater Acoustic

HighlightsThe use of passive underwater acoustic technology to estimate the species and quantity of freshwater fish provides a theoretical basis for effectively estimating the quantity of freshwater aquaculture.Mixed proportion recognition models for breams and crucians were built using probabilistic neural network (PNN) and support vector machine (SVM), and the influences of different super-parameters on the recognition rate were analyzed. The results showed that the classification model established with SVM after equiripple filtering was best.Mixed quantity prediction models for breams and crucians were constructed using multiple linear regression, and the effects of different filtering methods on the model performance were analyzed. The results showed that the best quantity prediction model was constructed with Butterworth filtering.Abstract. Acoustic signals of breams and crucians were collected at seven mixed proportions and 15 mixed quantitative gradients. After normalization and different filtering processes, the characteristics of the acoustic signals were extracted. Mixed proportion recognition models for breams and crucians were established using probabilistic neural network (PNN) and support vector machine (SVM). The results showed that the model established using SVM after equiripple filtering was best, and the recognition rate was 0.9583. A mixed quantity prediction model for breams and crucians was established by multiple linear regression based on ordinary least squares. The results showed that the model was best after Butterworth filtering, the adjusted decision coefficient of the model was 0.9514, and the relative analysis error was 4.7571. Keywords: Freshwater fish, Passive underwater acoustic signals, Pattern recognition, Regression analysis.

Bus service quality prediction and attribute ranking using probabilistic neural network and adaptive neuro fuzzy inference system

2016 ◽  
Vol 43 (9) ◽  
pp. 822-829 ◽  
Author(s):  
Rokibul Islam ◽  
Sarder Rafee Musabbir ◽  
Irfan Uddin Ahmed ◽  
Md. Hadiuzzaman ◽  
Mehedi Hasnat ◽  
...  
Keyword(s):  
Neural Network ◽  
Service Quality ◽  
Prediction Model ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Probabilistic Neural Network ◽  
Data Set ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Bus Service

This study applies probabilistic neural network (PNN) and adaptive neuro fuzzy inference system (ANFIS) to develop bus service quality (SQ) prediction model based on the preferences stated by users (on a scale of 1 to 5). A questionnaire survey is conducted and a data set from the survey is prepared to develop the SQ prediction model using PNN and ANFIS. Results show that ANFIS produced better prediction than PNN. The research is further extended to include ranking of the SQ attributes according to their impact on the overall result from the developed model. Attributes such as punctuality and reliability, seat availability, and service frequency were found to be the top three attributes that mostly affect the decision making process of the users. This study can aid service providers in improving the most important attributes of bus service to develop the quality of service, thereby increasing transit ridership.

Application of Probabilistic Neural Network Technique in Identifying Low Porosity and Low Permeability Gas-Layers in Logging Data

2011 ◽  
Vol 58-60 ◽  
pp. 1712-1715 ◽  
Author(s):  
Chong Zhang ◽  
Guang Chao Pan
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Probabilistic Neural Network ◽  
Response Characteristic ◽  
Low Permeability ◽  
Well Testing ◽  
Data Logging ◽  
Fluid Property ◽  
Property Identification ◽  
Low Porosity

In view of poor Physical Properties,complex Pore Structure and high saturation of low porosity and low permeability gas layers ,in order to overcome the difficult of fluid property identification in low porosity and low permeability gas layers using conventional method, probabilistic neural network technique was Proposed.According to an example for low porosity and low permeability gas reservoil in Southwest China, Combined with well testing data,logging Response Characteristics of various fluid property layers were analyzed.According the correlation between logging Response Characteristic values and fluid property, PNN was trained and PNN prediction model was established. fluid property in the region layers were identified.The results showed that the PNN prediction model was very promising influid property identification.

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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