Effect of Training Sample and Network Characteristics in Neural Network-Based Real Property Value Prediction

2018 ◽  
pp. 303-313 ◽  
Author(s):  
Sayali Sandbhor ◽  
N. B. Chaphalkar
Keyword(s):  
Neural Network ◽  
Training Sample ◽  
Property Value ◽  
Real Property ◽  
Value Prediction ◽  
Network Characteristics

Real Property Value Prediction Capability Using Fuzzy Logic and ANFIS

2021 ◽  
Author(s):  
Ankita Kamire ◽  
Nitin Chaphalkar ◽  
Sayali Sandbhor
Keyword(s):  
Fuzzy Logic ◽  
Property Value ◽  
Real Property ◽  
Value Prediction ◽  
Prediction Capability

Analysis of geodetic and legal documentation in the process of expropriation for roads. Krakow case study

2013 ◽  
Vol 62 (1) ◽  
pp. 67-84
Author(s):  
Anna Trembecka
Keyword(s):  
Local Government ◽  
Legal Status ◽  
The State ◽  
Property Value ◽  
Real Property ◽  
Construction Design ◽  
Special Rules ◽  
The City

Abstract Amendment to the Act on special rules of preparation and implementation of investment in public roads resulted in an accelerated mode of acquisition of land for the development of roads. The decision to authorize the execution of road investment issued on its basis has several effects, i.e. determines the location of a road, approves surveying division, approves construction design and also results in acquisition of a real property by virtue of law by the State Treasury or local government unit, among others. The conducted study revealed that over 3 years, in this mode, the city of Krakow has acquired 31 hectares of land intended for the implementation of road investments. Compensation is determined in separate proceedings based on an appraisal study estimating property value, often at a distant time after the loss of land by the owner. One reason for the lengthy compensation proceedings is challenging the proposed amount of compensation, unregulated legal status of the property as well as imprecise legislation. It is important to properly develop geodetic and legal documentation which accompanies the application for issuance of the decision and is also used in compensation proceedings.

scholarly journals Combine and conquer: event reconstruction with Bayesian Ensemble Neural Networks

2021 ◽  
Vol 2021 (4) ◽  
Author(s):  
Jack Y. Araz ◽  
Michael Spannowsky
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Top Quark ◽  
Feature Space ◽  
Training Sample ◽  
Bayesian Techniques ◽  
Training Sample Size ◽  
Event Reconstruction ◽  
Neural Network Classifiers ◽  
Quark Jets

Abstract Ensemble learning is a technique where multiple component learners are combined through a protocol. We propose an Ensemble Neural Network (ENN) that uses the combined latent-feature space of multiple neural network classifiers to improve the representation of the network hypothesis. We apply this approach to construct an ENN from Convolutional and Recurrent Neural Networks to discriminate top-quark jets from QCD jets. Such ENN provides the flexibility to improve the classification beyond simple prediction combining methods by linking different sources of error correlations, hence improving the representation between data and hypothesis. In combination with Bayesian techniques, we show that it can reduce epistemic uncertainties and the entropy of the hypothesis by simultaneously exploiting various kinematic correlations of the system, which also makes the network less susceptible to a limitation in training sample size.

Assessment of the Underground Water Contaminated by the Leachate of Waste Dump of Open Pit Coal Mine Based on RBF Neural Network

2012 ◽  
Vol 599 ◽  
pp. 272-277 ◽  
Author(s):  
Zhi Bin Liu ◽  
Xiao Wei Yang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Coal Mine ◽  
Comprehensive Evaluation ◽  
Rbf Neural Network ◽  
Training Sample ◽  
Underground Water ◽  
Open Pit ◽  
Waste Dump

This paper used RBF artificial neural network to evaluate the underground water contaminated by the leachate of waste dump of open pit coal mine of Xinqiu in Fuxin. Firstly, with the advantages of neural network method in dealing with nonlinear problem, the RBF neural network model was built. Then, the normalized standard matrix was taken as training sample and the MATLAB software was used to train the training sample. Finally, the monitoring data were taken as test samples and were inputted in the RBF neural network model to evaluate the groundwater quality of study area. At the same time, the concept of degree of membership was adopted in the result making it more objective and accurate. The result shows that the ground water of this mining is seriously polluted, class of its pollution is Ⅳ-Ⅴ.The method with strong classification function and reliable evaluation results is simple and effective, and can be widely applied in all kinds of water resources comprehensive evaluation.

scholarly journals A Detection algorithm based on Convolutional Neural Network

2018 ◽  
Author(s):  
Fei Rong ◽  
Li Shasha ◽  
Xu Qingzheng ◽  
Liu Kun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Training Model ◽  
Test Sample ◽  
Training Sample ◽  
Detection Algorithm ◽  
Small Scale ◽  
Detection Model ◽  
Sample Data ◽  
Ratio Change

The Station logo is a way for a TV station to claim copyright, which can realize the analysis and understanding of the video by the identification of the station logo, so as to ensure that the broadcasted TV signal will not be illegally interfered. In this paper, we design a station logo detection method based on Convolutional Neural Network by the characteristics of the station, such as small scale-to-height ratio change and relatively fixed position. Firstly, in order to realize the preprocessing and feature extraction of the station data, the video samples are collected, filtered, framed, labeled and processed. Then, the training sample data and the test sample data are divided proportionally to train the station detection model. Finally, the sample is tested to evaluate the effect of the training model in practice. The simulation experiments prove its validity.

Research on the Installation and Alignment Method of Ship Multi-support Bearings Based on Different Confidence-Level Training Samples

2021 ◽  
pp. 1-9
Author(s):  
Yibin Deng ◽  
Xiaogang Yang ◽  
Shidong Fan ◽  
Hao Jin ◽  
Tao Su ◽  
...  
Keyword(s):  
Neural Network ◽  
Confidence Level ◽  
Rapid Development ◽  
Maximum Error ◽  
Training Sample ◽  
Measured Data ◽  
Engine Room ◽  
Training Samples ◽  
Force Coupling ◽  
Level Training

Because of the long propulsion shafting of special ships, the number of bearings is large and the number of measured bearing reaction data is small, which makes the installation of shafting difficult. To apply a small amount of measured data to the process of ship installation so as to accurately calculate the displacement value in the actual installation, this article proposes a method to calculate the displacement value of shafting intermediate bearing based on different confidence-level training samples. Taking a ro-ro ship as the research object, this research simulates the actual installation process, gives a higher confidence level to a small amount of measured data, constructs a new training sample set for machine learning, and finally obtains the genetic algorithm-backpropagation(GABP) neural network reflecting the actual installation process. At the same time, this research compares the accuracy between different confidence-level training sample shafting neural network and the shafting neural network without measured data, and the results show that the accuracy of shafting neural network with different confidence-level training samples is higher. Although as the adjustment times and the number of measured data increase, the network accuracy is significantly improved. After adding four measured data, the maximum error is within 1%, which can play a guiding role in the ship propulsion shafting alignment. Introduction With the rapid development of science and technology in the world, special ships such as engineering ships, official ships, and warships play an important role (Carrasco et al. 2020; Prill et al. 2020). Some ships of this special type are limited by various factors such as the stern line of engine room, hull stability, and operation requirements. They usually adopt the layout of middle or front engine room, which causes the propulsion system to have a longer shaft and the number of intermediate shafts and intermediate bearings exceeds two. This forms a so-called multisupport shafting (Lee et al. 2019) and it increases the difficulty of shafting alignment because of the force-coupling between the bearings (Lai et al. 2018a, 2018b). The process of the existing methods for calculating the displacement value is complex, and because of the influence of installation error and other factors, it is necessary to adjust the bearing height several times to make the bearing reaction meet the specification requirements(Kim et al. 2017, Ko et al. 2017). So how to predict the accurate displacement value of each intermediate bearing is the key to solving the problem of multisupport shafting intermediate bearing installation and calibration (Zhou et al. 2005, Xiao-fei et al. 2017).

Prediction and analysis of compressive strength of recycled aggregate thermal insulation concrete based on GA-BP optimization network

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jinsong Tu ◽  
Yuanzhen Liu ◽  
Ming Zhou ◽  
Ruixia Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Compressive Strength ◽  
Thermal Insulation ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Training Sample ◽  
Recycled Aggregate ◽  
Generalization Performance ◽  
Content Type

Purpose This paper aims to predict the 28-day compressive strength of recycled thermal insulation concrete more accurately. Design/methodology/approach The initial weights and thresholds of BP neural network are improved by genetic algorithm on MATLAB 2014 a platform. Findings Genetic algorithm–back propagation (GA-BP) neural network is more stable. The generalization performance of the complex is better. Originality/value The GA-BP neural network based on the training sample data can better realize the strength prediction of recycled aggregate thermal insulation concrete and reduce the complex orthogonal experimental process. GA-BP neural network is more stable. The generalization performance of the complex is better.

Ways to Obtain Training Sample in Inversible System of Neural Network

2013 ◽  
Vol 373-375 ◽  
pp. 1135-1138
Author(s):  
Shao Song Wan ◽  
Jian Cao
Keyword(s):  
Neural Network ◽  
Training Sample ◽  
Training Structure ◽  
Selection Of

This paper discusses training structure and procedure about inversible system of neural network. Subsequently, selection of training sample is focused on. Finally, the paper proposes some principles and ways to obtain training sample of inversible system.

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