Development of intelligent models for ravelling using neural network

2005 ◽  
Author(s):  
M. Miradi
Keyword(s):  
Neural Network ◽  
Intelligent Models

Intelligent Models Applied to Elastic Modulus of Jointed Rock Mass

2018 ◽  
pp. 1-30 ◽  
Author(s):  
Jagan Jayabalan ◽  
Sanjiban Sekhar Roy ◽  
Pijush Samui ◽  
Pradeep Kurup
Keyword(s):  
Neural Network ◽  
Rock Mass ◽  
Elastic Modulus ◽  
Roughness Parameter ◽  
Jointed Rock ◽  
Jointed Rock Mass ◽  
Artificial Neural Network Ann ◽  
Learning Machine ◽  
Hidden Layer ◽  
Intelligent Models

Elastic Modulus (Ej) of jointed rock mass is a key parameter for deformation analysis of rock mass. This chapter adopts three intelligent models {Extreme Learning Machine (ELM), Minimax Probability Machine Regression (MPMR) and Generalized Regression Neural Network (GRNN)} for determination of Ej of jointed rock mass. MPMR is derived in a probability framework. ELM is the modified version of Single Hidden Layer Feed forward network. GRNN approximates any arbitrary function between the input and output variables. Joint frequency (Jn), joint inclination parameter (n), joint roughness parameter (r), confining pressure (s3) (MPa), and elastic modulus (Ei) (GPa) of intact rock have been taken as inputs of the ELM, GRNN and MPMR models. The output of ELM, GRNN and MPMR is Ej of jointed rock mass. In this study, ELM, GRNN and MPMR have been used as regression techniques. The developed GRNN, ELM and MPMR have been compared with the Artificial Neural Network (ANN) models.

scholarly journals Pruning Fuzzy Neural Network Applied to the Construction of Expert Systems to Aid in the Diagnosis of the Treatment of Cryotherapy and Immunotherapy

2019 ◽  
Vol 3 (2) ◽  
pp. 22 ◽  
Author(s):  
Augusto Junio Guimarães ◽  
Paulo Vitor de Campos Souza ◽  
Vinícius Jonathan Silva Araújo ◽  
Thiago Silva Rezende ◽  
Vanessa Souza Araújo
Keyword(s):  
Neural Network ◽  
Pattern Classification ◽  
Fuzzy Neural Network ◽  
Hybrid Approach ◽  
Hpv Infection ◽  
Current State ◽  
Fuzzy Neural ◽  
Classification Tasks ◽  
Intelligent Models ◽  
Excellent Tool

Human papillomavirus (HPV) infection is related to frequent cases of cervical cancer and genital condyloma in humans. Up to now, numerous methods have come into existence for the prevention and treatment of this disease. In this context, this paper aims to help predict the susceptibility of the patient to forms treatment using both cryotherapy and immunotherapy. These studies facilitate the choice of medications, which can be painful and embarrassing for patients who have warts on intimate parts. However, the use of intelligent models generates efficient results but does not allow a better interpretation of the results. To solve the problem, we present the method of a fuzzy neural network (FNN). A hybrid model capable of solving complex problems and extracting knowledge from the database will pruned through F-score techniques to perform pattern classification in the treatment of warts, and to produce a specialist system based on if/then rules, according to the experience obtained from the database collected through medical research. Finally, binary pattern-classification tests realized in the FNN and compared with other models commonly used for classification tasks capture results of greater accuracy than the current state of the art for this type of problem (84.32% for immunotherapy, and 88.64% for cryotherapy), and extract fuzzy rules from the problem database. It was found that the hybrid approach based on neural networks and fuzzy systems can be an excellent tool to aid the prediction of cryotherapy and immunotherapy treatments.

scholarly journals Half-hour global solar radiation forecasting based on static and dynamic multivariate neural networks

2021 ◽  
Vol 9 (2) ◽  
Author(s):  
Mohammed Ali Jallal ◽  
◽  
Samira Chabaa ◽  
Abdelouhab Zeroual ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Solar Radiation ◽  
Solar Energy ◽  
Meteorological Parameters ◽  
Energy Systems ◽  
Global Solar Radiation ◽  
Acquisition Time ◽  
Feedback Connection ◽  
Intelligent Models ◽  
The City

Precise global solar radiation (GSR) measurements in a given location are very essential for designing and supervising solar energy systems. In the case of rarity or absence of these measurements, it is important to have a theoretical or empirical model to compute the GSR values. Therefore, the main goal of this work is to offer, to designers and engineers of solar energy systems, an appropriate and accurate way to predict the half-hour global solar radiation (HHGSR) time series from some available meteorological parameters (relative humidity, air temperature, wind speed, precipitation, and acquisition time vector in half-hour scale). For that purpose, two intelligent models are developed: the first one is a multivariate dynamic neural network with feedback connection, and the second is a multivariate static neural network. The database used to build these models was recorded in Agdal’s meteorological station in Marrakesh, Morocco, during the years of 2013 and 2014, and it was divided into two subsets. The first subset is used for training and validating the models, and the second subset is used for testing the efficiency and the robustness of the developed models. The obtained results, in terms of the statistical performance indicators, demonstrate the efficiency of the developed forecasting models to accurately predict the HHGSR parameter in the city of Marrakesh, Morocco.

scholarly journals Implementation of Genetic Algorithm Integrated with the Deep Neural Network for Estimating at Completion Simulation

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Karrar Raoof Kareem Kamoona ◽  
Cenk Budayan
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Deep Neural Network ◽  
Optimization Algorithms ◽  
Support Vector ◽  
Project Cost ◽  
Final Project ◽  
Input Optimization ◽  
Intelligent Models ◽  
Intelligent Model

In construction project management, there are several factors influencing the final project cost. Among various approaches, estimate at completion (EAC) is an essential approach utilized for final project estimation. The main merit of EAC is including the probability of the project performance and risk. In addition, EAC is extremely helpful for project managers to define and determine the critical throughout the project progress and determine the appropriate solutions to these problems. In this research, a relatively new intelligent model called deep neural network (DNN) is proposed to calculate the EAC. The proposed DNN model is authenticated against one of the predominated intelligent models conducted on the EAC prediction, namely, support vector regression model (SVR). In order to demonstrate the capability of the model in the engineering applications, historical project information obtained from fifteen projects in Iraq region is inspected in this research. The second phase of this research is about the integration of two input algorithms hybridized with the proposed and the comparable predictive intelligent models. These input optimization algorithms are genetic algorithm (GA) and brute force algorithm (BF). The aim of integrating these input optimization algorithms is to approximate the input attributes and investigate the highly influenced factors on the calculation of EAC. Overall, the enthusiasm of this study is to provide a robust intelligent model that estimates the project cost accurately over the traditional methods. Also, the second aim is to introduce a reliable methodology that can provide efficient and effective project cost control. The proposed GA-DNN is demonstrated as a reliable and robust intelligence model for EAC calculation.

Intelligent Models to Predict the Prognosis of Premature Neonates According to Their EEG Signals

2020 ◽  
pp. 830-840
Author(s):  
Yasser Al Hajjar ◽  
Abd El Salam Ahmad Al Hajjar ◽  
Bassam Daya ◽  
Pierre Chauvet
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Decision Tree ◽  
Linear Discriminant ◽  
Premature Newborns ◽  
Intelligent Models ◽  
Intelligent Model ◽  
Electroencephalogram Eeg ◽  
The Brain

The aim of this paper is to find the best intelligent model that allows predicting the future of premature newborns according to their electroencephalogram (EEG). EEG is a signal that measures the electrical activity of the brain. In this paper, the authors used a dataset of 397 EEG records detected at birth of premature newborns and their classification by doctors two years later: normal, sick or risky. They executed machine learning on this dataset using several intelligent models such as multiple linear regression, linear discriminant analysis, artificial neural network and decision tree. They used 14 parameters concerning characteristics extracted from EEG records that affect the prognosis of the newborn. Then, they presented a complete comparative study between these models in order to find who gives best results. Finally, they found that decision tree gave best result with performance of 100% for sick records, 76.9% for risky and 69.1% for normal ones.

Control of the weld quality using welding parameters in a robotic welding process

2018 ◽  
Vol 1 (87) ◽  
pp. 32-40 ◽  
Author(s):  
M.-H. Park ◽  
B.-J. Jin ◽  
T.-J. Yun ◽  
J.-S. Son ◽  
C.-G. Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Arc Welding ◽  
Gma Welding ◽  
Welding Process ◽  
Welding Parameters ◽  
Automatic Welding ◽  
Welding Quality ◽  
Arc Welding Process ◽  
Intelligent Models ◽  
Welding System

Purpose: Since the welding automations have widely been required for industries and engineering, the development of the predicted model has become more important for the increased demands for the automatic welding systems where a poor welding quality becomes apparent if the welding parameters are not controlled. The automated welding system must be modelling and controlling the changes in weld characteristics and produced the output that is in some way related to the change being detected as welding quality. To be acceptable a weld quality must be positioned accurately with respect to the joints, have good appearance with sufficient penetration and reduce low porosity and inclusion content. Design/methodology/approach: To achieve the objectives, two intelligent models involving the use of a neural network algorithm in arc welding process with the help of a numerical analysis program MATLAB have been developed. Findings: The results represented that welding quality was fully capable of quantifying and qualifying the welding faults. Research limitations/implications: Welding parameters in the arc welding process should be well established and categorized for development of the automatic welding system. Furthermore, typical characteristics of welding quality are the bead geometry, composition, microstructure and appearance. However, an intelligent algorithm that predicts the optimal bead geometry and accomplishes the desired mechanical properties of the weldment in the robotic GMA (Gas Metal Arc) welding should be required. The developed algorithm should expand a wide range of material thicknesses and be applicable in all welding position for arc welding process. Furthermore, the model must be available in the form of mathematical equations for the automatic welding system. Practical implications: The neural network models which called BP (Back Propagation) and LM (Levenberg-Marquardt) neural networks to predict optimal welding parameters on the required bead reinforcement area in lab joint in the robotic GMA welding process have been developed. Experimental results have been employed to find the optimal algorithm to predict bead reinforcement area by BP and LM neural networks in lab joint in the robotic GMA welding. The developed intelligent models can be estimated the optimal welding parameters on the desired bead reinforcement area and weld criteria, establish guidelines and criteria for the most effective joint design for the robotic arc welding process. Originality/value: In this study, intelligent models, which employed the neural network algorithms, one of AI (Artificial Intelligence) technologies have been developed to study the effects of welding parameters on bead reinforcement area and to predict the optimal bead reinforcement area for lab joint in the robotic GMA welding process. BP (Back Propagation) and LM (Levenberg-Marquardt) neural network algorithm have been used to develop the intelligent model.

Thermal Resistance Modeling of Oscillating Heat Pipes for Nanofluids by Artificial Intelligence Approach

2019 ◽  
Vol 141 (7) ◽  
Author(s):  
M. Malekan ◽  
A. Khosravi ◽  
H. R. Goshayeshi ◽  
M. E. H. Assad ◽  
J. J. Garcia Pabon
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Thermal Resistance ◽  
Heat Pipe ◽  
Fuzzy Inference ◽  
Working Fluid ◽  
Group Method ◽  
Feed Forward Neural Network ◽  
Inference System ◽  
Intelligent Models

In this study, thermal resistance of a closed-loop oscillating heat pipe (OHP) is investigated using experimental tests and artificial intelligence methods. For this target, γFe2O3 and Fe3O4 nanoparticles are mixed with the base fluid. Also, intelligent models are developed to predict the thermal resistance of the OHP. These models are developed based on the heat input into evaporator section, the thermal conductivity of working fluids, and the ratio of the inner diameter to length of OHP. The intelligent methods are multilayer feed-forward neural network (MLFFNN), adaptive neuro-fuzzy inference system (ANFIS) and group method of data handling (GMDH) type neural network. Thermal resistance of the heat pipe (as a measure of thermal performance) is considered as the target. The results showed that using the nanofluids as working fluid in the OHP decreased the thermal resistance, where this decrease for Fe3O4/water nanofluid was more than that of γFe2O3/water. The intelligent models also predicted successfully the thermal resistance of OHP with a correlation coefficient close to 1. The root-mean-square error (RMSE) for MLFFNN, ANFIS, and GMDH models was obtained as 0.0508, 0.0556, and 0.0569 (°C/W) (for the test data), respectively.

scholarly journals An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1196
Author(s):  
Hossein Moayedi ◽  
Amir Mosavi
Keyword(s):  
Neural Network ◽  
Solar Energy ◽  
Trial And Error ◽  
Promising Tool ◽  
Shuffled Complex Evolution ◽  
Proposed Model ◽  
Shuffled Frog Leaping ◽  
Intelligent Models ◽  
Influential Parameters ◽  
Shed Light

Proper management of solar energy as an effective renewable source is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO), is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for nonlinearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development.

Bio-Inspired Computing through Artificial Neural Network

2017 ◽  
pp. 246-274
Author(s):  
Nilamadhab Dash ◽  
Rojalina Priyadarshini ◽  
Brojo Kishore Mishra ◽  
Rachita Misra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intelligent Systems ◽  
Deductive Reasoning ◽  
Real Life ◽  
Artificial Neural ◽  
Living Organisms ◽  
Artificial Neural Network Ann ◽  
Intelligent Models ◽  
Algorithmic Model

Developing suitable mathematical or algorithmic model to solve real life complex problems is one of the major challenges faced by the researchers especially those involved in the computer science field. To a large extent Computational intelligence has been found to be effective in designing such models. Bio inspired computing is the technique which makes the machines intelligent by adapting the behavior and methods exhibited by the human beings and other living organisms while forming intelligent systems. These intelligent models include the intelligent techniques such as Artificial Neural Network (ANN), evolutionary computation, swarm intelligence, fuzzy system, artificial immune system accompanied by fuzzy logic, expert system, deductive reasoning. All these together form the area of Bio inspired computing. The chapter deals with various bio inspired technique, giving emphasis on issues, development, advances and practical implementations of ANN.

Bio-Inspired Computing through Artificial Neural Network

Fuzzy Systems ◽  
2017 ◽  
pp. 1285-1313
Author(s):  
Nilamadhab Dash ◽  
Rojalina Priyadarshini ◽  
Brojo Kishore Mishra ◽  
Rachita Misra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intelligent Systems ◽  
Deductive Reasoning ◽  
Real Life ◽  
Artificial Neural ◽  
Living Organisms ◽  
Artificial Neural Network Ann ◽  
Intelligent Models ◽  
Algorithmic Model

Developing suitable mathematical or algorithmic model to solve real life complex problems is one of the major challenges faced by the researchers especially those involved in the computer science field. To a large extent Computational intelligence has been found to be effective in designing such models. Bio inspired computing is the technique which makes the machines intelligent by adapting the behavior and methods exhibited by the human beings and other living organisms while forming intelligent systems. These intelligent models include the intelligent techniques such as Artificial Neural Network (ANN), evolutionary computation, swarm intelligence, fuzzy system, artificial immune system accompanied by fuzzy logic, expert system, deductive reasoning. All these together form the area of Bio inspired computing. The chapter deals with various bio inspired technique, giving emphasis on issues, development, advances and practical implementations of ANN.

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