Optimal Design of Stiffened Composite Panel for Performance and Manufacturing Considerations

2000 ◽  
Author(s):  
J. Yoo ◽  
P. Hajela
Keyword(s):  
Soft Computing ◽  
Back Propagation ◽  
Analytical Description ◽  
Computing Methods ◽  
Composite Panel ◽  
Design Variables ◽  
Soft Computing Techniques ◽  
Soft Computing Methods ◽  
Function Approximations ◽  
Composite Wing

Abstract This paper describes a design study in which a stiffened composite wing panel is configured for a combination of performance and manufacturing related requirements. The principal focus of the paper resides in demonstrating the adaptation of newly emergent soft-computing methods for a variety of sub-tasks that constitute the design process. These sub-tasks include function approximations, modeling of processes that lack a good analytical description, and design optimization in a space that consists of a mix of integer, discrete, and continuous design variables. Soft computing techniques discussed in this context include function approximations using back-propagation neural networks, modeling of the composite panel fabrication process using evolutionary fuzzy models, and the application of genetic algorithms and immune network modeling to the optimization problem.

Prediction of The Uniaxial Compressive Strength of Rocks Materials

2018 ◽  
pp. 31-96 ◽  
Author(s):  
Nurcihan Ceryan ◽  
Nuray Korkmaz Can
Keyword(s):  
Soft Computing ◽  
Computing Methods ◽  
P Wave ◽  
Support Vector ◽  
Advantages And Disadvantages ◽  
Artifical Neural Network ◽  
P Wave Velocity ◽  
Soft Computing Techniques ◽  
Soft Computing Methods ◽  
Direct And Indirect Methods

This study briefly will review determining UCS including direct and indirect methods including regression model soft computing techniques such as fuzzy interface system (FIS), artifical neural network (ANN) and least sqeares support vector machine (LS-SVM). These has advantages and disadvantages of these methods were discussed in term predicting UCS of rock material. In addition, the applicability and capability of non-linear regression, FIS, ANN and LS-SVM SVM models for predicting the UCS of the magnatic rocks from east Pondite, NE Turkey were examined. In these soft computing methods, porosity and P-durability secon index defined based on P-wave velocity and slake durability were used as input parameters. According to results of the study, the performanc of LS-SVM models is the best among these soft computing methods suggested in this study.

A Review of Soft Computing Methods Application in Rock Mechanic Engineering

2016 ◽  
pp. 1-70 ◽  
Author(s):  
Nurcihan Ceryan
Keyword(s):  
Rock Mass ◽  
Soft Computing ◽  
Rock Mechanics ◽  
Computing Methods ◽  
Rock Material ◽  
Rock Slope Stability ◽  
Rock Mechanic ◽  
Natural Rock ◽  
Soft Computing Techniques ◽  
Soft Computing Methods

Engineering behavior of rock mass is controlled by many factors, related to its nature and the environmental conditions. Determining all the parameters, ranking their weights, and clarifying their relative effects are very difficult tasks to accomplish. To overcome these difficulties, many researchers have employed soft computing methods in rock mechanics engineering. The soft computing methods have taken an important role in rock mechanics, and their abilities to address uncertainties, insufficient information and ambiguous linguistic expressions stand out in treating complex natural rock mass. This chapter briefly will review the development of soft computing techniques in rock mechanics engineering, especially in predicting of rock engineering classification system and mechanical properties of rock material and rock mass, determination weathering degree of rock material, evolution of rock performance, blasting and, rock slope stability. In addition, the future of the development and application of soft computing in rock mechanics engineering is discussed.

A Comprehensive Literature Review on Nature-Inspired Soft Computing and Algorithms

2020 ◽  
pp. 1851-1885
Author(s):  
Bilal Ervural ◽  
Beyzanur Cayir Ervural ◽  
Cengiz Kahraman
Keyword(s):  
Soft Computing ◽  
Bat Algorithm ◽  
Computing Methods ◽  
Analytical Models ◽  
Grey Wolf Optimizer ◽  
Particle Swarm Algorithm ◽  
Bee Colony ◽  
Soft Computing Techniques ◽  
Soft Computing Methods ◽  
Firefly Optimization

Soft Computing techniques are capable of identifying uncertainty in data, determining imprecision of knowledge, and analyzing ill-defined complex problems. The nature of real world problems is generally complex and their common characteristic is uncertainty owing to the multidimensional structure. Analytical models are insufficient in managing all complexity to satisfy the decision makers' expectations. Under this viewpoint, soft computing provides significant flexibility and solution advantages. In this chapter, firstly, the major soft computing methods are classified and summarized. Then a comprehensive review of eight nature inspired – soft computing algorithms which are genetic algorithm, particle swarm algorithm, ant colony algorithms, artificial bee colony, firefly optimization, bat algorithm, cuckoo algorithm, and grey wolf optimizer algorithm are presented and analyzed under some determined subject headings (classification topics) in a detailed way. The survey findings are supported with charts, bar graphs and tables to be more understandable.

scholarly journals Comparison of six different soft computing methods in modeling evaporation in different climates

2016 ◽  
Author(s):  
Lunche Wang ◽  
Ozgur Kisi ◽  
Mohammad Zounemat-Kermani ◽  
Yiqun Gan
Keyword(s):  
Soft Computing ◽  
Computing Methods ◽  
Least Square ◽  
Support Vector ◽  
Climatic Data ◽  
Regression Methods ◽  
Soft Computing Techniques ◽  
Soft Computing Methods ◽  
Fuzzy Genetic ◽  
Different Climates

Abstract. Evaporation plays important roles in regional water resources management,terrestrial ecological process and regional climate change. This study investigated the abilities of six different soft computing methods, Multi-layer perceptron (MLP), generalized regression neural network (GRNN), fuzzy genetic (FG), least square support vector machine (LSSVM), multivariate adaptive regression spline (MARS), adaptive neuro-fuzzy inference systems with grid partition (ANFIS-GP), and two regression methods, multiple linear regression (MLR) and Stephens and Stewart model (SS) in predicting monthly Ep. Long-term climatic data at eight stations in different climates, air temperature (Ta), solar radiation (Rg), sunshine hours (Hs), relative humidity (RH) and wind speed (Ws) during 1961–2000 are used for model development and validation. The first part of applications focused on testing and comparing the model accuracies using different local input combinations. The results showed that the models have different accuracies in different climates and the MLP model performed superior to the other models in predicting monthly Ep at most stations, while GRNN model performed better in Tibetan Plateau. The accuracies of above models ranked as: MLP, GRNN, LSSVM, FG, ANFIS-GP, MARS and MLR. Generalized models were also developed and tested with data of eight stations. The overall results indicated that the soft computing techniques generally performed better than the regression methods, but MLR and SS models can be more preferred at some climatic zones instead of complex nonlinear models, for example, the BJ, CQ and HK stations.

scholarly journals Technical Approach in Text Mining for Stock Market Prediction: A Systematic Review

2018 ◽  
Vol 10 (2) ◽  
pp. 770 ◽  
Author(s):  
Mohammad Rabiul Islam ◽  
Imad Fakhri Al-Shaikhli ◽  
Rizal Bin Mohd Nor ◽  
Vijayakumar Varadarajan
Keyword(s):  
Text Mining ◽  
Empirical Research ◽  
Soft Computing ◽  
Computing Methods ◽  
Technical Approach ◽  
Meaningful Information ◽  
Soft Computing Techniques ◽  
Textual Data ◽  
Methods And Techniques ◽  
Soft Computing Methods

Text mining methods and techniques have disclosed the mining task throughout information retrieval discipline in the field of soft computing techniques. To find the meaningful information from the vast amount of electronic textual data become a humongous task for trading decision. This empirical research of text mining role on financial text analysing in where stock predictive model need to improve based on rank search method. The review of this paper basically focused on text mining techniques, methods and principle component analysis that help reduce the dimensionality within the characteristics and optimal features. Moreover, most sophisticated soft-computing methods and techniques are reviewed in terms of analysis, comparison and evaluation for its performance based on electronic textual data. Due to research significance, this empirical research also highlights the limitation of different strategies and methods on exact aspects of theoretical framework for enhancing of performance.

A Comprehensive Literature Review on Nature-Inspired Soft Computing and Algorithms

2017 ◽  
pp. 34-68
Author(s):  
Bilal Ervural ◽  
Beyzanur Cayir Ervural ◽  
Cengiz Kahraman
Keyword(s):  
Soft Computing ◽  
Bat Algorithm ◽  
Computing Methods ◽  
Analytical Models ◽  
Grey Wolf Optimizer ◽  
Particle Swarm Algorithm ◽  
Bee Colony ◽  
Soft Computing Techniques ◽  
Soft Computing Methods ◽  
Firefly Optimization

Soft Computing techniques are capable of identifying uncertainty in data, determining imprecision of knowledge, and analyzing ill-defined complex problems. The nature of real world problems is generally complex and their common characteristic is uncertainty owing to the multidimensional structure. Analytical models are insufficient in managing all complexity to satisfy the decision makers' expectations. Under this viewpoint, soft computing provides significant flexibility and solution advantages. In this chapter, firstly, the major soft computing methods are classified and summarized. Then a comprehensive review of eight nature inspired – soft computing algorithms which are genetic algorithm, particle swarm algorithm, ant colony algorithms, artificial bee colony, firefly optimization, bat algorithm, cuckoo algorithm, and grey wolf optimizer algorithm are presented and analyzed under some determined subject headings (classification topics) in a detailed way. The survey findings are supported with charts, bar graphs and tables to be more understandable.

scholarly journals Interaction of hydro-socio-technology-knowledge indicators in integrated water resources management using soft-computing techniques

2020 ◽  
Author(s):  
Masoumeh Zeinali ◽  
Sarvin Zamanzad-Ghavidel ◽  
Yaser Mehri ◽  
Hazi Mohammad Azamathulla
Keyword(s):  
Water Resources ◽  
Soft Computing ◽  
Absolute Error ◽  
Computing Methods ◽  
Coefficient Of Determination ◽  
Group Method ◽  
Technology Knowledge ◽  
Soft Computing Techniques ◽  
The World ◽  
Soft Computing Methods

Abstract Various factors affect the development of social, cultural, and economic aspects of societies. One of these factors is the state of water resources. In this study, countries of the world with decreasing renewable water per capita were examined during the period 2005–2017. Specifically, 35, 5, 20, 48, 43, and 151 countries were selected from the American, Oceania, European, African, Asian continents, and the world respectively. Further, three hydro-socio-technology-knowledge indicators associated with demographic, technology, and knowledge dimensions were estimated with soft-computing methods (i.e. Group Method of Data Handling (GMDH), Radial Basis Function (RBF), and Regression Trees (R Trees)) for the world's continents). The GMDH model's performance was the best among the other soft-computing methods in estimating the hydro-socio-technology-knowledge indicators for all the world's continents based on statistical criteria (coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE)). The values of RMSE for GMDH models for the ratio of rural to urban population (PRUP), population density (PD), number of internet users (IU) and education index (EI) indicators equaled (0.291, 0.046, 0.127, 0.199), (0.094, 0.023, 0.174, 0.137), (0.237, 0.044, 0.166, 0.225), (0.173, 0.031, 0.126, 0.163), (0.218, 0.058, 0.142, 0.196) and (0.231, 0.049, 0.167, 0.195) for America, Oceania, Europe, Africa, Asia and the world, respectively. The results indicate that there is an interaction between socio-technology-knowledge indicators. Thus, for water resources in all continents and the world, the hydro-socio-technology-knowledge indicators can be used for proper planning and management of water resources.

A Soft Computing Overview

2010 ◽  
pp. 1-11
Author(s):  
Marcos Gestal ◽  
Daniel Rivero
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Soft Computing ◽  
Computing Methods ◽  
Testing System ◽  
Soft Computing Techniques ◽  
Soft Computing Methods ◽  
Computation Algorithms ◽  
Brain Behavior ◽  
The Brain

Nature has proved to be the best testing system, where we can analyze the effectiveness of any method of solving problems. It provides one of the most complex problems to be resolved: the survival. Analyzing how the species behave to achieve that survival, soft computing methods try to mimic this behavior to provide meaningful solutions to diverse problems. This chapter offers an introduction the fundamentals that the different soft computing techniques translate from Nature. It includes an approach of the brain behavior (Artificial Neural Networks) or the evolution ideas taken from Darwin’ laws (Evolutionary Computation algorithms).

scholarly journals Comparison of hard limiting and soft computing methods for predicting software effort estimation: In reference to Small Scale Visualization Projects

2018 ◽  
Vol 7 (4.6) ◽  
pp. 294
Author(s):  
T. M Kiran Kumar ◽  
M. A. Jayaram
Keyword(s):  
Soft Computing ◽  
Computing Methods ◽  
Small Scale ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Grade Point ◽  
Error Matrix ◽  
Prototype Development ◽  
Soft Computing Techniques ◽  
Soft Computing Methods

It is a well known fact that software effort estimation is exceptionally critical in every software industry, particular during the development of projects. It is hard to estimate the parameters involved due to ambiguity and uncertainty associated with the parameters. It is exactly here the hard limiting techniques, soft computing techniques comes to play. In this unique circumstance, this paper, presents an attempt to that compare the two paradigms for effort estimation. For this, we have considered fifty real time small visualization projects thrive by post graduate students.  The prototype development involves following stages:         i)            Elicitation of seven novel parameters namely Lines of Code, Cumulative Grade Point Average, New and changed code, Reused code, Cyclomatic Complexity, Algorithmic Complexity and Functional Points.       ii)            Developing of hard limiting methods and soft computing methods for prediction of software effort involved in terms of duration in minutes.For the validation of the models error metrics namely: Mean Absolute Error (MAE), Mean Magnitude of Relative Error (MMRE), Mean of Magnitude of error Relative to the Estimate (MMER) and Root Mean Square Error (RMSE) have been used. The result showed that the models compared very well with marginal difference in terms of predict values of error matrix. 

scholarly journals A Review of Soft Computing Methods Application in Rock Mechanic Engineering

2016 ◽  
pp. 606-673
Author(s):  
Nurcihan Ceryan
Keyword(s):  
Rock Mass ◽  
Soft Computing ◽  
Rock Mechanics ◽  
Computing Methods ◽  
Rock Material ◽  
Rock Slope Stability ◽  
Rock Mechanic ◽  
Natural Rock ◽  
Soft Computing Techniques ◽  
Soft Computing Methods

Engineering behavior of rock mass is controlled by many factors, related to its nature and the environmental conditions. Determining all the parameters, ranking their weights, and clarifying their relative effects are very difficult tasks to accomplish. To overcome these difficulties, many researchers have employed soft computing methods in rock mechanics engineering. The soft computing methods have taken an important role in rock mechanics, and their abilities to address uncertainties, insufficient information and ambiguous linguistic expressions stand out in treating complex natural rock mass. This chapter briefly will review the development of soft computing techniques in rock mechanics engineering, especially in predicting of rock engineering classification system and mechanical properties of rock material and rock mass, determination weathering degree of rock material, evolution of rock performance, blasting and, rock slope stability. In addition, the future of the development and application of soft computing in rock mechanics engineering is discussed.

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