Recent Trends in Soft Computing Techniques for Solving Real Time Engineering Problems (original) (raw)
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Proceedings of the 11th INDIACom; INDIACom-2017; IEEE Conference ID: 40353, 2017 4th International Conference on “Computing for Sustainable Global Development”, ISSN 0973-7529; ISBN 978-93-80544-24-3, Pages 706–711, 2017
Now a day’s, well known accepted soft computing methods likes as neural sets and fuzzy sets, adaptive evolutionary computing and neuro fuzzy based system, and Intelligence Computing’s etc. for carrying out numerical varying data simulation analysis. These approaches become applied on different engineering problems as independently. In this research paper many introducer and researchers describe the soft computing review technique which using for solving real domains problems and simulate the reappearance of the intelligence, as adaptation and learning for real times engineering problems... The intelligence computing Computational intelligence is a universal approximate, and it has the great function of non-linear mapping and optimization techniques. Also we discuss a brief of intelligent systems in Neural Network. And discuss it features and constituents of real problems. We concentrate on solving real domain problems in Neural Network of Intelligent System as Intelligence computing and soft computing
Soft computing and its applications
Fuzzy Sets and Systems, 2004
Soft Computing is a relatively new computing paradigm bestowed with tools and techniques for handling real world problems. The main components of this computing paradigm are neural networks, fuzzy logic and evolutionary computation. Each and every component of the soft computing paradigm operates either independently or in coalition with the other components for addressing problems related to modeling, analysis and processing of data. An overview of the essentials and applications of the soft computing paradigm is presented in this chapter with reference to the functionalities and operations of its constituent components. Neural networks are made up of interconnected processing nodes/neurons, which operate on numeric data. These networks posses the capabilities of adaptation and approximation. The varied amount of uncertainty and ambiguity in real world data are handled in a linguistic framework by means of fuzzy sets and fuzzy logic. Hence, this component is efficient in understanding vagueness and imprecision in real world knowledge bases. Genetic algorithms, simulated annealing algorithm and ant colony optimization algorithm are representative evolutionary computation techniques, which are efficient in deducing an optimum solution to a problem, thanks to the inherent exhaustive search methodologies adopted. Of late, rough sets have evolved to improve upon the performances of either of these components by way of approximation techniques. These soft computing techniques have been put to use in wide variety of problems ranging from scientific to industrial applications. Notable among these applications include image processing, pattern recognition, Kansei information processing, data mining, web intelligence etc.
APPLICATIONS OF SOFT COMPUTING IN VARIOUS AREAS
Soft Computing is the study of science of reasoning, thinking, analyzing and detecting that correlates the real world problems to the biological inspired methods. Soft Computing is the big motivation behind the idea of conceptual intelligence in machines. As such, it is an extension of heuristics and solve complex problems that too difficult to model mathematically. Soft Computing is tolerant of impression; uncertainty and approximation which is differ from hand computing. Soft Computing enumerates techniques like ANN, Evolutionary computing, Fuzzy Logic and statistics, they are advantageous and separately applied techniques but when used together solve complex problems very easily. This paper highlights various soft computing techniques and emerging fields of soft computing where they successfully applied.
Soft Computing Techniques in Various Areas
2020
Soft computing is a study of the science of logic, thinking, analysis and research that combines real-world problems with biologically inspired methods. Soft computing is the main motivation behind the idea of conceptual intelligence in machines. As such, it is an extension of heuristics and the resolution of complex problems that are very difficult to model mathematically. Smooth computing tolerates printing; uncertainty and approximation that differ from manual calculation. Soft Computing enumerates techniques like ANN, Evolutionary computing, Fuzzy Logic and statistics, they are advantageous and separately applied techniques which are used together to solve problems which are complex, very easily. This article highlights the various soft computing ting techniques and emerging areas of soft computing ting where they have been successfully implemented.
Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications
1998
The essence of soft computing is that, unlike the traditional, hard computing, it is aimed at an accommodation with the pervasive imprecision of the real world. Thus, the guiding principle of soft computing is: ' ... exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness, low solution cost and better rapport with reality.' In the final analysis, the role model for soft computing is the human mind. Soft computing is not a single methodology. Rather, it is a partnership. The principal partners at this juncture are fuzzy logic, neurocomputing, genetic computing and probabilistic computing, with the latter subsuming chaotic systems, belief networks and parts of learning theory. In coming years, the ubiquity of intelligent systems is certain to have a profound impact on the ways in which man-made intelligent systems are conceived, designed, manufactured, employed and interacted with. It is in this perspective that the basic issues relating to soft computing and intelligent systems are addressed in this paper.
Taxonomy of Adaptive Neuro-Fuzzy Inference System in Modern Engineering Sciences
Computational Intelligence and Neuroscience
Adaptive Neuro-Fuzzy Inference System (ANFIS) blends advantages of both Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) in a single framework. It provides accelerated learning capacity and adaptive interpretation capabilities to model complex patterns and apprehends nonlinear relationships. ANFIS has been applied and practiced in various domains and provided solutions to commonly recurring problems with improved time and space complexity. Standard ANFIS has certain limitations such as high computational expense, loss of interpretability in larger inputs, curse of dimensionality, and selection of appropriate membership functions. This paper summarizes that the standard ANFIS is unsuitable for complex human tasks that require precise handling of machines and systems. The state-of-the-art and practice research questions have been discussed, which primarily focus on the applicability of ANFIS in the diversifying field of engineering sciences. We conclude that the standard ANFIS a...
Soft Computing Techniques and itsApplications
Soft computing, unlike traditional computing, fits prediction models and provides answers to real complex problems. Unlike hard computing, soft computing tolerates great, uncertainty, partial truth and prediction. In fact, the model for soft computing is the human mind. Technologies such as fuzzy logic, genetic algorithms, artificial neural networks, machine learning and expert systems are part of soft computing. The theory of soft computing, introduced in the 1980s, is now a major area of research in all areas. Because of its low cost and high performance, soft computing is now successfully used in many home, commercial and industrial applications. The field of application of soft computing is increasing today. This article outlines the current state of Soft computing technology and explains the latter advantages and disadvantages to traditional computing technology..
Future Generation Computer Systems, 2005
The complexity and uncertainty inherent in heterogeneous, multidisciplinary, large-scale systems call for methods and strategies that allow for synergy in softcomputing techniques, such as fuzzy logic, artificial neural networks and evolutionary computing. Likewise, well-established classic algorithms for modelling and simulation require deep refinement and improvement if they are to meet new computational requirements. Research on the above-mentioned topics is key to guaranteeing success in current application areas. This special section is inspired by "Soft-Computing: Systems and Applications", a series of sessions held at the ICCS'02 conference in Amsterdam in April 2002, and by "New Algorithmic Approaches to Existing Application Areas", a session held at the ICCS'03 conference in St. Petersburg in June 2003. This special section consists of 12 papers describing some recent work in the area, addressing classic and soft-computing approaches for dealing with complex systems in a wide range of disciplines. The first part of the special section consists of five papers addressing the utilisation of new approaches for modelling, the improvement of classic methods and the application of well-established methods in quite a diverse range of application areas, such as volcanic processes, genetic information, passive microwave systems and river systems. Cellular automata (CA) are an alternative approach to differential equations for modelling complex systems. Indeed, this approach can be used for modelling as well as for simulating complex fluid dynamic systems whose evolution depends on the local rela
A Study of Neuro-fuzzy System in Approximation-based Problems
Mathematika, 2008
The Fusion of Artificial Neural Networks (ANN) and Fuzzy Inference System (FIS) has attracted a growing interest of researchers in various scientific and engineering areas due to the growing need for adaptive intelligent systems to solve real world problems. ANN learns by adjusting the interconnections between layers. FIS is a popular computing framework based on the concept of fuzzy set theory and fuzzy if-then rules. The advantages of the combination of ANN and FIS are apparent. This paper implements a hybrid neuro-fuzzy system underlying ANFIS (Adaptive Neuro Fuzzy Inference System), a fuzzy inference system implemented in the framework of neural networks. The motivation stems from a desire to achieve performance in terms of accuracy and several simulations studies regarding the determination of the optimal number of membership functions have been done. In our simulations, we utilize the ANFIS architecture to model nonlinear functions. In addition, the effects of using different ...