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A Review of Soft Computing Techniques and Applications
INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) ICRADL – 2021, 2021
Soft Computing can be defined as a science of thinking, reasoning that helps to deal with complex systems. Its main aim is to develop intelligent machines in order to solve realworld problems. It differs from the conventional hard computing as it can handle uncertainty, imprecision easily. It includes use of different techniques such as machine learning, artificial neural networks etc. that can be used together for solving complex problems that are difficult to tackle using conventional models of mathematics. These techniques play a vital role in identifying hidden patterns from the data and doing the classification for making intelligent decisions. This paper reviews some of the soft computing techniques and its applications.
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..
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.
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 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.
Soft Computing Techniques and Their Applications
Mathematical Models, Methods and Applications, 2015
The modern science is still striving to develop consciousness-based machine. The forecasting is an intuition-based or consciousness-based problem. It is an important problem for planning, decision-making and designing of an appropriate controller for the systems. The paper deals with the synergism of soft computing techniques mainly artificial neural network, fuzzy logic systems, and genetic algorithms and their applications in forecasting.
Soft Computing: Theories and Applications
Advances in Intelligent Systems and Computing
The series "Advances in Intelligent Systems and Computing" contains publications on theory, applications, and design methods of Intelligent Systems and Intelligent Computing. Virtually all disciplines such as engineering, natural sciences, computer and information science, ICT, economics, business, e-commerce, environment, healthcare, life science are covered. The list of topics spans all the areas of modern intelligent systems and computing such as: computational intelligence, soft computing including neural networks, fuzzy systems, evolutionary computing and the fusion of these paradigms, social intelligence, ambient intelligence, computational neuroscience, artificial life, virtual worlds and society, cognitive science and systems, Perception and Vision, DNA and immune based systems, self-organizing and adaptive systems, e-Learning and teaching, human-centered and human-centric computing, recommender systems, intelligent control, robotics and mechatronics including human-machine teaming, knowledge-based paradigms, learning paradigms, machine ethics, intelligent data analysis, knowledge management, intelligent agents, intelligent decision making and support, intelligent network security, trust management, interactive entertainment, Web intelligence and multimedia. The publications within "Advances in Intelligent Systems and Computing" are primarily proceedings of important conferences, symposia and congresses. They cover significant recent developments in the field, both of a foundational and applicable character. An important characteristic feature of the series is the short publication time and worldwide distribution. This permits a rapid and broad dissemination of research results.
Advances in Intelligent Systems and Computing, 2016
The purpose of this article is to provide an overview of soft computing applications in actuarial science. Soft computing (SC) refers to modes of computing in which precision is traded for tractability, robustness and ease of implementation. For the most part, SC encompasses the technologies of fuzzy logic, genetic algorithms, and neural networks, and it has emerged as an effective tool for dealing with control, modeling, and decision problems in complex systems. The paper ends with a general comment on the study. arc35_11_01a
SOFT COMPUTING AND ITS APPLICATIONS AS THE STATE-OF-THE-ART TECHNIQUE FOR CONTEMPORARY RESEARCH
IAEME PUBLICATION, 2020
Technological development in information system ensues because of hybrid intelligent systems in soft computing. Hybrid intelligent system is a kind of system which engages a blend of artificial intelligence subfield procedures and practices. Soft computing speaks about the confidence of computational techniques in various disciplines, which challenges in education, modeling, and investigating complex problems. High complexity soft computing applications have been brought as zero complexity due to the advancement of technological development in this era. This research article deals with the insight of soft computing branches, research applications and hybrid intelligent system that produces zero complexity which will create an inspiration to new researchers.