Soft Computing and its Applications

Data Mining ◽  
2013 ◽  
pp. 366-394
Author(s):  
Siddhartha Bhattacharyya ◽  
Ujjwal Maulik ◽  
Sanghamitra Bandyopadhyay
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Evolutionary Computation ◽  
Soft Computing ◽  
Real World ◽  
Industrial Applications ◽  
Approximation Techniques ◽  
Computing Paradigm ◽  
Numeric Data ◽  
Main Components

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 and its Applications

2010 ◽  
pp. 1-30 ◽  
Author(s):  
Siddhartha Bhattacharyya ◽  
Ujjwal Maulik ◽  
Sanghamitra Bandyopadhyay
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Evolutionary Computation ◽  
Soft Computing ◽  
Real World ◽  
Industrial Applications ◽  
Approximation Techniques ◽  
Computing Paradigm ◽  
Numeric Data ◽  
Main Components

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.

Hybrid Intelligent Diagnosis Approach Based On Neural Pattern Recognition and Fuzzy Decision-Making

2012 ◽  
pp. 444-466
Author(s):  
Amine Chohra ◽  
Nadia Kanaoui ◽  
Véronique Amarger ◽  
Kurosh Madani
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Pattern Recognition ◽  
Fuzzy Logic ◽  
Decision Support ◽  
Fault Diagnosis ◽  
Soft Computing ◽  
Industrial Applications ◽  
Suggested Approach ◽  
Intelligent Diagnosis

Fault diagnosis is a complex and fuzzy cognitive process, and soft computing methods and technologies based on Neural Networks (NN) and Fuzzy Logic (FL), have shown great potential in the development of Decision Support Systems (DSS). Dealing with expert (human) knowledge consideration, Computer Aided Diagnosis (CAD) dilemma is one of the most interesting, but also one of the most difficult problems. Among difficulties contributing to challenging nature of this problem, one can mention the need of fine pattern recognition (classification) and decision-making. This Chapter deals with classification and decision-making based on Artificial Intelligence using multiple model approaches under soft computing implying modular Neural Networks (NN) and Fuzzy Logic (FL) for biomedical and industrial applications. The aim of this Chapter is absolutely not to replace specialized human but to suggest decision support tools: hybrid intelligent diagnosis systems with a satisfactory reliability degree for CAD. In this Chapter, a methodology is given in order to design hybrid intelligent diagnosis systems for a large field of biomedical and industrial applications. For this purpose, first, a survey on diagnosis tasks in such applications is presented. Second, fault diagnosis systems are presented. Third, the main steps of hybrid intelligent diagnosis systems are developed, for each step emphasizing problems and suggesting solutions able to ensure the design of hybrid intelligent diagnosis systems with a satisfactory reliability degree. In fact, the main steps discussed are knowledge representation, classification, classifier issued information fusion, and decision-making. Then, the suggested approach is developed for a CAD in biomedicine, from Auditory Brainstem Response (ABR) test, and the prototype design and experimental results are presented. Finally, a discussion is given with regard to the reliability and large application field of the suggested approach.

Hybrid Intelligent Diagnosis Approach Based On Neural Pattern Recognition and Fuzzy Decision-Making

2011 ◽  
pp. 372-394
Author(s):  
Amine Chohra ◽  
Nadia Kanaoui ◽  
Véronique Amarger ◽  
Kurosh Madani
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Pattern Recognition ◽  
Fuzzy Logic ◽  
Decision Support ◽  
Fault Diagnosis ◽  
Soft Computing ◽  
Industrial Applications ◽  
Suggested Approach ◽  
Intelligent Diagnosis

Fault diagnosis is a complex and fuzzy cognitive process, and soft computing methods and technologies based on Neural Networks (NN) and Fuzzy Logic (FL), have shown great potential in the development of Decision Support Systems (DSS). Dealing with expert (human) knowledge consideration, Computer Aided Diagnosis (CAD) dilemma is one of the most interesting, but also one of the most difficult problems. Among difficulties contributing to challenging nature of this problem, one can mention the need of fine pattern recognition (classification) and decision-making. This Chapter deals with classification and decision-making based on Artificial Intelligence using multiple model approaches under soft computing implying modular Neural Networks (NN) and Fuzzy Logic (FL) for biomedical and industrial applications. The aim of this Chapter is absolutely not to replace specialized human but to suggest decision support tools: hybrid intelligent diagnosis systems with a satisfactory reliability degree for CAD. In this Chapter, a methodology is given in order to design hybrid intelligent diagnosis systems for a large field of biomedical and industrial applications. For this purpose, first, a survey on diagnosis tasks in such applications is presented. Second, fault diagnosis systems are presented. Third, the main steps of hybrid intelligent diagnosis systems are developed, for each step emphasizing problems and suggesting solutions able to ensure the design of hybrid intelligent diagnosis systems with a satisfactory reliability degree. In fact, the main steps discussed are knowledge representation, classification, classifier issued information fusion, and decision-making. Then, the suggested approach is developed for a CAD in biomedicine, from Auditory Brainstem Response (ABR) test, and the prototype design and experimental results are presented. Finally, a discussion is given with regard to the reliability and large application field of the suggested approach.

scholarly journals INDUSTRIAL APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS

2014 ◽  
pp. 8-20
Author(s):  
Kurosh Madani
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real World ◽  
Industrial Applications ◽  
Large Field ◽  
Nonlinear Functions ◽  
The Past ◽  
Real World Applications ◽  
Ann Models ◽  
Artificial Neural

In a large number of real world dilemmas and related applications the modeling of complex behavior is the central point. Over the past decades, new approaches based on Artificial Neural Networks (ANN) have been proposed to solve problems related to optimization, modeling, decision making, classification, data mining or nonlinear functions (behavior) approximation. Inspired from biological nervous systems and brain structure, Artificial Neural Networks could be seen as information processing systems, which allow elaboration of many original techniques covering a large field of applications. Among their most appealing properties, one can quote their learning and generalization capabilities. The main goal of this paper is to present, through some of main ANN models and based techniques, their real application capability in real world industrial dilemmas. Several examples through industrial and real world applications have been presented and discussed.

scholarly journals Towards Robustness in Neural Network Based Fault Diagnosis

2008 ◽  
Vol 18 (4) ◽  
pp. 443-454 ◽  
Author(s):  
Krzysztof Patan ◽  
Marcin Witczak ◽  
Józef Korbicz
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Soft Computing ◽  
Analytical Techniques ◽  
Industrial Applications ◽  
Nonlinear Behaviour ◽  
Design Problems ◽  
Modelling Uncertainty ◽  
Soft Computing Techniques

Towards Robustness in Neural Network Based Fault DiagnosisChallenging design problems arise regularly in modern fault diagnosis systems. Unfortunately, classical analytical techniques often cannot provide acceptable solutions to such difficult tasks. This explains why soft computing techniques such as neural networks become more and more popular in industrial applications of fault diagnosis. Taking into account the two crucial aspects, i.e., the nonlinear behaviour of the system being diagnosed as well as the robustness of a fault diagnosis scheme with respect to modelling uncertainty, two different neural network based schemes are described and carefully discussed. The final part of the paper presents an illustrative example regarding the modelling and fault diagnosis of a DC motor, which shows the performance of the proposed strategy.

SOFT COMPUTING: AN INTRODUCTION

2018 ◽  
Vol 8 (6) ◽  
pp. 63
Author(s):  
Matthew N. O. Sadiku ◽  
Yonghui Wang ◽  
Suxia Cui ◽  
Sarhan M. Musa
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Low Cost ◽  
Computational Techniques ◽  
Inaccurate Information ◽  
Major Benefit ◽  
Partial Truth ◽  
Human Decision

Soft computing (SC) is a newly emerging multidisciplinary field. It is a collection of computational techniques, such as expert systems, fuzzy logic, neural networks, and evolutionary algorithms, which provide information processing capabilities to solve complex practical problems. The major benefit of SC lies in its ability to tolerate imprecision, uncertainty, partial truth, and approximation in processing imprecise and inaccurate information and simulating human decision making at low cost. This paper provides a brief introduction on soft computing.

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