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What is Soft Computing

Chapter 43
Handbook of Research on ICTs and Management Systems for Improving Efficiency in Healthcare and Social Care
Soft computing encompasses a set of computational techniques and algorithms that are used to deal with complex systems. Soft computing exploits the given tolerance for imprecision, partial truth, and uncertainty for a particular problem.
Published in Chapter:
Soft Methods for Automatic Drug Infusion in Medical Care Environment
Filipe Quinaz (University of Beira Interior, Portugal), Paulo Fazendeiro (University of Beira Interior, Portugal & Portuguese Telecommunications Institute (IT), Portugal), Miguel Castelo-Branco (University of Beira Interior, Portugal), and Pedro Araújo (University of Beira Interior, Portugal & Portuguese Telecommunications Institute (IT), Portugal)
Copyright: © 2013 |Pages: 25
DOI: 10.4018/978-1-4666-3990-4.ch043
Abstract
The automatic drug infusion in medical care environment remains an elusive goal due to the inherent specificities of the biological systems under control and to subtle shortcomings of the current models. The central aim of this chapter is to present an overview of soft computing techniques and systems that can be used to ameliorate those problems. The applications of control systems in modern medicine are discussed along with several enabling methodologies. The advantages and limitations of automatic drug infusion systems are analyzed. In order to comprehend the evolution of these systems and identify recent advances and research trends, a survey on the hypertension control problem is provided. For illustration, a state-of-the-art automatic drug infusion controller of Sodium Nitroprusside for the mean arterial pressure is described in detail. The chapter ends with final remarks on future research directions towards a fully automated drug infusion system.
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More Results
Chapter 43
Soft Methods for Automatic Drug Infusion in Medical Care Environment
Soft computing encompasses a set of computational techniques and algorithms that are used to deal with complex systems. Soft computing exploits the given tolerance for imprecision, partial truth, and uncertainty for a particular problem.
Published in Chapter: Soft Methods for Automatic Drug Infusion in Medical Care Environment; From: Handbook of Research on ICTs and Management Systems for Improving Efficiency in Healthcare and Social Care
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Chapter 82
A Fuzzy Multi-Agent System for Combinatorial Optimization
Collection of new computational techniques in computer science, artificial intelligence, machine learning, and many applied and engineering areas whose role model is the human mind and his guiding principle is exploit the tolerance for imprecision, uncertainty, partial truth, and approximation to achieve tractability, robustness, and low solution cost (Li, 1998; Verdegay, 2005; Zadeh, 1994)
Published in Chapter: A Fuzzy Multi-Agent System for Combinatorial Optimization; From: Encyclopedia of Networked and Virtual Organizations
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Chapter 8
Churn Management of E-Banking Customers by Fuzzy AHP
It is the hybrid combination of algorithms that were designed to model and enable solutions to real world problems without using complex mathematical solutions.
Published in Chapter: Churn Management of E-Banking Customers by Fuzzy AHP; From: Handbook of Research on Financial and Banking Crisis Prediction through Early Warning Systems
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Chapter 10
The Use of Soft Computing in Management
A term applied to a field within computer science which is characterized by the use of inexact solutions to computationally-hard tasks.
Published in Chapter: The Use of Soft Computing in Management; From: Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications
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Chapter 12
A Survey on Different Digital Authentication Systems Using Fuzzy Logic
Soft Computing is the way of solution of NP hard problems through artificial intelligence.
Published in Chapter: A Survey on Different Digital Authentication Systems Using Fuzzy Logic; From: Handbook of Research on Biomimicry in Information Retrieval and Knowledge Management
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Chapter 128
A Hybrid System for Automatic Infant Cry Recognition II
A partnership of techniques which in combination are tolerant of imprecision, uncertainty, partial truth, and approximation, and whose role model is the human mind. Its principal constituents are Fuzzy Logic (FL), Neural Computing (NC), Evolutionary Computation (EC) Machine Learning (ML) and Probabilistic Reasoning (PR)
Published in Chapter: A Hybrid System for Automatic Infant Cry Recognition II; From: Encyclopedia of Artificial Intelligence
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Chapter 333
Intelligent Information Systems
An older term for Computational Intelligence (see above).
Published in Chapter: Intelligent Information Systems; From: Encyclopedia of Information Science and Technology, Second Edition
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Chapter 28
Distributed Learning Algorithm Applications to the Scheduling of Wireless Sensor Networks
Is a field of computer science which is characterized by the use of inexact solutions to computationally hard tasks such as the NP-complete problems.
Published in Chapter: Distributed Learning Algorithm Applications to the Scheduling of Wireless Sensor Networks; From: Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications
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Chapter 2
An Intelligent Process Development Using Fusion of Genetic Algorithm with Fuzzy Logic
A set of artificial intelligence techniques provides efficient and feasible solutions in comparison with conventional computing. These techniques are also known as computational intelligence. They are basically integrated techniques to find solutions for the problems which are highly complex, ill- defined and difficult to model. Real world problems deal with imprecision and uncertainty can be easily handled using such techniques. Soft computing provides set of techniques which are hybridized and finally useful for designing intelligent systems.
Published in Chapter: An Intelligent Process Development Using Fusion of Genetic Algorithm with Fuzzy Logic; From: Handbook of Research on Artificial Intelligence Techniques and Algorithms
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Chapter 115
Granular Computing
In contrast to “hard computing” soft computing is collection of methods (fuzzy sets, rough sets neutral nets etc.) for dealing with ambiguous situations like imprecision, uncertainty, e.g. human expressions like “high profit at reasonable risks”. The objective of applying soft computing is to obtain robust solutions at reasonable costs.
Published in Chapter: Granular Computing; From: Encyclopedia of Artificial Intelligence
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Chapter 7
Application of Soft Computing Techniques for Renewable Energy Network Design and Optimization
It is a term applied to a field within computer science which is characterized by the use of inexact solutions to computationally hard tasks such as the solution of NP-complete problems, for which there is no known algorithm that can compute an exact solution in polynomial time.
Published in Chapter: Application of Soft Computing Techniques for Renewable Energy Network Design and Optimization; From: Handbook of Research on Artificial Intelligence Techniques and Algorithms
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Chapter 1
Introduction and Trends to Fuzzy Logic and Fuzzy Databases
Computational techniques in computer science and some engineering disciplines, which attempt to study, model, and analyze very complex phenomena: those for which more conventional methods have not yielded low cost, analytic, and complete solutions. Earlier computational approaches could model and precisely analyze only relatively simple systems. More complex systems arising in biology, medicine, the humanities, management sciences, artificial intelligence, machine learning, and similar fields often remained intractable to conventional mathematical and analytical methods. Soft computing techniques include: fuzzy systems (FS), neural networks (NN), evolutionary computation (EC), probabilistic reasoning (PR), and other ideas (chaos theory, etc.). Soft computing techniques often complement each other.
Published in Chapter: Introduction and Trends to Fuzzy Logic and Fuzzy Databases; From: Handbook of Research on Fuzzy Information Processing in Databases
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Chapter 5
Adaptive Neuro-Fuzzy Systems
Soft Computing refers to a partnership of computational techniques in computer science, artificial intelligence, machine learning and some engineering disciplines, which attempt to study, model, and analyze complex phenomena. The principle partners at this juncture are fuzzy logic, neuron-computing, probabilistic reasoning, and genetic algorithms. Thus the principle of soft computing is to exploit the tolerance for imprecision, uncertainty, and partial truth to achieve tractability, robustness, low cost solution, and better rapport with reality.
Published in Chapter: Adaptive Neuro-Fuzzy Systems; From: Encyclopedia of Artificial Intelligence
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Chapter 238
Watermarking Using Intelligent Methods: Survey
This is a term applied for defining approaches when the problem is not clear and its solution is unpredictable.
Published in Chapter: Watermarking Using Intelligent Methods: Survey; From: Encyclopedia of Business Analytics and Optimization
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Chapter 119
Harmony Search for Multiple Dam Scheduling
Collection of computational techniques in computer science, especially in artificial intelligence, such as fuzzy logic, neural networks, chaos theory, and evolutionary algorithms
Published in Chapter: Harmony Search for Multiple Dam Scheduling; From: Encyclopedia of Artificial Intelligence
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Chapter 101
Uncertainty and Vagueness Concepts in Decision Making
In contrast to hard computing, soft computing is a collection of methods (fuzzy sets, rough sets, neutral nets, etc.) for dealing with ambiguous situations like imprecision and uncertainty, for example, human expressions like “high profit at reasonable risks”. The objective of applying soft computing is to obtain robust solutions at reasonable costs.
Published in Chapter: Uncertainty and Vagueness Concepts in Decision Making; From: Encyclopedia of Decision Making and Decision Support Technologies
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Chapter 727
Using Metaheuristics as Soft Computing Techniques for Efficient Optimization
Problem solving strategies that tolerate imprecision/uncertainty/approximation in the data, and also can handle partial information/non-exact solutions for optimization problems.
Published in Chapter: Using Metaheuristics as Soft Computing Techniques for Efficient Optimization; From: Encyclopedia of Information Science and Technology, Third Edition
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