SlideShare une entreprise Scribd logo
1  sur  2
Télécharger pour lire hors ligne
Soft Computing



Definition of Soft Computing

Soft Computing differs from conventional (hard) computing in that, unlike hard computing, it is
tolerant of imprecision, uncertainty, partial truth, and approximation. In effect, the role model for
soft computing is the human mind. Principal constituents of Soft Computing are Neural
Networks, Fuzzy Logic, Evolutionary Computation, Swarm Intelligence and Bayesian Networks.
The successful applications of soft computing suggest that the impact of soft computing will be
felt increasingly in coming years. Soft computing is likely to play an important role in science
and engineering, but eventually its influence may extend much farther

Soft Computing became a formal Computer Science area of study in the early 1990's.Earlier
computational approaches could model and precisely analyze only relatively simple systems.
More complex systems arising in biology, medicine, the humanities, management sciences, and
similar fields often remained intractable to conventional
mathematical and analytical methods. That said, it should be pointed out that simplicity and
complexity of systems are relative, and many conventional mathematical models have been both
challenging and very productive. Soft computing deals with imprecision, uncertainty, partial
truth, and approximation to achieve tractability, robustness and low solution cost

Introduction of Soft Computing

Unlike hard computing schemes, which strive for exactness and full truth, soft computing
techniques exploit the given tolerance of imprecision, partial truth, and uncertainty for a
particular problem. Another common contrast comes from the observation that inductive
reasoning plays a larger role in soft computing than in hard computing. Components of soft
computing include: Neural Network, Perceptron, Fuzzy Systems, Baysian Network, Swarm
Intelligence and Evolutionary Computation.

The highly parallel processing and layered neuronal morphology with learning abilities of the
human cognitive faculty ~the brain~ provides us with a new tool for designing a cognitive
machine that can learn and recognize complicated patterns like human faces and Japanese
characters. The theory of fuzzy logic, the basis for soft computing, provides mathematical
power for the emulation of the higher-order cognitive functions ~the thought and perception
processes. A marriage between these evolving disciplines, such as neural computing, genetic
algorithms and fuzzy logic, may provide a new class of computing systems ~neural-fuzzy
systems ~ for the emulation of higher-order cognitive power

Neural Networks:

Neural Networks, which are simplified models of the biological neuron system, is a massively
parallel distributed processing system made up of highly interconnected neural computing
elements that have the ability to learn and thereby acquire knowledge and making it available for
use. It resembles the brain in two respects:
- Knowledge is acquired by the network through a learning process.
-Interconnection strengths known as synaptic weights are used to store the knowledge




A neuron is composed of nucleus- a cell body known as soma. Attached to the soma are long
irregularly shaped filaments called dendrites. The dendrites behave as input channels, all inputs
from other neurons arrive through dendrites.

Another link to soma called Axon is electrically active and serves as an output channel. If the
cumulative inputs received by the soma raise internal electric potential of the cell known as
membrane potential, then the neuron fires by propagating the action potential down the axon to
excite or inhibit other neurons. The axon terminates in a specialized contact called synapse that
connects the axon with the dendrite links of another neuron

An artificial neuron model bears direct analogy to the actual constituents of biological neuron.
This model forms basis of Artificial Neural Networks

Contenu connexe

Tendances

soft-computing
 soft-computing soft-computing
soft-computingstudent
 
ON SOFT COMPUTING TECHNIQUES IN VARIOUS AREAS
ON SOFT COMPUTING TECHNIQUES IN VARIOUS AREASON SOFT COMPUTING TECHNIQUES IN VARIOUS AREAS
ON SOFT COMPUTING TECHNIQUES IN VARIOUS AREAScscpconf
 
Introduction to Soft Computing
Introduction to Soft Computing Introduction to Soft Computing
Introduction to Soft Computing Aakash Kumar
 
Introduction to soft computing
Introduction to soft computingIntroduction to soft computing
Introduction to soft computingAnkush Kumar
 
Soft Computing
Soft ComputingSoft Computing
Soft ComputingMANISH T I
 
Application of soft computing techniques in electrical engineering
Application of soft computing techniques in electrical engineeringApplication of soft computing techniques in electrical engineering
Application of soft computing techniques in electrical engineeringSouvik Dutta
 
Soft computing (ANN and Fuzzy Logic) : Dr. Purnima Pandit
Soft computing (ANN and Fuzzy Logic)  : Dr. Purnima PanditSoft computing (ANN and Fuzzy Logic)  : Dr. Purnima Pandit
Soft computing (ANN and Fuzzy Logic) : Dr. Purnima PanditPurnima Pandit
 
Soft Computing-173101
Soft Computing-173101Soft Computing-173101
Soft Computing-173101AMIT KUMAR
 
Emotion detection using cnn.pptx
Emotion detection using cnn.pptxEmotion detection using cnn.pptx
Emotion detection using cnn.pptxRADO7900
 
Unit I & II in Principles of Soft computing
Unit I & II in Principles of Soft computing Unit I & II in Principles of Soft computing
Unit I & II in Principles of Soft computing Sivagowry Shathesh
 
Neural networks and deep learning
Neural networks and deep learningNeural networks and deep learning
Neural networks and deep learningRADO7900
 
Soft computing
Soft computingSoft computing
Soft computingCSS
 
Soft computing from net
Soft computing from netSoft computing from net
Soft computing from netEasyMedico.com
 
Machine Learning techniques
Machine Learning techniques Machine Learning techniques
Machine Learning techniques Jigar Patel
 

Tendances (20)

soft-computing
 soft-computing soft-computing
soft-computing
 
ON SOFT COMPUTING TECHNIQUES IN VARIOUS AREAS
ON SOFT COMPUTING TECHNIQUES IN VARIOUS AREASON SOFT COMPUTING TECHNIQUES IN VARIOUS AREAS
ON SOFT COMPUTING TECHNIQUES IN VARIOUS AREAS
 
Kiran computer
Kiran computerKiran computer
Kiran computer
 
Introduction to Soft Computing
Introduction to Soft Computing Introduction to Soft Computing
Introduction to Soft Computing
 
Soft computing
Soft computingSoft computing
Soft computing
 
Soft computing
Soft computingSoft computing
Soft computing
 
Introduction to soft computing
Introduction to soft computingIntroduction to soft computing
Introduction to soft computing
 
Soft Computing
Soft ComputingSoft Computing
Soft Computing
 
Application of soft computing techniques in electrical engineering
Application of soft computing techniques in electrical engineeringApplication of soft computing techniques in electrical engineering
Application of soft computing techniques in electrical engineering
 
Soft computing (ANN and Fuzzy Logic) : Dr. Purnima Pandit
Soft computing (ANN and Fuzzy Logic)  : Dr. Purnima PanditSoft computing (ANN and Fuzzy Logic)  : Dr. Purnima Pandit
Soft computing (ANN and Fuzzy Logic) : Dr. Purnima Pandit
 
SoftComputing1
SoftComputing1SoftComputing1
SoftComputing1
 
Introduction to soft computing
 Introduction to soft computing Introduction to soft computing
Introduction to soft computing
 
Soft Computing-173101
Soft Computing-173101Soft Computing-173101
Soft Computing-173101
 
Emotion detection using cnn.pptx
Emotion detection using cnn.pptxEmotion detection using cnn.pptx
Emotion detection using cnn.pptx
 
Soft computing
Soft computingSoft computing
Soft computing
 
Unit I & II in Principles of Soft computing
Unit I & II in Principles of Soft computing Unit I & II in Principles of Soft computing
Unit I & II in Principles of Soft computing
 
Neural networks and deep learning
Neural networks and deep learningNeural networks and deep learning
Neural networks and deep learning
 
Soft computing
Soft computingSoft computing
Soft computing
 
Soft computing from net
Soft computing from netSoft computing from net
Soft computing from net
 
Machine Learning techniques
Machine Learning techniques Machine Learning techniques
Machine Learning techniques
 

Similaire à Soft computing abstracts

Similaire à Soft computing abstracts (20)

Artificial Neural Network Abstract
Artificial Neural Network AbstractArtificial Neural Network Abstract
Artificial Neural Network Abstract
 
Nature Inspired Reasoning Applied in Semantic Web
Nature Inspired Reasoning Applied in Semantic WebNature Inspired Reasoning Applied in Semantic Web
Nature Inspired Reasoning Applied in Semantic Web
 
Neural networks
Neural networksNeural networks
Neural networks
 
neural networks
neural networksneural networks
neural networks
 
08 neural networks(1).unlocked
08 neural networks(1).unlocked08 neural networks(1).unlocked
08 neural networks(1).unlocked
 
Neural Computing
Neural ComputingNeural Computing
Neural Computing
 
Hybrid Intelligent
Hybrid IntelligentHybrid Intelligent
Hybrid Intelligent
 
Neural network
Neural networkNeural network
Neural network
 
Artifical neural networks
Artifical neural networksArtifical neural networks
Artifical neural networks
 
Quantum neural network
Quantum neural networkQuantum neural network
Quantum neural network
 
ANN - UNIT 1.pptx
ANN - UNIT 1.pptxANN - UNIT 1.pptx
ANN - UNIT 1.pptx
 
Neural Network
Neural NetworkNeural Network
Neural Network
 
Lesson 37
Lesson 37Lesson 37
Lesson 37
 
AI Lesson 37
AI Lesson 37AI Lesson 37
AI Lesson 37
 
[IJET V2I2P20] Authors: Dr. Sanjeev S Sannakki, Ms.Anjanabhargavi A Kulkarni
[IJET V2I2P20] Authors: Dr. Sanjeev S Sannakki, Ms.Anjanabhargavi A Kulkarni[IJET V2I2P20] Authors: Dr. Sanjeev S Sannakki, Ms.Anjanabhargavi A Kulkarni
[IJET V2I2P20] Authors: Dr. Sanjeev S Sannakki, Ms.Anjanabhargavi A Kulkarni
 
Project Report -Vaibhav
Project Report -VaibhavProject Report -Vaibhav
Project Report -Vaibhav
 
What is Neural Network?
What is Neural Network?What is Neural Network?
What is Neural Network?
 
An Overview On Neural Network And Its Application
An Overview On Neural Network And Its ApplicationAn Overview On Neural Network And Its Application
An Overview On Neural Network And Its Application
 
Artificial Neural Network report
Artificial Neural Network reportArtificial Neural Network report
Artificial Neural Network report
 
Paper id 252014107
Paper id 252014107Paper id 252014107
Paper id 252014107
 

Soft computing abstracts

  • 1. Soft Computing Definition of Soft Computing Soft Computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation. In effect, the role model for soft computing is the human mind. Principal constituents of Soft Computing are Neural Networks, Fuzzy Logic, Evolutionary Computation, Swarm Intelligence and Bayesian Networks. The successful applications of soft computing suggest that the impact of soft computing will be felt increasingly in coming years. Soft computing is likely to play an important role in science and engineering, but eventually its influence may extend much farther Soft Computing became a formal Computer Science area of study in the early 1990's.Earlier computational approaches could model and precisely analyze only relatively simple systems. More complex systems arising in biology, medicine, the humanities, management sciences, and similar fields often remained intractable to conventional mathematical and analytical methods. That said, it should be pointed out that simplicity and complexity of systems are relative, and many conventional mathematical models have been both challenging and very productive. Soft computing deals with imprecision, uncertainty, partial truth, and approximation to achieve tractability, robustness and low solution cost Introduction of Soft Computing Unlike hard computing schemes, which strive for exactness and full truth, soft computing techniques exploit the given tolerance of imprecision, partial truth, and uncertainty for a particular problem. Another common contrast comes from the observation that inductive reasoning plays a larger role in soft computing than in hard computing. Components of soft computing include: Neural Network, Perceptron, Fuzzy Systems, Baysian Network, Swarm Intelligence and Evolutionary Computation. The highly parallel processing and layered neuronal morphology with learning abilities of the human cognitive faculty ~the brain~ provides us with a new tool for designing a cognitive machine that can learn and recognize complicated patterns like human faces and Japanese characters. The theory of fuzzy logic, the basis for soft computing, provides mathematical power for the emulation of the higher-order cognitive functions ~the thought and perception processes. A marriage between these evolving disciplines, such as neural computing, genetic algorithms and fuzzy logic, may provide a new class of computing systems ~neural-fuzzy systems ~ for the emulation of higher-order cognitive power Neural Networks: Neural Networks, which are simplified models of the biological neuron system, is a massively parallel distributed processing system made up of highly interconnected neural computing elements that have the ability to learn and thereby acquire knowledge and making it available for
  • 2. use. It resembles the brain in two respects: - Knowledge is acquired by the network through a learning process. -Interconnection strengths known as synaptic weights are used to store the knowledge A neuron is composed of nucleus- a cell body known as soma. Attached to the soma are long irregularly shaped filaments called dendrites. The dendrites behave as input channels, all inputs from other neurons arrive through dendrites. Another link to soma called Axon is electrically active and serves as an output channel. If the cumulative inputs received by the soma raise internal electric potential of the cell known as membrane potential, then the neuron fires by propagating the action potential down the axon to excite or inhibit other neurons. The axon terminates in a specialized contact called synapse that connects the axon with the dendrite links of another neuron An artificial neuron model bears direct analogy to the actual constituents of biological neuron. This model forms basis of Artificial Neural Networks