04-04-2012, 03:41 PM
Soft Computing
soft_computing.docx (Size: 108.67 KB / Downloads: 61)
Introduction:
The idea of soft computing was initiated in 1981 when Lotfi A. Zadeh published his first paper on soft data analysis “What is Soft Computing”, Soft Computing. Springer-Verlag Germany/USA 1997.].
Zadeh, defined Soft Computing into one multidisciplinary system as the fusion of the fields of Fuzzy Logic, Neuro-Computing, Evolutionary and Genetic Computing, and Probabilistic Computing.
Soft Computing is the fusion of methodologies designed to model and enable solutions to real world problems, which are not modeled or too difficult to model mathematically.
Definition of Soft Computing (SC)
Lotfi A. Zadeh, 1992 : “Soft Computing is an emerging approach to computing which parallel the remarkable ability of the human mind to reason and learn in a environment of uncertainty and imprecision”.
Soft Computing is still growing and developing. Hence, a clear definite agreement on what comprises Soft Computing has not yet been reached. More new sciences are still merging into Soft Computing.
The Soft Computing consists of several computing paradigms mainly:
Fuzzy Systems, Neural Networks, and Genetic Algorithms.
• Fuzzy set: for knowledge representation via fuzzy. If – Then rules.
• Neural Networks: for learning and adaptation
• Genetic Algorithms: for evolutionary computation
Goals of Soft Computing:
Soft Computing is a new multidisciplinary field, to construct new generation of Artificial Intelligence, known as Computational Intelligence.
The main goal of Soft Computing is to develop intelligent machines to provide solutions to real world problems, which are not modeled or too difficult to model mathematically.
Its aim is to exploit the tolerance for Approximation, Uncertainty, Imprecision and Partial Truth in order to achieve close resemblance with human like decision making.
Neural Network:
The term neural network was traditionally used to refer to a network or circuit of biological neurons. The modern usage of the term often refers to artificial neural networks, which are composed of artificial neurons or nodes. Thus the term has two distinct usages:
1. Biological neural networks are made up of real biological neurons that are connected or functionally related in the peripheral nervous system or the central nervous system. In the field of neuroscience, they are often identified as groups of neurons that perform a specific physiological function in laboratory analysis.
Fuzzy control system
A fuzzy control system is a control system based on fuzzy logic—a mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1, in contrast to classical or digital logic, which operates on discrete values of either 0 or 1 (true or false).
Conclusion:
The concept of fuzzy set has been and is a paradigm in the scientific-technological world with important repercussions in all social sectors because of the diversity of its applications, of the ease of its technological transference, and of the economic saving that its use supposes. Although when the first article on the subject was published about 40 years ago it was met with resistance from certain academic sectors, time has shown that fuzzy sets constitute the nucleus of a doctrinal body of indubitable solidness, dynamism and international recognition which is known as soft computing.
soft_computing.docx (Size: 108.67 KB / Downloads: 61)
Introduction:
The idea of soft computing was initiated in 1981 when Lotfi A. Zadeh published his first paper on soft data analysis “What is Soft Computing”, Soft Computing. Springer-Verlag Germany/USA 1997.].
Zadeh, defined Soft Computing into one multidisciplinary system as the fusion of the fields of Fuzzy Logic, Neuro-Computing, Evolutionary and Genetic Computing, and Probabilistic Computing.
Soft Computing is the fusion of methodologies designed to model and enable solutions to real world problems, which are not modeled or too difficult to model mathematically.
Definition of Soft Computing (SC)
Lotfi A. Zadeh, 1992 : “Soft Computing is an emerging approach to computing which parallel the remarkable ability of the human mind to reason and learn in a environment of uncertainty and imprecision”.
Soft Computing is still growing and developing. Hence, a clear definite agreement on what comprises Soft Computing has not yet been reached. More new sciences are still merging into Soft Computing.
The Soft Computing consists of several computing paradigms mainly:
Fuzzy Systems, Neural Networks, and Genetic Algorithms.
• Fuzzy set: for knowledge representation via fuzzy. If – Then rules.
• Neural Networks: for learning and adaptation
• Genetic Algorithms: for evolutionary computation
Goals of Soft Computing:
Soft Computing is a new multidisciplinary field, to construct new generation of Artificial Intelligence, known as Computational Intelligence.
The main goal of Soft Computing is to develop intelligent machines to provide solutions to real world problems, which are not modeled or too difficult to model mathematically.
Its aim is to exploit the tolerance for Approximation, Uncertainty, Imprecision and Partial Truth in order to achieve close resemblance with human like decision making.
Neural Network:
The term neural network was traditionally used to refer to a network or circuit of biological neurons. The modern usage of the term often refers to artificial neural networks, which are composed of artificial neurons or nodes. Thus the term has two distinct usages:
1. Biological neural networks are made up of real biological neurons that are connected or functionally related in the peripheral nervous system or the central nervous system. In the field of neuroscience, they are often identified as groups of neurons that perform a specific physiological function in laboratory analysis.
Fuzzy control system
A fuzzy control system is a control system based on fuzzy logic—a mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1, in contrast to classical or digital logic, which operates on discrete values of either 0 or 1 (true or false).
Conclusion:
The concept of fuzzy set has been and is a paradigm in the scientific-technological world with important repercussions in all social sectors because of the diversity of its applications, of the ease of its technological transference, and of the economic saving that its use supposes. Although when the first article on the subject was published about 40 years ago it was met with resistance from certain academic sectors, time has shown that fuzzy sets constitute the nucleus of a doctrinal body of indubitable solidness, dynamism and international recognition which is known as soft computing.