COULD COMPUTING AND SOFT COMPUTING
Intro
First of
all I would like to praise and gratitude to God who has blessed me so that I
could finish this paper.I also want to thank my friend who supports. No man which out and out,I admit I
am human one have bounds,therefore is not all the things can be made perfectly
in opus writes this. I just try to do as useful as possible with competence I
whatever available.
I am human ordinary and have lack,I have the honour to accept
criticism and tips of reader all together that prudent. I will accept it that
to fix opus writes for result what do can better again for to the fore it.
A.Cloud computing
Today, the new information world is developed. This world
allows us to be mobile, to do business faster from any place. Nowadays, we are
no longer dependent on the computers to connect to the Internet. The majority
of mobile devices have eliminated this inconvenience. And today we can observe
a dramatic growth of technical and information resources provided through the
network. It has changed our life for ever, as well as changed the organization
paradigm of information systems.Cloud Computing is an excellent paradigm for today to enable
the high-end computing on numerous nodes of a distributed system. Your clients
don’t even need to know how it works. They just use your resources as the
Internet-service and are satisfied with it.We understand how it is important to provide services with
intuitive transparent interface based on the powerful distributed heterogeneous
platform. Cloud Computing paradigm brings the next generation of virtualization
process and the new way of service-oriented architecture (SOA) application.Contek Soft uses modern technologies and knows how to
organize and apply one of the SOA model depending on your business goals:Infrastructure as a Service (IaaS)Platform as a Service (PaaS)Software as a Service (SaaS)
B.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. The guiding principle of soft computing is: Exploit the tolerance
for imprecision, uncertainty, partial truth, and approximation to achieve
tractability, robustness and low solution cost. The basic ideas underlying soft
computing in its current incarnation have links to many earlier influences,
among them Zadeh's 1965 paper on fuzzy sets; the 1973 paper on the analysis of
complex systems and decision processes; and the 1979 report (1981 paper) on
possibility theory and soft data analysis. The inclusion of neural computing
and genetic computing in soft computing came at a later point.
At this juncture, the principal constituents of Soft
Computing (SC) are Fuzzy Logic (FL), Neural Computing (NC), Evolutionary
Computation (EC) Machine Learning (ML) and Probabilistic Reasoning (PR), with
the latter subsuming belief networks, chaos theory and parts of learning
theory. What is important to note is that soft computing is not a melange.
Rather, it is a partnership in which each of the partners contributes a
distinct methodology for addressing problems in its domain. In this
perspective, the principal constituent methodologies in SC are complementary
rather than competitive. Furthermore, soft computing may be viewed as a
foundation component for the emerging field of conceptual intelligence.
-Fuzzy Systems
-Neural Networks
-Evolutionary Computation-Machine Learning-Probabilistic Reasoning
*Importance of Soft Computing
The complementarity of FL, NC, GC, and PR has an important consequence: in many cases a problem can be solved most effectively by using FL, NC, GC and PR in combination rather than exclusively. A striking example of a particularly effective combination is what has come to be known as "neurofuzzy systems." Such systems are becoming increasingly visible as consumer products ranging from air conditioners and washing machines to photocopiers and camcorders. Less visible but perhaps even more important are neurofuzzy systems in industrial applications. What is particularly significant is that in both consumer products and industrial systems, the employment of soft computing techniques leads to systems which have high MIQ (Machine Intelligence Quotient). In large measure, it is the high MIQ of SC-based systems that accounts for the rapid growth in the number and variety of applications of soft computing.
The conceptual structure of soft computing suggests that students should be trained not just in fuzzy logic, neurocomputing, genetic programming, or probabilistic reasoning but in all of the associated methodologies, though not necessarily to the same degree.
At present, the BISC Group (Berkeley Initiative on Soft Computing) comprises close to 600 students, professors, employees of private and non-private organizations and, more generally, individuals who have interest or are active in soft computing or related areas. Currently, BISC has over 50 Institutional Affiliates, with their ranks continuing to grow in number.
At Berkeley, BISC provides a supportive environment for visitors, postdocs and students who are interested in soft computing and its applications. In the main, support for BISC comes from member companies.
* Glimpse Into The Future
The successful applications of soft computing and the rapid growth of BISC suggest that the impact of soft computing will be felt increasingly in coming years. Soft computing is likely to play an especially important role in science and engineering, but eventually its influence may extend much farther.
In many ways, soft computing represents a significant paradigm shift in the aims of computing - a shift which reflects the fact that the human mind, unlike present day computers, possesses a remarkable ability to store and process information which is pervasively imprecise, uncertain and lacking in categoricity.
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.
It is precisely this dynamism which has lead us to reflect in this article on what the defining limits of soft computing are in an attempt to widen the range of its basic components with the inclusion of metaheuristics. This wider and more general perspective of soft computing allows the possibility of incorporating new and as yet undeveloped search/optimization methods (without any of the already explored methods being the protagonist), thereby avoiding the tendency indicated by Zadeh in [3] to proclaim the methodology in which we are interested to be the best (which, as Zadeh pointed out, is yet another version of the famous hammer principle which says that "When the only tool you have is a hammer, everything begins to look like a nail").
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