Soft Computing Notes for RGPV 8th Semester
What Is 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.
Importance of Soft Computing
The complementarily 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.
A 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.
Hard Computing Vs Soft Computing
1) Hard computing, i.e., conventional computing, requires a precisely stated analytical model and often a lot of computation time. 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.
2) Hard computing based on binary logic, crisp systems, numerical analysis and crisp software but soft computing based on fuzzy logic, neural nets and probabilistic reasoning.
3) Hard computing has the characteristics of precision and categoricity and the soft computing, approximation and dispositionality. Although in hard computing, imprecision and uncertainty are undesirable properties, in soft computing the tolerance for imprecision and uncertainty is exploited to achieve tractability, lower cost, high Machine Intelligence Quotient (MIQ) and economy of communication
4) Hard computing requires programs to be written; soft computing can evolve its own programs
5) Hard computing uses two-valued logic; soft computing can use multivalve or fuzzy logic
6) Hard computing is deterministic; soft computing incorporates stochasticity
7) Hard computing requires exact input data; soft computing can deal with ambiguous and noisy data
8) Hard computing is strictly sequential; soft computing allows parallel computations
9) Hard computing produces precise answers; soft computing can yield approximate answers
Soft Computing Techniques
soft computing techniques resemble biological processes more closely than traditional techniques, which are largely based on formal logical system , such as sententious logic and predicate logic, or rely heavily on computer-aided numerical analysis (as in finite element analysis). Soft computing techniques are intended to complement each other.
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.