The IUS Soft Computing Research Group - IUSSCRG
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.
· 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.
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.
For activities in this field, please refer to
· Southeast Europe Journal of Soft Computing
· Journal of Soft Computing
· Ther world Federation of Soft Computing Homepage and its journal Applied Soft Computing
MEMBERS
Members of the research group:
|
Abdurrazag Ali |
Aburas |
aaburas[at]ius.edu.ba |
|
Faruk Berat |
Akçeşme |
farberak[at]yahoo.com |
|
Mehmet |
Can |
mcan[at]ius.edu.ba |
|
Betül |
Çiçek |
betul.cicek[at]yahoo.com |
|
Nesibe Merve |
Demir |
ndemir[at]ius.edu.ba |
|
Fehim |
Fındık |
ffindik[at]ius.edu.ba |
|
Sadina |
Gagula-Palalic |
sadina[at]ius.edu.ba |
|
Haris |
Gavranovic |
hgavranovic[at]ius.edu.ba |
|
Alma |
Husagic-Selman |
aselman[at]ius.edu |
|
Amir |
Jamak |
amirjamak[at]yahoo.com |
|
Kanita |
Karadjuzović-Hadziabdić |
kanita[at]ius.edu.ba |
|
Raşit
|
Köker |
rkoker[at]ius.edu.ba |
|
Ali Osman |
Kuşakçı |
akusakci[at]ius.edu.ba |
|
Haris |
Memic |
hmemic[at]ius.edu.ba |
|
Indira |
Muhic |
imuhic[at]ius.edu |
|
Tarik |
Namas |
tnamas[at]ius.edu.ba |
|
Indira |
Rustempasic |
irustempasic[at]ius.edu.ba |
|
Alen |
Savatic |
alen[at]savatic.net |
|
Suvad |
Selman |
sselman[at]ius.edu.ba |
|
Kemal |
Turan |
kturan[at]ius.edu.ba |
|
Mehmet Akif |
Yaman |
myaman[at]ius.edu.ba |
PUBLICATIONS
A. Jamak, A. Savatic, and M. Can, Principal Component Analysis for Authorship Attribution, 49-56
A. Omerovic, J. Jusufovic, and M. Can, Optimization of Transport Problems with Fuzzy Coefficients, pp. 27-32
A. Savatic, A. Jamak, and M. Can, Detecting the Authors of Texts by Neural Network Committee Machines, pp. 81-92
Alp Kor and M. Can, A Fuzzy Model for A Multistage Supply Chain System Controlled By Kanban, pp. 43-48
F. B. Akçeşme and M. Can, Three Variable Cancer Angiogenesis models, pp. 1-7
J. Jusufovic, A. Omerovic, and M. Can, Preemptive Fuzzy Goal Programming in Fuzzy Environments, 8-10
M. Can and R. Kara, Bifurcation Analysis for Metapopulation Models, pp. 19-26
M. Can, A. Jamak, and A. Savatic, Teaching Neural Networks to Detect the Authors of Texts Using Lexical Descriptors, 57-72
M. Can, Cournot Model of Duopoly with Incomplete Information, 33-36
M. Can, Fuzzy Multiple Objective Models For Facility Location Problems, pp. 93-98
M. Can, K. Karađuzovic Hadžiabdić, and N. M. Demir, Teaching Neural Networks to Classify the Authors of Texts, 73-80
M. Can, Mathematical Models of Tumor Growth and Angiogenesis, pp. 11-18
M. Can, Principal Component Analysis and Neural Networks for Authorship Attribution, pp. 99-114
M. S. Eryılmaz, A. O. Kuşakçı, H. Gavranovic, and F. Findik, Analysis of Shoe Manufacturing Factory By Simulation of Production Processes, 120-127
S. Gagula-Palalic, and M. Can, Inventory Control Using Fuzzy Dynamic Programming, pp. 37-42
S. Gagula-Palalic, M. Can, Fuzzy C-means Model and Algorithm for Data Clustering, pp. 115-119
S. Selman, K. Turan, and A. O. Kuşakçı, Distinction of the Authors of Texts Using Multilayered Feedforward
LINKS
SEJSC Southeast Europe Journal of Soft Computing
IUS Soft Computing Research Group will start the publication of a new journal on soft computing. Group members will act as editorial board.
Bosnia and Herzegovina Operation Research Society - BIHORS
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