Soft Computing Applications

2019

The paper attempts to give protection of soft computing in the investigations of the scientist form the institutes of Informatics, Information technologies and Information and Communications Technologies of the Bulgarian Academy of Sciences. Presented is a short list of 60 publications on Soft Computing.

Studies in Fuzziness and Soft Computing, 2012

After a brief expression of my personal gratitude for the inspiring mentorship received from Professors Lotfi Zadeh and Abe Mamdani, I review some of the 70’s seminal papers that lead to Prof Mamdani’s innovation in fuzzy control (FC). Then, I discuss the concurrent development of FC and expert system applications, which took place in the 80’s, noting the similarity stemming from their common use of knowledge bases (KB) developed via rapid prototyping. FC and expert systems also shared a common difficulty: avoiding KB obsolescence over time which was caused by the dynamic environment in which they were deployed. For FC, the lack of automation in their design and maintenance process changed in the 90’s, when Soft Computing (SC) offered a broader computational paradigm for developing intelligent systems, by adding a search and learning component to the fuzzy logic reasoning component. These SC components allowed researchers to automate the fine-tuning of fuzzy systems. In a 2004 position paper, Prof. Mamdani and I made some initial remarks regarding the use and misuse of SC as a tool. In the last six years we have seen an evolution of SC, with a clearer role for their use in capturing knowledge to embed in object-level models, and meta-knowledge to guide the design and upkeep of these models. I illustrate this concept with three real-world examples in insurance risk management, fleet asset selection, and power plant management.

Soft computing, unlike traditional computing, fits prediction models and provides answers to real complex problems. Unlike hard computing, soft computing tolerates great, uncertainty, partial truth and prediction. In fact, the model for soft computing is the human mind. Technologies such as fuzzy logic, genetic algorithms, artificial neural networks, machine learning and expert systems are part of soft computing. The theory of soft computing, introduced in the 1980s, is now a major area of research in all areas. Because of its low cost and high performance, soft computing is now successfully used in many home, commercial and industrial applications. The field of application of soft computing is increasing today. This article outlines the current state of Soft computing technology and explains the latter advantages and disadvantages to traditional computing technology..

Fuzzy Sets and Systems, 2004

Soft Computing is a relatively new computing paradigm bestowed with tools and techniques for handling real world problems. The main components of this computing paradigm are neural networks, fuzzy logic and evolutionary computation. Each and every component of the soft computing paradigm operates either independently or in coalition with the other components for addressing problems related to modeling, analysis and processing of data. An overview of the essentials and applications of the soft computing paradigm is presented in this chapter with reference to the functionalities and operations of its constituent components. Neural networks are made up of interconnected processing nodes/neurons, which operate on numeric data. These networks posses the capabilities of adaptation and approximation. The varied amount of uncertainty and ambiguity in real world data are handled in a linguistic framework by means of fuzzy sets and fuzzy logic. Hence, this component is efficient in understanding vagueness and imprecision in real world knowledge bases. Genetic algorithms, simulated annealing algorithm and ant colony optimization algorithm are representative evolutionary computation techniques, which are efficient in deducing an optimum solution to a problem, thanks to the inherent exhaustive search methodologies adopted. Of late, rough sets have evolved to improve upon the performances of either of these components by way of approximation techniques. These soft computing techniques have been put to use in wide variety of problems ranging from scientific to industrial applications. Notable among these applications include image processing, pattern recognition, Kansei information processing, data mining, web intelligence etc.

Proceedings of the IEEE, 1999

Soft computing (SC) is an association of computing methodologies that includes as its principal members fuzzy logic, neuro-computing, evolutionary computing and probabilistic computing. We present a collection of methods and tools that can be used to perform diagnostics, estimation, and control. These tools are a great match for real-world applications that are characterized by imprecise, uncertain data, and incomplete domain knowledge. We outline the advantages of applying SC techniques and in particular the synergy derived from the use of hybrid SC systems. We illustrate some combinations of hybrid SC systems, such as fuzzy logic controllers (FLCs) tuned by neural networks (NNs) and evolutionary computing (EC), NNs tuned by EC or FLCs, and EC controlled by FLCs. We discuss three successful real-world examples of SC applications to industrial equipment diagnostics, freight train control, and residential property valuation.

The theoretical and practical developments of the theory of fuzzy logic and soft computing are surveyed. Specially, we review the history and main milestones of fuzzy logic (in the wide sense), the more recent development of soft computing, and presenting a view of applications: from the most abstract to the most practical ones. It is widely accepted that the main components of Soft Computing are Fuzzy Logic, Probabilistic Reasoning, Neural Computing and Genetic Algorithms. This four constituents share common features and they are considered complementary instead of competitive. The mentioned technologies can be combined in models which exploit their best characteristics. The applications range from the purely theoretical ones, those which develop new lines in abstract mathematics or logic, passing across the areas of multi-media, preference modelling, information retrieval, hybrid intelligent systems, image processing, etc., to practical applications domains such as robotics and manufacturing, actuarial science, nuclear or medical engineering. The basics of fuzzy logic and soft computing are surveyed to a range of applications of these fields ranging from the purely theoretical to the most practical ones.

2000

So Computing admits approximate reasoning, imprecision, uncertainty and partial truth in order to mimic aspects of the remarkable human capability of making decisions in real-life and ambiguous environments. "So Computing in Industrial Applications" contains a collection of papers that were presented at the 11th On-line World Conference on So Computing in Industrial Applications, held in September-October 2006. is carefully edited book provides a comprehensive overview of the recent advances in the industrial applications of so computing and covers a wide range of application areas, including data analysis and data mining, computer graphics, intelligent control, systems, pattern recognition, classi ers, as well as modeling optimization. e book is aimed at researchers and practitioners who are engaged in developing and applying intelligent systems principles to solving real-world problems. It is also suitable as wider reading for science and engineering postgraduate students. -7 8 3 5 4 0 7 0 7 0 4 2 9 ISBN 978-3-540-70704-2 › Advances in So Com put ing 39

2020

Soft computing is a study of the science of logic, thinking, analysis and research that combines real-world problems with biologically inspired methods. Soft computing is the main motivation behind the idea of conceptual intelligence in machines. As such, it is an extension of heuristics and the resolution of complex problems that are very difficult to model mathematically. Smooth computing tolerates printing; uncertainty and approximation that differ from manual calculation. Soft Computing enumerates techniques like ANN, Evolutionary computing, Fuzzy Logic and statistics, they are advantageous and separately applied techniques which are used together to solve problems which are complex, very easily. This article highlights the various soft computing ting techniques and emerging areas of soft computing ting where they have been successfully implemented.

Soft Computing is the study of science of reasoning, thinking, analyzing and detecting that correlates the real world problems to the biological inspired methods. Soft Computing is the big motivation behind the idea of conceptual intelligence in machines. As such, it is an extension of heuristics and solve complex problems that too difficult to model mathematically. Soft Computing is tolerant of impression; uncertainty and approximation which is differ from hand computing. Soft Computing enumerates techniques like ANN, Evolutionary computing, Fuzzy Logic and statistics, they are advantageous and separately applied techniques but when used together solve complex problems very easily. This paper highlights various soft computing techniques and emerging fields of soft computing where they successfully applied.

Computer Science & Information Technology ( CS & IT ), 2013

Soft Computing refers to the science of reasoning, thinking and deduction that recognizes and uses the real world phenomena of grouping, memberships, and classification of various quantities under study. As such, it is an extension of natural heuristics and capable of dealing with complex systems because it does not require strict mathematical definitions and distinctions for the system components. It differs from hard computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty and partial truth. 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 and partial truth to achieve tractability, robustness and low solution cost. The main techniques in soft computing are evolutionary computing, artificial neural networks, and fuzzy logic and Bayesian statistics. Each technique can be used separately, but a powerful advantage of soft computing is the complementary nature of the techniques. Used together they can produce solutions to problems that are too complex or inherently noisy to tackle with conventional mathematical methods. The applications of soft computing have proved two main advantages. First, it made solving nonlinear problems, in which mathematical models are not available, possible. Second, it introduced the human knowledge such as cognition, recognition, understanding, learning, and others into the fields of computing. This resulted in the possibility of constructing intelligent systems such as autonomous self-tuning systems, and automated designed systems. This paper highlights various areas of soft computing techniques.