Soft computing and intelligent systems design - [Book review]

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.

Advances in Intelligent Systems and Computing

The series "Advances in Intelligent Systems and Computing" contains publications on theory, applications, and design methods of Intelligent Systems and Intelligent Computing. Virtually all disciplines such as engineering, natural sciences, computer and information science, ICT, economics, business, e-commerce, environment, healthcare, life science are covered. The list of topics spans all the areas of modern intelligent systems and computing such as: computational intelligence, soft computing including neural networks, fuzzy systems, evolutionary computing and the fusion of these paradigms, social intelligence, ambient intelligence, computational neuroscience, artificial life, virtual worlds and society, cognitive science and systems, Perception and Vision, DNA and immune based systems, self-organizing and adaptive systems, e-Learning and teaching, human-centered and human-centric computing, recommender systems, intelligent control, robotics and mechatronics including human-machine teaming, knowledge-based paradigms, learning paradigms, machine ethics, intelligent data analysis, knowledge management, intelligent agents, intelligent decision making and support, intelligent network security, trust management, interactive entertainment, Web intelligence and multimedia. The publications within "Advances in Intelligent Systems and Computing" are primarily proceedings of important conferences, symposia and congresses. They cover significant recent developments in the field, both of a foundational and applicable character. An important characteristic feature of the series is the short publication time and worldwide distribution. This permits a rapid and broad dissemination of research results.

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.

Advances in intelligent and soft computing, 2006

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.

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.

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..

International Journal of Computational Intelligence Systems, 2010

Soft Computing (SC) is a concept with constantly evolving semantics, as researchers have adopted its main philosophy while adding various interpretations and facets to this concept. Originally defined as a loose association or partnership of components, SC has gone through several transformational phases. This paper will trace some of the phases experienced by the author as part of his understanding of the evolution of SC and its role in constructing decision-making models. The first phase is the hybridization phase, driven by the inherit ease of integration of SC components. The second phase is a two-level model characterization, based on the split between object-level and meta-level reasoning. This phase, inspired by traditional AI problem formulation, led to a third phase, in which we addressed the knowledge and meta-knowledge representation required by each of these reasoning levels using a linguistics analogy. The fourth phase is the extension of the heuristics used at the metalevel, e.g. Metaheuristics (MH's) from evolutionary algorithms to other search methods. The fifth and last phase, further described in this paper, is the proposal for a strong separation between offline MH's (used for design and tuning) and online MH's (used for models selection or aggregation.) This last view suggests a broader use of SC components, since it enables us to use hybrid SC techniques at each of the MH's levels as well as at the object level. Furthermore, this separation facilitates the model lifecycle management, which is required to maintain the models vitality and prevent their obsolescence over time.