Fusion of soft and hard computing: multi-dimensional categorization of computationally intelligent hybrid systems

Sick, Bernhard; Ovaska, S.J.

doi:10.1007/s00521-006-0045-y

Cited by 5 publications

(3 citation statements)

References 33 publications

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“…Today, the decision for a specific kind of classifier (e.g., a particular soft computing or hard computing classifier, cf. [84,69]) is often made before the question, how particular properties of the classifier can be realized, is answered. We suggest to consider the latter point first.…”

Section: Resultsmentioning

confidence: 99%

So near and yet so far: New insight into properties of some well-known classifier paradigms

Fisch

Kühbeck

Sick

et al. 2010

Information Sciences

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Section: Resultsmentioning

confidence: 99%

So near and yet so far: New insight into properties of some well-known classifier paradigms

Fisch

Kühbeck

Sick

et al. 2010

Information Sciences

Self Cite

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“…Different fusion schemes were classified as 12 core categories and six supplementary categories, and the characteristic features of soft computing and hard computing constituents in practical fusion implementations were discussed as well. Sick and Ovaska (2007) introduced a multi-dimensional categorization scheme for fusion techniques and applied it by analyzing several fusion techniques where the soft computing part was realized by a neural network. The categorization scheme facilitated the discussion of advantages or drawbacks of certain fusion approaches, thus supporting the development of novel fusion techniques and applications.…”

Section: The Futurementioning

confidence: 99%

Development of soft computing and applications in agricultural and biological engineering

Huang

Lan

Thomson

et al. 2010

Computers and Electronics in Agriculture

265

126

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Soft computing is a set of "inexact" computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming. This paper reviews the development of soft computing techniques. With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture. The future of development and application of soft computing in agricultural and biological engineering is discussed.

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“…Therefore, efficient techniques are needed to process time series which contain temporal information with different reference periods. An approach which combines hard-and soft-computing techniques (cf., [2] and [3]) for this purpose will be described here.…”

Section: Introductionmentioning

confidence: 99%

Processing Short-Term and Long-Term Information With a Combination of Polynomial Approximation Techniques and Time-Delay Neural Networks

Fuchs

Gruber²,

Reitmaier

et al. 2009

IEEE Trans. Neural Netw.

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Neural networks are often used to process temporal information, i.e., any kind of information related to time series. In many cases, time series contain short-term and long-term trends or behavior. This paper presents a new approach to capture temporal information with various reference periods simultaneously. A least squares approximation of the time series with orthogonal polynomials will be used to describe short-term trends contained in a signal (average, increase, curvature, etc.). Long-term behavior will be modeled with the tapped delay lines of a time-delay neural network (TDNN). This network takes the coefficients of the orthogonal expansion of the approximating polynomial as inputs such considering short-term and long-term information efficiently. The advantages of the method will be demonstrated by means of artificial data and two real-world application examples, the prediction of the user number in a computer network and online tool wear classification in turning.

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Fusion of soft and hard computing: multi-dimensional categorization of computationally intelligent hybrid systems

Cited by 5 publications

References 33 publications

So near and yet so far: New insight into properties of some well-known classifier paradigms

So near and yet so far: New insight into properties of some well-known classifier paradigms

Development of soft computing and applications in agricultural and biological engineering

Processing Short-Term and Long-Term Information With a Combination of Polynomial Approximation Techniques and Time-Delay Neural Networks

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