Tuning of Large-Scale Linguistic Equation (LE) Models with Genetic Algorithms

Juuso, Esko

doi:10.1007/978-3-642-04921-7_17

Cited by 18 publications

(18 citation statements)

References 5 publications

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“…The scaling functions handle efficiently the parameter constraints of the monotonously increasing second order polynomials and the whole system is configured with a set of parameters. (Juuso, 2009a)…”

Section: Genetic Tuningmentioning

confidence: 99%

“…The nonlinear scaling technique is needed in constructing nonlinear models with linear equations (Juuso, 2004a). Constraints handling (Juuso, 2009a) and data-based analysis (Juuso and Lahdelma, 2010), improve possibilities to update the scaling functions recursively (Juuso, 2011). The LE approach together with knowledge-based systems, neural networks and evolutionary computation form the computational intelligence part (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Multimodel Simulation in Decomposed Systems

Juuso¹

2018

Linköping Electronic Conference Proceedings

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Intelligent methodologies provide a good basis for multimodel simulation. Small, specialised systems have a large number of feasible solutions, but developing truly adaptive, and still understandable, systems for highly complex systems require domain expertise and more compact approaches at the basic level. The nonlinear scaling approach extends the application areas of linear methodologies to nonlinear modelling and reduces the need for decomposition with local models. Fuzzy set systems provide a good basis for understandable models for decomposed systems. Data-based methodologies are suitable for developing these adaptive applications via the following steps: variable analysis, linear models and intelligent extensions. Complex problems are solved level by level to keep the domain expertise as an essential part of the solution.

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Section: Genetic Tuningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Multimodel Simulation in Decomposed Systems

Juuso¹

2018

Linköping Electronic Conference Proceedings

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“…The feasible range is defined as a trapezoidal membership function defined by support and core areas, see [26]. The scaling functions are monotonously increasing throughout the feasible range, see [22,27]. This is satisfied if the coefficients, are restricted to the range .…”

Section: Nonlinear Scalingmentioning

confidence: 99%

“…The constraints are taken into account by moving the corner points or the upper and lower limits if needed. The systems can be tuned with genetic algorithms [27].…”

Section: Nonlinear Scalingmentioning

confidence: 99%

Intelligent Performance Analysis with a Natural Language Interface

Juuso¹

2017

Management Systems in Production Engineering

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Abstract:Performance improvement is taken as the primary goal in the asset management. Advanced data analysis is needed to efficiently integrate condition monitoring data into the operation and maintenance. Intelligent stress and condition indices have been developed for control and condition monitoring by combining generalized norms with efficient nonlinear scaling. These nonlinear scaling methodologies can also be used to handle performance measures used for management since management oriented indicators can be presented in the same scale as intelligent condition and stress indices. Performance indicators are responses of the process, machine or system to the stress contributions analyzed from process and condition monitoring data. Scaled values are directly used in intelligent temporal analysis to calculate fluctuations and trends. All these methodologies can be used in prognostics and fatigue prediction. The meanings of the variables are beneficial in extracting expert knowledge and representing information in natural language. The idea of dividing the problems into the variable specific meanings and the directions of interactions provides various improvements for performance monitoring and decision making. The integrated temporal analysis and uncertainty processing facilitates the efficient use of domain expertise. Measurements can be monitored with generalized statistical process control (GSPC) based on the same scaling functions.

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“…The feasible range is defined by a membership function, and membership functions for finer partitions can be generated from membership definitions (Juuso et al, 1993). The basic scaling approach presented in (Juuso, 2004) has been improved later: a new constraint handling was introduced in (Juuso, 2009b), and a new skewness based methodology was presented for signal processing in (Juuso and Lahdelma, 2010).…”

Section: Introductionmentioning

confidence: 99%

Recursive Dynamic Modelling in Changing Operating Conditions

Juuso¹

2015

Linköping Electronic Conference Proceedings

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Changing operating condition may require updates for the dynamic models. Recursive updates are needed especially when sufficient information about the new situations is not available. In machine diagnostics and prognostics, the analysis starts from good conditions and new phenomena activated with time may considerably change the model. In biological wastewater treatment processes, the condition of the biomass drastically changes during the operation of the treatment process. Measurents based on the image analysis of the biomass condition is recently included. Recursive modelling is clearly needed in these situations. The usual approach is to modify the model equations. However, the interactions do not necessarily change if the meanings of the variables are modified. This paper keeps the model equations constant and modifies the nonlinear scaling of the variables by extending the data-driven scaling to a recursive approach. The recursive methodology is tested in two applications: machine diagnostics and wastewater treatment.

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Tuning of Large-Scale Linguistic Equation (LE) Models with Genetic Algorithms

Cited by 18 publications

References 5 publications

Intelligent Multimodel Simulation in Decomposed Systems

Intelligent Multimodel Simulation in Decomposed Systems

Intelligent Performance Analysis with a Natural Language Interface

Recursive Dynamic Modelling in Changing Operating Conditions

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