Designing expert system for in situ toughened Si3N4 based on adaptive neural fuzzy inference system and genetic algorithms

Zeng, Qingfeng; Zhang, Litong; Xu, Yuanhui; Cheng, Laifei; Yan, Xiu-Tian; Jia-kui, ZU; Dai, Guanzhong

doi:10.1016/j.matdes.2008.05.008

Cited by 10 publications

(3 citation statements)

References 18 publications

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“…Many toolboxes can be found for different control problems, among which are the following: the dynamic powertrain simulation tool WARPSTAR2 for the modelling of electric vehicles [69]; a graphical tool aimed at controlling and monitoring temperature and relative humidity in the context of fine agriculture [70]; ASAFES2, a neurofuzzy function approximator, which combines the Takagi-Sugeno fuzzy reasoning method with stochastic reinforcement learning [71]; etc. Moreover, we can find some Matlab toolboxes for control applications, for instance: CIAPS [72] is an open source package based on artificial neural networks and fuzzy logic simulations for the assessment of electrical power systems; Zeng et al developed an expert system which combines ANFIS with genetic algorithms for designing in situ toughened Si 3 N 4 [73]; etc. Finally, several suites are available for fuzzy control, such as MEANDER [74], which evaluates the performance of agent-based systems.…”

Section: A Fuzzy Systems Software For Solving Specific Problems Of Cmentioning

confidence: 99%

A Survey of Fuzzy Systems Software: Taxonomy, Current Research Trends, and Prospects

Alcalá‐Fdez

Alonso

2016

IEEE Trans. Fuzzy Syst.

105

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Fuzzy systems have been used widely thanks to their ability to successfully solve a wide range of problems in different application fields. However, their replication and application requires a high level of knowledge and experience. Furthermore, few researchers publish the software and/or source code associated with their proposals, which is a major obstacle to scientific progress in other disciplines and in industry. In recent years, most fuzzy system software has been developed in order to facilitate the use of fuzzy systems. Some software is commercially distributed but most software is available as free and open source software, reducing such obstacles and providing many advantages: quicker detection of errors, innovative applications, faster adoption of fuzzy systems, etc. In this paper, we present an overview of freely available and open source fuzzy systems software in order to provide a well-established framework that helps researchers to find existing proposals easily and to develop well founded future work. To accomplish this, we propose a twolevel taxonomy and we describe the main contributions related to each field. Moreover, we provide a snapshot of the status of the publications in this field according to the ISI Web of Knowledge. Finally, some considerations regarding recent trends and potential research directions are presented.

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Section: A Fuzzy Systems Software For Solving Specific Problems Of Cmentioning

confidence: 99%

A Survey of Fuzzy Systems Software: Taxonomy, Current Research Trends, and Prospects

Alcalá‐Fdez

Alonso

2016

IEEE Trans. Fuzzy Syst.

105

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“…Few other GA-ANN combined studies can also be found in literature. [240][241][242][243][244][245][246][247][248][249][250][251][252][253][254] Genetic algorithms are also used for glass applications, 255 optics calculation and beam line design, 256 automated instrument design, 257 electronic beam brightness optimisation, 258 automotive body assembly, 259 material design in multiphase material, 260 dynamic multi-objective optimisation for machining gradient materials, 261 laser precision drilling, 262 material symmetry, 263 etc. Many such works from diverse areas are simply a matter of time.…”

Section: Other Applicationsmentioning

confidence: 99%

Genetic algorithms in polymeric material production, design, processing and other applications: a review

Mitra¹

2008

International Materials Reviews

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Genetic algorithms (GAs) belong to the broader family of generic population based metaheuristic optimisation algorithms called evolutionary algorithms. These are one of the biology inspired computing techniques that emulate the basic Darwinian concept of natural selection. Because of their many positive attributes such as ease of use, versatility and robustness, GAs have become important in materials research very rapidly since their inception. Though going through a major stage of theoretical development, GAs have been successfully applied in many fields such as polymer production, polymer design, polymer processing, polymer laminates and other polymers, as well as non-polymer applications in recent times. The present review attempts to present the state of the art in this area. The review is broadly divided into three sections: fundamentals of GAs, the use of GAs in polymeric materials and the use of GAs in non-polymer applications. First, the fundamentals of GAs are presented to provide some glimpse of the technique; this is followed by presentations on applications of GAs in various polymeric fields, such as in polymer production, polymer scheduling, polymer design, polymer laminates and other polymer related applications and non-polymeric fields, e.g. metallic laminates and other metallic material applications. A thorough literature review and critical analysis of the review is provided and attempts have been made to portray how evolutionary optimisation techniques, GAs in particular, are used across a variety of applications.

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“…Fuzzy neural networks combine fuzzy concepts and fuzzy inference rules with the architecture and learning of neural networks, and have been successfully applied in system identification [18], intelligent control [19][20][21], pattern classification [22] and expert system [23,24], etc. Since IFSs have proved to be more powerful to deal with vagueness and uncertainty than fuzzy sets, some researchers have also investigated the combination of IFSs and artificial neural networks [25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Intuitionistic Fuzzy Hopfield Neural Network and Its Stability

Li¹,

Yang²,

Wu³

2011

NNW

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Nowadays, remote sensing technology is being used as an essential tool for monitoring and detecting oil spills to take precautions and to prevent the damages to the marine environment. As an important branch of remote sensing, satellite based synthetic aperture radar imagery (SAR) is the most effective way to accomplish these tasks. Since a marine surface with oil spill seems as a dark object because of much lower backscattered energy, the main problem is to recognize and differentiate the dark objects of oil spills from others to be formed by oceanographic and atmospheric conditions. In this study, Radarsat-1 images covering Lebanese coasts were employed for oil spill detection. For this purpose, a powerful classifier, Artificial Neural Network Multilayer Perceptron (ANN MLP) was used. As the original contribution of the paper, the network was trained by a novel heuristic optimization algorithm known as Artificial Bee Colony (ABC) method besides the conventional Backpropagation (BP) and Levenberg-Marquardt (LM) learning algorithms. A comparison and evaluation of different network training algorithms regarding reliability of detection and robustness show that for this problem best result is achieved with the Artificial Bee Colony algorithm (ABC).

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Designing expert system for in situ toughened Si3N4 based on adaptive neural fuzzy inference system and genetic algorithms

Cited by 10 publications

References 18 publications

A Survey of Fuzzy Systems Software: Taxonomy, Current Research Trends, and Prospects

A Survey of Fuzzy Systems Software: Taxonomy, Current Research Trends, and Prospects

Genetic algorithms in polymeric material production, design, processing and other applications: a review

Intuitionistic Fuzzy Hopfield Neural Network and Its Stability

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