An Indexed Bibliography of Genetic Algorithms with Fuzzy Logic

Alander, Jarmo T.

doi:10.1007/978-1-4615-6135-4_13

Cited by 20 publications

(14 citation statements)

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“…Specifically tailored for neural network input and structure selection are the evolution strategy (ES) proposed in [44] and the genetic algorithm (GA) presented in [404]; see also Chap. For fuzzy models, evolutionary algorithms are applied for input and rule structure selection as well [3,68,133,146,154,178,232,266,279,293,372]; see also Sect. For fuzzy models, evolutionary algorithms are applied for input and rule structure selection as well [3,68,133,146,154,178,232,266,279,293,372]; see also Sect.…”

Section: Choice Of the Model Inputsmentioning

confidence: 99%

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Nonlinear System Identification

Nelles

2001

1,092

447

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PrefaceThe goal of this book is to provide engineers and scientIsts in academia and industry with a thorough understanding of the underlying principles of nonlinear system identification. The reader will be able to apply the discussed models and methods to real problems with the necessary confidence and the awareness of potential difficulties that may arise in practice. This book is self-contained in the sense that it requires merely basic knowledge of matrix algebra, signals and systems, and statistics. Therefore, it also serves as an introduction to linear system identification and gives a practical overview on the major optimization methods used in engineering. The emphasis of this book is on an intuitive understanding of the subject and the practical application of the discussed techniques. It is not written in a theorem/proof style; rather the mathematics is kept to a minimum and the pursued ideas are illustrated by numerous figures, examples, and real-world applications.Fifteen years ago, nonlinear system identification was a field of several ad-hoc approaches, each applicable only to a very restricted class of systems. With the advent of neural networks, fuzzy models, and modern structure optimization techniques a much wider class of systems can be handled. Although one major characteristic of nonlinear systems is that almost every nonlinear system is unique, tools have been developed that allow the use of the same approach for a broad variety of systems. Certainly, a more problem-specific procedure typically promises superior performance, but from an industrial point of view a good tradeoff between development effort and performance is the decisive criterion for success. This book presents neural networks and fuzzy models together with major classical approaches in a unified framework. The strict distinction between the model architectures on the one hand (Part II) and the techniques for fitting these models to data on the other hand (Part I) tries to overcome the confusing mixture between both that is frequently encountered in the neuro and fuzzy literature. Nonlinear system identification is currently a field of very active research; many new methods will be developed and old methods will be refined. Nevertheless, I am confident that the underlying principles will continue to be valuable in the future.This book offers enough material for a two-semester course on optimization and nonlinear system identification. A higher level one-semester graduate course can focus on Chap. 7, the complete Part II, and Chaps. 17 to 21 in VI Preface Part III. For a more elementary one-semester course without prerequisites in optimization and linear system identification Chaps. 1 to 4 and 16 might be covered, while Chaps. 10, 14, 18, and 21 can be skipped. Alternatively, a course might omit the dynamic systems in Part III and instead emphasize the optimization techniques and nonlinear static modeling treated in Parts I and II. The applications presented in Part IV focus on a certain model architecture, and will convince th...

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Section: Choice Of the Model Inputsmentioning

confidence: 99%

“…3. Out of these three, linear optimization techniques are the most mature and most straightforward to apply.…”

Section: Overview Of Optimization Techniquesmentioning

confidence: 99%

Nonlinear System Identification

Nelles

2001

1,092

447

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“…In this area, Fu et al 19 provide a descriptive review of the most important approaches for simulation optimization, opportunely organized in a multi-categories classification framework. Additional classification frameworks and applications for simulation-based optimization can be found in many other research works, such as Azadivar, 20 Fogel, 21 Merkuryev and Visipkov, 22 Alander, 23 Carson and Maria, 24 Andradóttir, 25 Mollaghasemi et al, 26 Andradóttir 27 and Fulcher. 28 It is worth saying that simulation-based optimization has been successfully applied in different industry areas including both large companies in critical sectors (i.e., automotive, see for instance Ref.…”

Section: Introductionmentioning

confidence: 99%

An Application of Modeling and Simulation to Support Industrial Plants Design

Longo

Massei

Nicoletti³

2012

Int. J. Model. Simul. Sci. Comput.

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The focus of the research work presented in this article is the design of an industrial plant devoted to produce different types of hazelnuts-based products. The industrial plant design is initially based on a traditional approach (production process flow charts analysis and plant layout study) and it is then supported by a simulation model mainly used to investigate the system behavior in different operative scenarios. The industrial plant simulation model (called HAZIMUT, HAZelnuts Industrial plant design and management based on advanced simulation Models Utilization) recreates the entire hazelnuts production process (based on nine different production lines). The HAZIMUT simulation model is used to evaluate the effects -caused by multiple changes in the production lines capacities -on multiple performance measures based on machines levels utilization, work in process and production system productivity. The simulation results show how variations in production lines capacities generate an over-reaction of the system with major changes in some of the performance measures therefore stressing the importance to use the HAZIMUT simulator to tune the system correctly to improve the overall plant performances.approaches allow to acquire knowledge about the system under study, the results cannot be transferred to the real system because of the restrictive assumptions and simplifications often required for the mathematical formulation of the problem. An alternative approach, that, over the years, has strongly proved to be useful both as decision support tool and as problem solving methodology for real-world complex systems design is based on the use of Modeling and Simulation, M&S. 1 In effect, starting from the '50s, thanks to the advances in computer hardware and software, M&S has been extensively used in industry. As stated by Wiendahl and Worbs 2 and Fowler and Rose 3 simulation plays a key role as practical methodology for understanding the high-level dynamics that usually characterize complex manufacturing and production systems. M&S offers the possibility to generate reliable output results, to describe and analyze the behavior of existing systems while changing initial conditions and operative scenarios. Furthermore, during the last 60 years, a number of different simulation software tools have been developed and proposed; an overview of simulation software tools used for computer aided production engineering can be found in Ref. 4. According to Banks, 1 different features must be considered in choosing a simulation software package including input features, processing features, output features, environment features and finally cost features. To this end, Cochran and Chen 5 propose an approach based on fuzzy set for simulation software packages selection while Nikoukaran and Ray 6 provide a survey about methodologies for simulation software selection, techniques for evaluating simulation software, criteria used in evaluation and recommendations. Important guidelines about simulation software selection can also ...

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“…In these cases, the combination of fuzzy systems and evolutionary algorithms has proved to be very suitable. The literature in the field is extensive and the diversity of different combinations using fuzzy systems and evolutionary algorithms is considerable (see [3] for a bibliographic survey and [4], [5] for overviews of the field).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy system parameters discovery by bacterial evolutionary algorithm

Nawa

Furuhashi

1999

IEEE Trans. Fuzzy Syst.

179

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This paper presents a new method for discovering the parameters of a fuzzy system; namely, the combination of input variables of the rules, the parameters of the membership functions of the variables, and a set of relevant rules; from numerical data using the newly proposed bacterial evolutionary algorithm (BEA). In early work, the authors proposed the pseudobacterial genetic algorithm (PBGA) that incorporates a modified mutation operator called bacterial mutation, based on a biological phenomenon of microbial evolution. The BEA has the same features of the PBGA, but introduces a new operation, called gene transfer operation, equally inspired by a microbial evolution phenomenon. While the bacterial mutation performs local optimization within the limits of a single chromosome, the gene transfer operation allows the chromosomes to directly transfer information to the other counterparts in the population. The gene transfer is inspired by the phenomenon of transfer of strands of genes in a population of bacteria. By means of this mechanism, one bacterium can rapidly spread its genetic information to other cells. Numerical experiments were performed to show the effectiveness of the BEA. The obtained results show the benefits that can be obtained with this method.

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An Indexed Bibliography of Genetic Algorithms with Fuzzy Logic

Cited by 20 publications

References 87 publications

Nonlinear System Identification

Nonlinear System Identification

An Application of Modeling and Simulation to Support Industrial Plants Design

Fuzzy system parameters discovery by bacterial evolutionary algorithm

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