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1. |
Record Nr. |
UNINA9910739432703321 |
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Autore |
Leira Bernt J. |
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Titolo |
Optimal stochastic control schemes within a structural reliability framework / / by Bernt J. Leira |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2013 |
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ISBN |
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Edizione |
[1st ed. 2013.] |
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Descrizione fisica |
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1 online resource (102 p.) |
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Collana |
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SpringerBriefs in Statistics, , 2191-544X |
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Disciplina |
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Soggetti |
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Statistics |
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di contenuto |
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1.Introduction -- 2.Structural Limit States and Reliability Measures -- 3.Dynamic Structural Response Analysis and Probabilistic Representation -- 4.Categories of On-line Control Schemes Based on Structural Reliability Criteria -- 5. Example Applications Related to On-line Control Schemes -- 6. Conclusions. |
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Sommario/riassunto |
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The book addresses the topic of on-line implementation of structural and mechanical design criteria as an explicit part of optimal control schemes. The intention of the present research monograph is to reflect recent developments within this area. Examples of application of relevant control algorithms are included to illustrate their practical implementation. These examples are mainly taken from the area of marine technology with the multi-component external loading being represented as both varying in time and with magnitudes that are represented as statistical quantities. The relevant target group will be mechanical and structural engineers that are concerned with “smart components and structures” where optimal design principles and control actuators are combined. The book is also relevant for engineers e.g. involved in mechatronics and control applications. . |
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2. |
Record Nr. |
UNINA9910483998803321 |
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Titolo |
Evolutionary Machine Learning Techniques : Algorithms and Applications / / edited by Seyedali Mirjalili, Hossam Faris, Ibrahim Aljarah |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (287 pages) |
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Collana |
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Algorithms for Intelligent Systems, , 2524-7573 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Artificial intelligence |
Neural networks (Computer science) |
Computational Intelligence |
Artificial Intelligence |
Mathematical Models of Cognitive Processes and Neural Networks |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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This book provides an in-depth analysis of the current evolutionary machine learning techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy networks, modular neural networks, physical neural networks, and deep neural networks. The book provides essential definitions, literature reviews, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the pros and cons of classical training algorithms. It features a range of proven and recent nature-inspired algorithms used to train different types of artificial neural networks, including genetic algorithm, ant colony optimization, particle swarm |
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optimization, grey wolf optimizer, whale optimization algorithm, ant lion optimizer, moth flame algorithm, dragonfly algorithm, salp swarm algorithm, multi-verse optimizer, and sine cosine algorithm. The book also covers applications of the improved artificial neural networks to solve classification, clustering, prediction and regression problems in diverse fields. |
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