R. P. Johnson – författare
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3 produkter
3 produkter
Häftad, Engelska, 2004
1 187 kr
Skickas inom 5-8 vardagar
This book sets out the basic principles of composite construction with reference to beams, slabs, columns and frames, and their applications to building structures. It deals with the problems likely to arise in the design of composite members in buildings, and relates basic theory to the design approach of Eurocodes 2, 3 and 4. The new edition is based for the first time on the finalised Eurocode for steel/concrete composite structures.
Del 26 - Series In Machine Perception And Artificial Intelligence
Neural Network Training Using Genetic Algorithms
Inbunden, Engelska, 1996
488 kr
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The use of genetic algorithms as a training method for neural networks is described in this book. After introducing neural networks and genetic algorithms, it gives a number of examples to demonstrate the use of the proposed techniques. Moreover, a comparison of the results with the back-propagation algorithm is made.
Del 14 - Advances In Fuzzy Systems-applications And Theory
Automatic Generation Of Neural Network Architecture Using Evolutionary Computation
Inbunden, Engelska, 1997
995 kr
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This book describes the application of evolutionary computation in the automatic generation of a neural network architecture. The architecture has a significant influence on the performance of the neural network. It is the usual practice to use trial and error to find a suitable neural network architecture for a given problem. The process of trial and error is not only time-consuming but may not generate an optimal network. The use of evolutionary computation is a step towards automation in neural network architecture generation.An overview of the field of evolutionary computation is presented, together with the biological background from which the field was inspired. The most commonly used approaches to a mathematical foundation of the field of genetic algorithms are given, as well as an overview of the hybridization between evolutionary computation and neural networks. Experiments on the implementation of automatic neural network generation using genetic programming and one using genetic algorithms are described, and the efficacy of genetic algorithms as a learning algorithm for a feedforward neural network is also investigated.