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Journal ArticleDOI

Design optimization of electromagnetic devices using artificial neural networks

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TLDR
The procedure explained can be used to provide good initial designs for use with iterative search techniques to reduce searching time and is highly desirable in connection with increasing the effectiveness of the optimal design procedure.
Abstract
A novel method for the optimal design of the electromagnetic devices is presented The method utilizes artificial neural networks (ANNs) in a design environment which encompasses numerical computations and expert's input for generating a variety of ANN training data Results of two implementation examples are provided The optimal design is obtained quickly (in a matter of milliseconds) once the ANNs are trained with a variety of geometrical topologies The procedure explained can be used to provide good initial designs for use with iterative search techniques (currently used) to reduce searching time This aspect is highly desirable in connection with increasing the effectiveness of the optimal design procedure >

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Citations
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Journal ArticleDOI

Stochastic algorithms in electromagnetic optimization

TL;DR: This paper gives an overview of some stochastic optimization strategies, namely, evolution strategies, genetic algorithms, and simulated annealing, and how these methods can be applied to problems in electrical engineering.
Journal ArticleDOI

Artificial neural networks in the solution of inverse electromagnetic field problems

TL;DR: It is shown that artificial neural networks have a role to play in a limited domain of applications and while it is ineffective to train networks to cover a broad class of devices, it is indeed possible to develop well-trained networks that function effectively over a narrow range of performance of a particular class of device.
Journal ArticleDOI

Utilizing genetic algorithms for the optimal design of electromagnetic devices

TL;DR: A new technique for the design optimization of electromagnetic devices that adopts the genetic algorithms (GAs) as the search method is presented, applied to the optimization of the shape of a pole face in an electric motor.
Journal ArticleDOI

Accelerated Antenna Design Methodology Exploiting Parameterized Cauchy Models

TL;DR: This methodology includes a rapidly-converging iterative scheme that produces an analytical model of the behavior of the antenna structure and sensitivity and tolerance analysis can be carried out without the need for further costly electromagnetic simulations.
Journal ArticleDOI

A Two-Level Genetic Algorithm for Electromagnetic Optimization

TL;DR: A two-level genetic algorithm (2LGA) for electromagnetic optimization that employs the global convergence properties of the genetic algorithm, where acceleration of the optimization results from the fast computations of the coarse model (low level) and where accuracy is guaranteed by using a limited number of fine model evaluations.
References
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Book ChapterDOI

Learning internal representations by error propagation

TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
Book

Dynamic Programming

TL;DR: The more the authors study the information processing aspects of the mind, the more perplexed and impressed they become, and it will be a very long time before they understand these processes sufficiently to reproduce them.
Journal ArticleDOI

A performance comparison of trained multilayer perceptrons and trained classification trees

TL;DR: It is concluded that there is not enough theoretical basis to demonstrate clear-cut superiority of one technique over the other in power-system load forecasting, power- system security prediction, and speaker-independent vowel recognition.
Journal ArticleDOI

Optimization problems in electromagnetics

J. Simkin, +1 more
TL;DR: A software environment for optimizing electromagnetic devices is described that can interactively construct a computer model for the device to be optimized, select the variables with their constraints and create a suitable objective function for minimization.
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