Journal ArticleDOI
A review of artificial neural networks applications in microwave computer‐aided design (invited article)
Reads0
Chats0
TLDR
Some of their most significant applications and typical issues arising in practical implementation are illustrated and use of self-organizing maps enhancing model accuracy and applicability is introduced.Abstract:
Neural networks found significant applications in microwave CAD. In this paper, after providing a brief description of neural networks employed so far in this context, we illustrate some of their most significant applications and typical issues arising in practical implementation. We also summarize current research tendencies and introduce use of self-organizing maps enhancing model accuracy and applicability. We conclude considering some future developments and exciting perspectives opened from use of neural networks in microwave CAD. ©1999 John Wiley & Sons, Inc. Int J RF and Microwave CAE 9: 158–174, 1999.read more
Citations
More filters
Journal ArticleDOI
EM-based optimization of microwave circuits using artificial neural networks: the state-of-the-art
Abstract: This paper reviews the current state-of-the-art in electromagnetic (EM)-based design and optimization of microwave circuits using artificial neural networks (ANNs). Measurement-based design of microwave circuits using ANNs is also reviewed. The conventional microwave neural optimization approach is surveyed, along with typical enhancing techniques, such as segmentation, decomposition, hierarchy, design of experiments, and clusterization. Innovative strategies for ANN-based design exploiting microwave knowledge are reviewed, including neural space-mapping methods. The problem of developing synthesis neural networks is treated. EM-based statistical analysis and yield optimization using neural networks is reviewed. The key issues in transient EM-based design using neural networks are summarized. The use of ANNs to speed up "global modeling" for EM-based design of monolithic microwave integrated circuits is briefly described. Future directions in ANN techniques to microwave design are suggested.
Journal ArticleDOI
Neuromodeling of microwave circuits exploiting space-mapping technology
TL;DR: In this paper, the authors presented modeling of microwave circuits using artificial neural networks (ANN's) based on space-mapping (SM) technology, which decrease the cost of training, improve generalization ability, and reduce the complexity of the ANN topology with respect to the classical neuromodeling approach.
Journal ArticleDOI
Smart Modeling of Microwave Devices
TL;DR: This work has described neural networks for microwave modeling and design and demonstrated that neural networks are helpful in developing parametric or scalable models for passive and active microwave devices.
Journal ArticleDOI
Parametric Modeling of EM Behavior of Microwave Components Using Combined Neural Networks and Pole-Residue-Based Transfer Functions
TL;DR: An advanced technique to develop combined neural network and pole-residue-based transfer function models for parametric modeling of electromagnetic (EM) behavior of microwave components and can obtain better accuracy in challenging applications involving high dimension of geometric parameter space and large geometrical variations, compared with conventional modeling methods.
Journal ArticleDOI
Prediction of Blast-Induced Ground Vibration in an Open-Pit Mine by a Novel Hybrid Model Based on Clustering and Artificial Neural Network
TL;DR: The proposed HKM–ANN model was the most superior model in estimating PPV caused by blasting operations in this study and contributed a new computational model in predicting blast-induced PPV for the science community and practical engineering with high accuracy level.
References
More filters
Journal ArticleDOI
A new look at the statistical model identification
TL;DR: In this article, a new estimate minimum information theoretical criterion estimate (MAICE) is introduced for the purpose of statistical identification, which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure.
Journal ArticleDOI
Multilayer feedforward networks are universal approximators
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.
Journal ArticleDOI
Neural networks and physical systems with emergent collective computational abilities
TL;DR: A model of a system having a large number of simple equivalent components, based on aspects of neurobiology but readily adapted to integrated circuits, produces a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size.
Journal ArticleDOI
A logical calculus of the ideas immanent in nervous activity
Warren S. McCulloch,Walter Pitts +1 more
TL;DR: In this article, it is shown that many particular choices among possible neurophysiological assumptions are equivalent, in the sense that for every net behaving under one assumption, there exists another net which behaves under another and gives the same results, although perhaps not in the same time.
Journal ArticleDOI
Approximation by superpositions of a sigmoidal function
TL;DR: It is demonstrated that finite linear combinations of compositions of a fixed, univariate function and a set of affine functionals can uniformly approximate any continuous function ofn real variables with support in the unit hypercube.