Analysis, synthesis, and diagnostics of antenna arrays through complex-valued neural networks

Bregáins, Julio; Ares, F.

doi:10.1002/mop.21706

Cited by 15 publications

(10 citation statements)

References 6 publications

(27 reference statements)

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“…Recently,processing data in some applications like signal processing [1], image processing [2] and communication [3], etc., are significantly complex-valued in nature. Hence, in order to preserve their physical characteristics, a nonlinear transformation is required for representing the data in the Complex domain.…”

Section: Introductionmentioning

confidence: 99%

A Meta-cognitive Fully Complex-valued Fast Learning Classifier for real-valued classification problems

Sivachitra¹,

Vijayachitra

2015

2015 International Conference on Cognitive Computing and Information Processing(CCIP)

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In this article, a Meta-cognitive Fully Complexvalued Fast Learning Classifier (Mc-FCFLC) for solving realvalued classification problems is presented. Mc-FCFLC consist of two components namely, a cognitive component and a metacognitive one. The cognitive component of Mc-FCFLC is a single hidden layer network (FCFLC) with a nonlinear input and hidden layer, and a linear output layer. The meta-cognitive component of Mc-FCFLC consist of a self-regulatory learning mechanism that chooses a best learning strategy among what-tolearn, when-to-learn and how -to-learn for a given sample. The sample is either deleted, used for adding a new neuron or else it is reserved for future use. Thus the architecture of Mc-FCFLC is constructed during the training process. The performance of the Mc-FCFLC is evaluated with the other complex-valued and a few best performing real-valued classifiers on a set of benchmark classification problems obtained from the UCI machine learning repository. Further, a practical acoustic emission signal classification problem has been addressed. Performance results demonstrate that Mc-FCFLC has better classification ability than the other classifiers existing in the literature.

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Section: Introductionmentioning

confidence: 99%

A Meta-cognitive Fully Complex-valued Fast Learning Classifier for real-valued classification problems

Sivachitra¹,

Vijayachitra

2015

2015 International Conference on Cognitive Computing and Information Processing(CCIP)

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“…The signals in the application areas such as signal processing [1], [2], image processing [3] and communication [4], etc., have a complex-valued nature and continuously changes with time. These signals are required to be represented in the Complex domain in order to preserve their physical uniqueness.…”

Section: Introductionmentioning

confidence: 99%

A Meta-cognitive Fully Complex Valued Functional Link predictor Network for solving real valued prediction problems

Sivachitra¹,

Vijayachitra

2015

2015 International Conference on Cognitive Computing and Information Processing(CCIP)

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In this paper,a Meta-cognitive Fully Complex Valued Functional Link predictor Network (Mc-FCFLNP) is developed for solving the complex practical problems. Mc-FCFLNP network contains two components, first, a cognitive and next a metacognitive component. A Fully Complex-valued Functional Link network (FCFLNP) acts as a cognitive component and its self directed learning mechanism acts as meta-cognitive component. As the network does not possess hidden layers, the multi-variable polynomials are used in the input layer for representing the non-linear relationship between the input and the output. When the sample is sent to the Mc-FCFLNP network for training, the meta-cognitive component chooses what-to-learn, when-to-learn, and how-to-learn depending on the knowledge attained by the FCFLNP network and the novelty of the sample. The network utilises the sequential learning methodology for eliminating the limitations existing with the batch learning strategy. The recursive least square (RLS) update is used for tuning the output weight of the network and the Orthogonal Least Square (OLS) principle is used for the selection of the best polynomial. A set of bench mark prediction problems are used for validating the proposed network. Performance comparison of the Mc-FCFLNP clearly shows a better prediction ability when compared with the other existing networks in the literature.

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“…C OMPLEX-VALUED signals naturally arise in many applications like communication [1], signal processing [2]- [5], image processing [6]- [9], and radar applications [10]. Representing these signals and their nonlinear transformations in the complex domain is a natural way to preserve their physical characteristics.…”

Section: Introductionmentioning

confidence: 99%

A Sequential Learning Algorithm for Complex-Valued Self-Regulating Resource Allocation Network-CSRAN

Sundaram

Ramasamy

Sundararajan

2011

IEEE Trans. Neural Netw.

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This paper presents a sequential learning algorithm for a complex-valued resource allocation network with a self-regulating scheme, referred to as complex-valued self-regulating resource allocation network (CSRAN). The self-regulating scheme in CSRAN decides what to learn, when to learn, and how to learn based on the information present in the training samples. CSRAN is a complex-valued radial basis function network with a sech activation function in the hidden layer. The network parameters are updated using a complex-valued extended Kalman filter algorithm. CSRAN starts with no hidden neuron and builds up an appropriate number of hidden neurons, resulting in a compact structure. Performance of the CSRAN is evaluated using a synthetic complex-valued function approximation problem, two real-world applications consisting of a complex quadrature amplitude modulation channel equalization, and an adaptive beam-forming problem. Since complex-valued neural networks are good decision makers, the decision-making ability of the CSRAN is compared with other complex-valued classifiers and the best performing real-valued classifier using two benchmark unbalanced classification problems from UCI machine learning repository. The approximation and classification results show that the CSRAN outperforms other existing complex-valued learning algorithms available in the literature.

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Analysis, synthesis, and diagnostics of antenna arrays through complex-valued neural networks

Cited by 15 publications

References 6 publications

A Meta-cognitive Fully Complex-valued Fast Learning Classifier for real-valued classification problems

A Meta-cognitive Fully Complex-valued Fast Learning Classifier for real-valued classification problems

A Meta-cognitive Fully Complex Valued Functional Link predictor Network for solving real valued prediction problems

A Sequential Learning Algorithm for Complex-Valued Self-Regulating Resource Allocation Network-CSRAN

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