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15
Competitive Learning Algorithms for Robust Vector Quantization
, 1998
"... The efficient representation and encoding of signals with limited resources, e.g., finite storage capacity and restricted transmission bandwidth, is a fundamental problem in technical as well as biological information processing systems. Typically, under realistic circumstances, the encoding and com ..."
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Cited by 24 (1 self)
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The efficient representation and encoding of signals with limited resources, e.g., finite storage capacity and restricted transmission bandwidth, is a fundamental problem in technical as well as biological information processing systems. Typically, under realistic circumstances, the encoding and communication of messages has to deal with different sources of noise and disturbances. In this paper, we propose a unifying approach to data compression by robust vector quantization, which explicitly deals with channel noise, bandwidth limitations, and random elimination of prototypes. The resulting algorithm is able to limit the detrimental effect of noise in a very general communication scenario. In addition, the presented model allows us to derive a novel competitive neural networks algorithm, which covers topology preserving feature maps, the socalled neuralgas algorithm, and the maximum entropy softmax rule as special cases. Furthermore, continuation methods based on these noise models impr...
Neural Fuzzy Techniques in Vehicle Acoustic Signal Classification
, 1997
"... Vehicle acoustic signals have long been considered as unwanted traffic noise. In this research acoustic signals generated by each vehicle will be used to detect its presence and classify its type. Circular arrays of microphones were designed and built to detect desired signals and suppress unwanted ..."
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Cited by 8 (0 self)
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Vehicle acoustic signals have long been considered as unwanted traffic noise. In this research acoustic signals generated by each vehicle will be used to detect its presence and classify its type. Circular arrays of microphones were designed and built to detect desired signals and suppress unwanted ones. Circular arrays with multiple rings have an interesting and important property that is constant sidelobe levels. A modified genetic algorithm that can work directly with real numbers is used in the circular array design. It offers more effective ways to solve numerical problems than a standard genetic algorithm. In classifier
I FEBAM: A FeatureExtracting Bidirectional Associative Memory
"... Abstract—In this paper, a new model that can ultimately create its own set of perceptual features is proposed. Using a bidirectional associative memory (BAM)inspired architecture, the resulting model inherits properties such as attractorlike behavior and successful processing of noisy inputs, whil ..."
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Cited by 3 (3 self)
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Abstract—In this paper, a new model that can ultimately create its own set of perceptual features is proposed. Using a bidirectional associative memory (BAM)inspired architecture, the resulting model inherits properties such as attractorlike behavior and successful processing of noisy inputs, while being able to achieve principal component analysis (PCA) tasks such as feature extraction and dimensionality reduction. The model is tested by simulating image reconstruction and blind source separation tasks. Simulations show that the model fares particularly well compared to current neural PCA and independent component analysis (ICA) algorithms. It is argued the model possesses more cognitive explanative power than any other nonlinear/linear PCA and ICA algorithm.
Electric Voltage Control as an Implementation of Neural Network Applications 1
"... Abstract: Present study was proposed the monitoring of mathematical model of electric voltage source with using neural network for application in control systems as sensor and command signal. The monitoring system, consist of toroidal choke or transformer with high saturated ferromagnetic cores. The ..."
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Abstract: Present study was proposed the monitoring of mathematical model of electric voltage source with using neural network for application in control systems as sensor and command signal. The monitoring system, consist of toroidal choke or transformer with high saturated ferromagnetic cores. The input information we receive from current periodic curves. The current was distributed into Fourier or walsh series. The combination of these harmonics and their amplitude values determine monitoring voltage value directly. For increase of this system precision, the mathematical model was constructed on basis of partial differential quasistationary electromagnetic field equations and ordinary differential electromagnetic circuit equations combination. Key words: Artificial neural network, differential equations, computer science, electric control, information monitoring
ftp ejde.math.txstate.edu (login: ftp) EXISTENCE OF NONOSCILLATORY SOLUTIONS TO HIGHERORDER MIXED DIFFERENCE EQUATIONS
"... Abstract. In this paper, we consider the higher order neutral nonlinear difference equation ∆ m (x(n) + p(n)x(τ(n))) + f1(n, x(σ1(n))) − f2(n, x(σ2(n))) = 0, ∆ m (x(n) + p(n)x(τ(n))) + f1(n, x(σ1(n))) − f2(n, x(σ2(n))) = g(n), ∆ m (x(n) + p(n)x(τ(n))) + lX bi(n)x(σi(n)) = 0. i=1 We obtain suffi ..."
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Abstract. In this paper, we consider the higher order neutral nonlinear difference equation ∆ m (x(n) + p(n)x(τ(n))) + f1(n, x(σ1(n))) − f2(n, x(σ2(n))) = 0, ∆ m (x(n) + p(n)x(τ(n))) + f1(n, x(σ1(n))) − f2(n, x(σ2(n))) = g(n), ∆ m (x(n) + p(n)x(τ(n))) + lX bi(n)x(σi(n)) = 0. i=1 We obtain sufficient conditions for the existence of nonoscillatory solutions. Consider the difference equations 1.
Proc. IASTED/AAAI Conf. on Artificial Intelligence and Soft Computing, Cancn, Mxico, 2730 May, p.431434.
"... In this paper we propose two neural algorithms that can be considered a simplification and a generalization of the Differential Competitive Learning (DCL) neural network, respectively. Firstly, we suggest some simplifications for the original DCL model to eliminate redundant aspects of the competiti ..."
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In this paper we propose two neural algorithms that can be considered a simplification and a generalization of the Differential Competitive Learning (DCL) neural network, respectively. Firstly, we suggest some simplifications for the original DCL model to eliminate redundant aspects of the competition mechanism. We get rid of the lateral connections arguing that it is possible because the winning neuron is chosen based solely on metrical similarity measures and the lateral feedback weights play no effective role. The activation rule is made simpler requiring less computational effort. In the second model, we show how to combine lateral connections with metrical relations on the activation and the learning rules of DCL to effectively estimate cluster centroids. This model is also less sensitive to weight initialization. A number of simulations are carried out to compare the presented models in unsupervised clustering tasks.
unknown title
, 1994
"... sets and systems Fuzzy adaptive learning control network with online neural learning s ..."
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sets and systems Fuzzy adaptive learning control network with online neural learning s
Summary
, 2007
"... . This paper presents brief study mainly focused on the modeling aspect of the transfer function of the primitive element of a neuron. Then, the transfer function is drawn for an investigation towards its analysis and classification. This analysis has proposed nine different cases of proper modes. T ..."
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. This paper presents brief study mainly focused on the modeling aspect of the transfer function of the primitive element of a neuron. Then, the transfer function is drawn for an investigation towards its analysis and classification. This analysis has proposed nine different cases of proper modes. The paper from another view of study discusses the main attributes needed in neural structures to yield sufficient correlation between the activation function classification and the different aspects of structural view of the networking purposes. The correlation had been decided on the bases of the resulted outcomes of conducted experiments.
Creating Perceptual Features Using a BAMinspired Architecture
"... In this paper, it shown that the FeatureExtracting Bidirectional Associative Memory (FEBAM) can create its own set of perceptual features. Using a bidirectional associative memory (BAM)inspired architecture, FEBAM inherits properties such as attractorlike behavior and successful processing of noi ..."
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In this paper, it shown that the FeatureExtracting Bidirectional Associative Memory (FEBAM) can create its own set of perceptual features. Using a bidirectional associative memory (BAM)inspired architecture, FEBAM inherits properties such as attractorlike behavior and successful processing of noisy inputs, while being able to achieve principal component analysis (PCA) tasks such as feature extraction. The model is tested by simulating prototype development in a noisy environment. Simulations show that the model fares particularly well compared to current neural PCA and independent component analysis (ICA) algorithms. Therefore, it is argued that the model possesses more cognitive explanative power than any other PCA/ICA algorithm or BAMs taken separately.