gradient descent with momentum and learning rate backpropagation algorithm Search Results


feed forward neural networks with backpropagation  (MathWorks Inc)
90
MathWorks Inc feed forward neural networks with backpropagation
Feed Forward Neural Networks With Backpropagation, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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implementation of the ray backpropagation method  (MathWorks Inc)
90
MathWorks Inc implementation of the ray backpropagation method
Estimates of range and depth of a simulated source at 10 m depth, between a 100 and 900 m range from the receiver. The <t> backpropagation </t> method was applied to the four echoes, the earlier two echoes (1& 2) and the last two echoes (1& 2). The source image method was applied to the earlier two echoes. The range and depth estimates were obtained using estimates of the elevation angles from simulated data with 5 dB SNR.
Implementation Of The Ray Backpropagation Method, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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matlab software  (MathWorks Inc)
90
MathWorks Inc matlab software
Estimates of range and depth of a simulated source at 10 m depth, between a 100 and 900 m range from the receiver. The <t> backpropagation </t> method was applied to the four echoes, the earlier two echoes (1& 2) and the last two echoes (1& 2). The source image method was applied to the earlier two echoes. The range and depth estimates were obtained using estimates of the elevation angles from simulated data with 5 dB SNR.
Matlab Software, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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matlab software - by Bioz Stars, 2026-04
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empirical signature of backpropagation-like deep learning  (Greene gmbh)
90
Greene gmbh empirical signature of backpropagation-like deep learning
Estimates of range and depth of a simulated source at 10 m depth, between a 100 and 900 m range from the receiver. The <t> backpropagation </t> method was applied to the four echoes, the earlier two echoes (1& 2) and the last two echoes (1& 2). The source image method was applied to the earlier two echoes. The range and depth estimates were obtained using estimates of the elevation angles from simulated data with 5 dB SNR.
Empirical Signature Of Backpropagation Like Deep Learning, supplied by Greene gmbh, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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empirical signature of backpropagation-like deep learning - by Bioz Stars, 2026-04
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multilayer perceptron, including backpropagation (mlbp)  (MathWorks Inc)
90
MathWorks Inc multilayer perceptron, including backpropagation (mlbp)
Estimates of range and depth of a simulated source at 10 m depth, between a 100 and 900 m range from the receiver. The <t> backpropagation </t> method was applied to the four echoes, the earlier two echoes (1& 2) and the last two echoes (1& 2). The source image method was applied to the earlier two echoes. The range and depth estimates were obtained using estimates of the elevation angles from simulated data with 5 dB SNR.
Multilayer Perceptron, Including Backpropagation (Mlbp), supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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neural network matlab 2023b  (MathWorks Inc)
90
MathWorks Inc neural network matlab 2023b
Estimates of range and depth of a simulated source at 10 m depth, between a 100 and 900 m range from the receiver. The <t> backpropagation </t> method was applied to the four echoes, the earlier two echoes (1& 2) and the last two echoes (1& 2). The source image method was applied to the earlier two echoes. The range and depth estimates were obtained using estimates of the elevation angles from simulated data with 5 dB SNR.
Neural Network Matlab 2023b, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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time delay neural network with an autoregressive version of the backpropagation algorithm  (New Brunswick Scientific)
90
New Brunswick Scientific time delay neural network with an autoregressive version of the backpropagation algorithm
Estimates of range and depth of a simulated source at 10 m depth, between a 100 and 900 m range from the receiver. The <t> backpropagation </t> method was applied to the four echoes, the earlier two echoes (1& 2) and the last two echoes (1& 2). The source image method was applied to the earlier two echoes. The range and depth estimates were obtained using estimates of the elevation angles from simulated data with 5 dB SNR.
Time Delay Neural Network With An Autoregressive Version Of The Backpropagation Algorithm, supplied by New Brunswick Scientific, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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backpropagation network  (Grainger Industrial)
90
Grainger Industrial backpropagation network
Estimates of range and depth of a simulated source at 10 m depth, between a 100 and 900 m range from the receiver. The <t> backpropagation </t> method was applied to the four echoes, the earlier two echoes (1& 2) and the last two echoes (1& 2). The source image method was applied to the earlier two echoes. The range and depth estimates were obtained using estimates of the elevation angles from simulated data with 5 dB SNR.
Backpropagation Network, supplied by Grainger Industrial, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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feedforward backpropagation neural network ffbp nn  (MathWorks Inc)
90
MathWorks Inc feedforward backpropagation neural network ffbp nn
Estimates of range and depth of a simulated source at 10 m depth, between a 100 and 900 m range from the receiver. The <t> backpropagation </t> method was applied to the four echoes, the earlier two echoes (1& 2) and the last two echoes (1& 2). The source image method was applied to the earlier two echoes. The range and depth estimates were obtained using estimates of the elevation angles from simulated data with 5 dB SNR.
Feedforward Backpropagation Neural Network Ffbp Nn, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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first-order feedforward backpropagator  (BlindSight GmbH)
90
BlindSight GmbH first-order feedforward backpropagator
(a) Network architecture for the Iowa Gambling Task simulation (see [2], simulation 3). The network consists of a first-order <t>feedforward</t> <t>backpropagator,</t> of which the hidden units feed forward into a set of second-order hidden units, which in turn feed forward into two wagering units. (b) Network architecture for the Blindsight and artificial grammar learning (AGL) simulations (see [2], simulations 1 and 2). The network consists of a first-order feedforward backpropagation autoassociator, of which the input and output units are connected through fixed weights to a second-order comparator, which in turn feeds forward into two wagering units.
First Order Feedforward Backpropagator, supplied by BlindSight GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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backpropagation neural network bpnn  (MathWorks Inc)
90
MathWorks Inc backpropagation neural network bpnn
(a) Network architecture for the Iowa Gambling Task simulation (see [2], simulation 3). The network consists of a first-order <t>feedforward</t> <t>backpropagator,</t> of which the hidden units feed forward into a set of second-order hidden units, which in turn feed forward into two wagering units. (b) Network architecture for the Blindsight and artificial grammar learning (AGL) simulations (see [2], simulations 1 and 2). The network consists of a first-order feedforward backpropagation autoassociator, of which the input and output units are connected through fixed weights to a second-order comparator, which in turn feeds forward into two wagering units.
Backpropagation Neural Network Bpnn, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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advances in neural information processing systems  (Curran Associates Inc)
90
Curran Associates Inc advances in neural information processing systems
(a) Network architecture for the Iowa Gambling Task simulation (see [2], simulation 3). The network consists of a first-order <t>feedforward</t> <t>backpropagator,</t> of which the hidden units feed forward into a set of second-order hidden units, which in turn feed forward into two wagering units. (b) Network architecture for the Blindsight and artificial grammar learning (AGL) simulations (see [2], simulations 1 and 2). The network consists of a first-order feedforward backpropagation autoassociator, of which the input and output units are connected through fixed weights to a second-order comparator, which in turn feeds forward into two wagering units.
Advances In Neural Information Processing Systems, supplied by Curran Associates Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Image Search Results


Estimates of range and depth of a simulated source at 10 m depth, between a 100 and 900 m range from the receiver. The backpropagation method was applied to the four echoes, the earlier two echoes (1& 2) and the last two echoes (1& 2). The source image method was applied to the earlier two echoes. The range and depth estimates were obtained using estimates of the elevation angles from simulated data with 5 dB SNR.

Journal: Sensors (Basel, Switzerland)

Article Title: Experimental Results of Underwater Cooperative Source Localization Using a Single Acoustic Vector Sensor

doi: 10.3390/s130708856

Figure Lengend Snippet: Estimates of range and depth of a simulated source at 10 m depth, between a 100 and 900 m range from the receiver. The backpropagation method was applied to the four echoes, the earlier two echoes (1& 2) and the last two echoes (1& 2). The source image method was applied to the earlier two echoes. The range and depth estimates were obtained using estimates of the elevation angles from simulated data with 5 dB SNR.

Article Snippet: Model-based source localization methods are, in general, not considered for real-time implementations, because of the time needed to compute the optimization procedure, which requires a large number of forward model runs, but a non-optimized Matlab implementation of the ray backpropagation method took less than 4 s in a current laptop.

Techniques:

Source localization results for the scenario of using the backpropagation method for source range and depth, 900m and 10 m, respectively and SNR = 5 dB: true source and receiver position (represented by the star) and backpropagated rays ( a ), ambiguity curves ( σ ) and source-localization plot considering four rays ( b ) and two rays ( c ). The arrows in plots ( b ) and ( c ) indicate the estimated source position (upper plots) and the corresponding minimum of the ambiguity curve (lower plots).

Journal: Sensors (Basel, Switzerland)

Article Title: Experimental Results of Underwater Cooperative Source Localization Using a Single Acoustic Vector Sensor

doi: 10.3390/s130708856

Figure Lengend Snippet: Source localization results for the scenario of using the backpropagation method for source range and depth, 900m and 10 m, respectively and SNR = 5 dB: true source and receiver position (represented by the star) and backpropagated rays ( a ), ambiguity curves ( σ ) and source-localization plot considering four rays ( b ) and two rays ( c ). The arrows in plots ( b ) and ( c ) indicate the estimated source position (upper plots) and the corresponding minimum of the ambiguity curve (lower plots).

Article Snippet: Model-based source localization methods are, in general, not considered for real-time implementations, because of the time needed to compute the optimization procedure, which requires a large number of forward model runs, but a non-optimized Matlab implementation of the ray backpropagation method took less than 4 s in a current laptop.

Techniques:

Source range and depth estimates at various instants of the Makai'05 field calibration event using the ray backpropagation method and the image method. The column marked, σ , represents the minimum of the square root value of the objective function used with the backpropagation method. The true source depth is 10 m. The estimated range values compare with the GPS fixes in <xref ref-type= Figure 10 ." width="100%" height="100%">

Journal: Sensors (Basel, Switzerland)

Article Title: Experimental Results of Underwater Cooperative Source Localization Using a Single Acoustic Vector Sensor

doi: 10.3390/s130708856

Figure Lengend Snippet: Source range and depth estimates at various instants of the Makai'05 field calibration event using the ray backpropagation method and the image method. The column marked, σ , represents the minimum of the square root value of the objective function used with the backpropagation method. The true source depth is 10 m. The estimated range values compare with the GPS fixes in Figure 10 .

Article Snippet: Model-based source localization methods are, in general, not considered for real-time implementations, because of the time needed to compute the optimization procedure, which requires a large number of forward model runs, but a non-optimized Matlab implementation of the ray backpropagation method took less than 4 s in a current laptop.

Techniques:

(a) Network architecture for the Iowa Gambling Task simulation (see [2], simulation 3). The network consists of a first-order feedforward backpropagator, of which the hidden units feed forward into a set of second-order hidden units, which in turn feed forward into two wagering units. (b) Network architecture for the Blindsight and artificial grammar learning (AGL) simulations (see [2], simulations 1 and 2). The network consists of a first-order feedforward backpropagation autoassociator, of which the input and output units are connected through fixed weights to a second-order comparator, which in turn feeds forward into two wagering units.

Journal: Philosophical Transactions of the Royal Society B: Biological Sciences

Article Title: Higher order thoughts in action: consciousness as an unconscious re-description process

doi: 10.1098/rstb.2011.0421

Figure Lengend Snippet: (a) Network architecture for the Iowa Gambling Task simulation (see [2], simulation 3). The network consists of a first-order feedforward backpropagator, of which the hidden units feed forward into a set of second-order hidden units, which in turn feed forward into two wagering units. (b) Network architecture for the Blindsight and artificial grammar learning (AGL) simulations (see [2], simulations 1 and 2). The network consists of a first-order feedforward backpropagation autoassociator, of which the input and output units are connected through fixed weights to a second-order comparator, which in turn feeds forward into two wagering units.

Article Snippet: The network consists of a first-order feedforward backpropagator, of which the hidden units feed forward into a set of second-order hidden units, which in turn feed forward into two wagering units. ( b ) Network architecture for the Blindsight and artificial grammar learning (AGL) simulations (see [ 2 ], simulations 1 and 2).

Techniques: