FEEDFORWARD NEURAL NETWORK WITH ADAPTIVE REFERENCE PATTERN LAYER

1999 ◽  
Vol 09 (01) ◽  
pp. 1-9
Author(s):  
MIKKO LEHTOKANGAS
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Reference Pattern ◽  
Hybrid Neural Network ◽  
Rbf Networks ◽  
Pattern Layer ◽  
Input Variables ◽  
Hidden Layer ◽  
Efficient Learning

A hybrid neural network architecture is investigated for modeling purposes. The proposed hybrid is based on the multilayer perceptron (MLP) network. In addition to the usual hidden layers, the first hidden layer is selected to be an adaptive reference pattern layer. Each unit in this new layer incorporates a reference pattern that is located somewhere in the space spanned by the input variables. The outputs of these units are the component wise-squared differences between the elements of a reference pattern and the inputs. The reference pattern layer has some resemblance to the hidden layer of the radial basis function (RBF) networks. Therefore the proposed design can be regarded as a sort of hybrid of MLP and RBF networks. The presented benchmark experiments show that the proposed hybrid can provide significant advantages over standard MLPs and RBFs in terms of fast and efficient learning, and compact network structure.

FAST LEARNING USING MULTILAYER PERCEPTRON NETWORKS WITH ADAPTIVE CENTROID LAYER

2000 ◽  
Vol 14 (02) ◽  
pp. 211-223
Author(s):  
MIKKO LEHTOKANGAS
Keyword(s):  
Multilayer Perceptron ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Hybrid Neural Network ◽  
Rbf Networks ◽  
Fast Learning ◽  
Input Variables ◽  
Hidden Layer ◽  
Efficient Learning ◽  
Multilayer Perceptron Networks

A hybrid neural network architecture is investigated for classification purposes. The proposed hybrid is based on the multilayer perceptron (MLP) network. In addition to the usual hidden layers the first hidden layer is selected to be an adaptive centroid layer. Each unit in this new layer incorporates a centroid vector that is located somewhere in the space spanned by the input variables. The output of these units is the Euclidean distance between the centroid vector and the inputs. The centroid layer has some resemblance to the hidden layer of the radial basis function (RBF) networks. Therefore the proposed design can be regarded as a sort of hybrid of the MLP and RBF networks. The presented benchmark experiments demonstrate that the proposed hybrid can provide significant advantages over standard MLPs in terms of fast and efficient learning, and compact network structure.

scholarly journals Image reconstruction through a multimode fiber with a simple neural network architecture

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Changyan Zhu ◽  
Eng Aik Chan ◽  
You Wang ◽  
Weina Peng ◽  
Ruixiang Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Reconstruction ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Training Time ◽  
Modal Dispersion ◽  
Multimode Fibers ◽  
Speckle Patterns ◽  
Hidden Layer ◽  
Simple Neural Network

AbstractMultimode fibers (MMFs) have the potential to carry complex images for endoscopy and related applications, but decoding the complex speckle patterns produced by mode-mixing and modal dispersion in MMFs is a serious challenge. Several groups have recently shown that convolutional neural networks (CNNs) can be trained to perform high-fidelity MMF image reconstruction. We find that a considerably simpler neural network architecture, the single hidden layer dense neural network, performs at least as well as previously-used CNNs in terms of image reconstruction fidelity, and is superior in terms of training time and computing resources required. The trained networks can accurately reconstruct MMF images collected over a week after the cessation of the training set, with the dense network performing as well as the CNN over the entire period.

scholarly journals Bioacoustic classification of avian calls from raw sound waveforms with an open-source deep learning architecture

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Francisco J. Bravo Sanchez ◽  
Md Rahat Hossain ◽  
Nathan B. English ◽  
Steven T. Moore
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Limited Data ◽  
Extraction Techniques ◽  
Neural Network Architecture ◽  
The Neural Network ◽  
Current Classification ◽  
Efficient Learning ◽  
Sound Features

AbstractThe use of autonomous recordings of animal sounds to detect species is a popular conservation tool, constantly improving in fidelity as audio hardware and software evolves. Current classification algorithms utilise sound features extracted from the recording rather than the sound itself, with varying degrees of success. Neural networks that learn directly from the raw sound waveforms have been implemented in human speech recognition but the requirements of detailed labelled data have limited their use in bioacoustics. Here we test SincNet, an efficient neural network architecture that learns from the raw waveform using sinc-based filters. Results using an off-the-shelf implementation of SincNet on a publicly available bird sound dataset (NIPS4Bplus) show that the neural network rapidly converged reaching accuracies of over 65% with limited data. Their performance is comparable with traditional methods after hyperparameter tuning but they are more efficient. Learning directly from the raw waveform allows the algorithm to select automatically those elements of the sound that are best suited for the task, bypassing the onerous task of selecting feature extraction techniques and reducing possible biases. We use publicly released code and datasets to encourage others to replicate our results and to apply SincNet to their own datasets; and we review possible enhancements in the hope that algorithms that learn from the raw waveform will become useful bioacoustic tools.

scholarly journals Identifikasi Penyakit Diabetes Millitus Menggunakan Jaringan Syaraf Tiruan Dengan Metode Perambatan-Balik (Backpropagation)

2018 ◽  
Author(s):  
Sutedi Sutedi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Accuracy Rate ◽  
Layer 2 ◽  
Input Layer ◽  
Hidden Layer ◽  
Diabetes Melitus

Diabetes Melitus (DM) is dangerous disease that affect many of the variouslayer of work society. This disease is not easy to accurately recognized by thegeneral society. So we need to develop a system that can identify accurately. Systemis built using neural networks with backpropagation methods and the functionactivation sigmoid. Neural network architecture using 8 input layer, 2 output layerand 5 hidden layer. The results show that this methods succesfully clasifies datadiabetics and non diabetics with near 100% accuracy rate.

scholarly journals Conservation laws in a neural network architecture: Enforcing the atom balance of a Julia-based photochemical model (v0.2.0)

2021 ◽  
Author(s):  
Patrick Obin Sturm ◽  
Anthony S. Wexler
Keyword(s):  
Neural Network ◽  
Conservation Laws ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Machine Precision ◽  
Emissions Scenarios ◽  
Hidden Layer ◽  
Computational Resources ◽  
Atmospheric Phenomena ◽  
Insight Into

Abstract. Models of atmospheric phenomena provide insight into climate, air quality, and meteorology, and provide a mechanism for understanding the effect of future emissions scenarios. To accurately represent atmospheric phenomena, these models consume vast quantities of computational resources. Machine learning (ML) techniques such as neural networks have the potential to emulate compute-intensive components of these models to reduce their computational burden. However, such ML surrogate models may lead to nonphysical predictions that are difficult to uncover. Here we present a neural network architecture that enforces conservation laws. Instead of simply predicting properties of interest, a physically interpretable hidden layer within the network predicts fluxes between properties which are subsequently related to the properties of interest. As an example, we design a physics-constrained neural network surrogate model of photochemistry using this approach and find that it conserves atoms as they flow between molecules to machine precision, while outperforming a naïve neural network in terms of accuracy and non-negativity of concentrations.

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