Recognition ability of the fully connected Hopfield neural network under a persistent stimulus field

2009 ◽  
Vol 388 (7) ◽  
pp. 1279-1288 ◽  
Author(s):  
V.M. Vieira ◽  
M.L. Lyra ◽  
C.R. da Silva
Keyword(s):  
Neural Network ◽  
Hopfield Neural Network ◽  
Stimulus Field ◽  
Recognition Ability ◽  
Fully Connected

scholarly journals Mapping Hebbian Learning Rules to Coupling Resistances for Oscillatory Neural Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Corentin Delacour ◽  
Aida Todri-Sanial
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Hebbian Learning ◽  
Hopfield Neural Network ◽  
Analytical Framework ◽  
Relaxation Oscillators ◽  
Oscillatory Neural Network ◽  
Network Functionality ◽  
Fully Connected ◽  
Neuromorphic Architecture

Oscillatory Neural Network (ONN) is an emerging neuromorphic architecture with oscillators representing neurons and information encoded in oscillator's phase relations. In an ONN, oscillators are coupled with electrical elements to define the network's weights and achieve massive parallel computation. As the weights preserve the network functionality, mapping weights to coupling elements plays a crucial role in ONN performance. In this work, we investigate relaxation oscillators based on VO2 material, and we propose a methodology to map Hebbian coefficients to ONN coupling resistances, allowing a large-scale ONN design. We develop an analytical framework to map weight coefficients into coupling resistor values to analyze ONN architecture performance. We report on an ONN with 60 fully-connected oscillators that perform pattern recognition as a Hopfield Neural Network.

scholarly journals Optimal CNN–Hopfield Network for Pattern Recognition Based on a Genetic Algorithm

Algorithms ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 11
Author(s):  
Fekhr Eddine Keddous ◽  
Amir Nakib
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Hopfield Neural Network ◽  
Representation Learning ◽  
Problem Formulation ◽  
Hopfield Network ◽  
Learning Capabilities ◽  
A Chain ◽  
Fully Connected ◽  
Real World Problems

Convolutional neural networks (CNNs) have powerful representation learning capabilities by automatically learning and extracting features directly from inputs. In classification applications, CNN models are typically composed of: convolutional layers, pooling layers, and fully connected (FC) layer(s). In a chain-based deep neural network, the FC layers contain most of the parameters of the network, which affects memory occupancy and computational complexity. For many real-world problems, speeding up inference time is an important matter because of the hardware design implications. To deal with this problem, we propose the replacement of the FC layers with a Hopfield neural network (HNN). The proposed architecture combines both a CNN and an HNN: A pretrained CNN model is used for feature extraction, followed by an HNN, which is considered as an associative memory that saves all features created by the CNN. Then, to deal with the limitation of the storage capacity of the HNN, the proposed work uses multiple HNNs. To optimize this step, the knapsack problem formulation is proposed, and a genetic algorithm (GA) is used solve it. According to the results obtained on the Noisy MNIST Dataset, our work outperformed the state-of-the-art algorithms.

scholarly journals A COMPENSATED FUZZY HOPFIELD NEURAL NETWORK FOR CODEBOOK DESIGN IN VECTOR QUANTIZATION

2000 ◽  
Vol 14 (08) ◽  
pp. 1067-1079 ◽  
Author(s):  
SHAO-HAN LIU ◽  
JZAU-SHENG LIN
Keyword(s):  
Neural Network ◽  
Vector Quantization ◽  
Energy Function ◽  
Global Minimum ◽  
Parallel Implementation ◽  
Hopfield Neural Network ◽  
Optimal Result ◽  
Fuzzy C Means ◽  
Codebook Design ◽  
Fully Connected

In this paper, a new Hopfield-model net called Compensated Fuzzy Hopfield Neural Network (CFHNN) is proposed for vector quantization in image compression. In CFHNN, the compensated fuzzy c-means algorithm, modified from penalized fuzzy c-means, is embedded into Hopfield neural network so that the parallel implementation for codebook design is feasible. The vector quantization can be cast as an optimal problem that may also be regarded as a minimization of a criterion defined as a function of the average distortion between training vector and codevector. The CFHNN is trained to classify the divided vectors on a real image into feasible class to generate an available codebook when the defined energy function converges to near global minimum. The training vectors on a divided image are mapped to a two-dimensional Hopfield neural network. Also the compensated fuzzy c-means technique is used to update the quantization performance and to eliminate searching for the weighting factors. In the context of vector quantization, each training vector on the divided image is represented by a neuron which is fully connected by the other neurons. After a number of iterations, neuron states are refined to reach near optimal result when the defined energy function is converged.

KLASIFIKASI CITRA GENUS PANTHERA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)

2019 ◽  
Vol 24 (3) ◽  
pp. 220-228
Author(s):  
Gusti Alfahmi Anwar ◽  
Desti Riminarsih
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Input Layer ◽  
Fully Connected

Panthera merupakan genus dari keluarga kucing yang memiliki empat spesies popular yaitu, harimau, jaguar, macan tutul, singa. Singa memiliki warna keemasan dan tidak memilki motif, harimau memiliki motif loreng dengan garis-garis panjang, jaguar memiliki tubuh yang lebih besar dari pada macan tutul serta memiliki motif tutul yang lebih lebar, sedangkan macan tutul memiliki tubuh yang sedikit lebih ramping dari pada jaguar dan memiliki tutul yang tidak terlalu lebar. Pada penelitian ini dilakukan klasifikasi genus panther yaitu harimau, jaguar, macan tutul, dan singa menggunakan metode Convolutional Neural Network. Model Convolutional Neural Network yang digunakan memiliki 1 input layer, 5 convolution layer, dan 2 fully connected layer. Dataset yang digunakan berupa citra harimau, jaguar, macan tutul, dan singa. Data training terdiri dari 3840 citra, data validasi sebanyak 960 citra, dan data testing sebanyak 800 citra. Hasil akurasi dari pelatihan model untuk training yaitu 92,31% dan validasi yaitu 81,88%, pengujian model menggunakan dataset testing mendapatan hasil 68%. Hasil akurasi prediksi didapatkan dari nilai F1-Score pada pengujian didapatkan sebesar 78% untuk harimau, 70% untuk jaguar, 37% untuk macan tutul, 74% untuk singa. Macan tutul mendapatkan akurasi terendah dibandingkan 3 hewan lainnya tetapi lebih baik dibandingkan hasil penelitian sebelumnya.

Application of Hopfield neural network in unit commitment problem

2009 ◽  
Vol 29 (4) ◽  
pp. 1028-1031
Author(s):  
Wei-xin GAO ◽  
Xiang-yang MU ◽  
Nan TANG ◽  
Hong-liang YAN
Keyword(s):  
Neural Network ◽  
Unit Commitment ◽  
Hopfield Neural Network ◽  
Unit Commitment Problem ◽  
Commitment Problem
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