Application of stochastic real-valued reinforcement neural networks to batch production rescheduling

1997 ◽  
Vol 211 (8) ◽  
pp. 591-603 ◽  
Author(s):  
M I Heywood ◽  
M-C Chan ◽  
C R Chatwin
Keyword(s):  
Neural Network ◽  
Cross Section ◽  
Network Architecture ◽  
Hybrid Network ◽  
Neural Network Architecture ◽  
Batch Production ◽  
Worst Case ◽  
Hybrid Neural Network ◽  
Definition Of ◽  
Selection Of

This paper details the design and application of a hybrid neural network architecture for the rescheduling problem of batch manufacture. Design issues include the selection of an appropriate neural network paradigm, specification of the network architecture and support for multistep prediction. Application issues include decoupling the network dimension from that of the problem and the definition of suitable rescheduling operators. The ensuing hybrid network is tested against heuristics previously identified as typically representing estimates for best and worst case performance within a cross-section of batch rescheduling problems.

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.

Features of the Development of a Neural Network to Automate the Recognition of Steel Defects

2020 ◽  
Vol 11 (6) ◽  
pp. 330-334
Author(s):  
R. A. Karelova ◽  
◽  
E. E. Ignatov ◽  
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Cross Entropy ◽  
Neural Network Architecture ◽  
Development Environment ◽  
Steel Sheets ◽  
Python Language ◽  
The Neural Network ◽  
Metal Sheets ◽  
Selection Of

The article presents an embodiment of an artificial neural network for recognizing defects in images of steel sheets. Several stages of solving the problem are described: the choice of a development environment, a programming language, and libraries necessary for the implementation; features of data analysis, graphing, histograms, finding dependencies; the selection of a suitable neural network, the choice of neural network architecture, the selection of an algorithm for assessing quality and accuracy; neural network spelling; training and checking accuracy and quality, checking for overfitting (retraining). As development tools, Python language, PyTorch library, Jupyter development environment, convolutional neural network architecture — Unet are proposed. Features of the analysis of input images of steel sheets, features of the implementation of the neural network itself are described. The function of binary cross entropy was chosen as a criterion for assessing accuracy, since it seeks to bring the distribution of the network forecast to the target, fine not only for erroneous predictions, but also for uncertain ones. For additional evaluation, the DICE method was also used. The accuracy of the resulting model is 84 %. The proposed solution can become part of a hardware-software system for automating the recognition of defects on metal sheets.

scholarly journals Open Relation Extraction in Patent Claims with a Hybrid Network

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Boting Geng
Keyword(s):  
Neural Network ◽  
Natural Language ◽  
Language Processing ◽  
Network Architecture ◽  
Complex Structure ◽  
Relation Extraction ◽  
Attention Mechanism ◽  
Hybrid Network ◽  
Long Distance ◽  
Hybrid Neural Network

Research on relation extraction from patent documents, a high-priority topic of natural language process in recent years, is of great significance to a series of patent downstream applications, such as patent content mining, patent retrieval, and patent knowledge base constructions. Due to lengthy sentences, crossdomain technical terms, and complex structure of patent claims, it is extremely difficult to extract open triples with traditional methods of Natural Language Processing (NLP) parsers. In this paper, we propose an Open Relation Extraction (ORE) approach with transforming relation extraction problem into sequence labeling problem in patent claims, which extract none predefined relationship triples from patent claims with a hybrid neural network architecture based on multihead attention mechanism. The hybrid neural network framework combined with Bi-LSTM and CNN is proposed to extract argument phrase features and relation phrase features simultaneously. The Bi-LSTM network gains long distance dependency features, and the CNN obtains local content feature; then, multihead attention mechanism is applied to get potential dependency relationship for time series of RNN model; the result of neural network proposed above applied to our constructed open patent relation dataset shows that our method outperforms both traditional classification algorithms of machine learning and the-state-of-art neural network classification models in the measures of Precision, Recall, and F1.

scholarly journals Selection of neural network architecture and data augmentation procedures for predicting the course of cardiovascular diseases

2021 ◽  
Vol 2094 (3) ◽  
pp. 032037
Author(s):  
M G Dorrer ◽  
S E Golovenkin ◽  
S Yu Nikulina ◽  
Yu V Orlova ◽  
E Yu Pelipeckaya ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Cardiovascular Diseases ◽  
Network Architecture ◽  
Data Augmentation ◽  
Case Histories ◽  
Neural Network Architecture ◽  
Original Dataset ◽  
Augmentation Procedure ◽  
Selection Of

Abstract The article solves the problem of creating models for predicting the course and complications of cardiovascular diseases. Artificial neural networks based on the Keras library are used. The original dataset includes 1700 case histories. In addition, the dataset augmentation procedure was used. As a result, the overall accuracy exceeded 84%. Furthermore, optimizing the network architecture and dataset has increased the overall accuracy by 17% and precision by 7%.

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