scholarly journals Transistor Sizing using Hybrid Reinforcement Learning and Graph Convolution Neural Network Algorithm

2021 ◽  
Vol 3 (3) ◽  
pp. 194-208
Author(s):  
P. Karthigaikumar
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Circuit Design ◽  
Figure Of Merit ◽  
Transistor Sizing ◽  
And Topology ◽  
Learning Techniques ◽  
Neural Network Algorithm ◽  
Hybrid Reinforcement ◽  
Graphical Structure

Transistor sizing is one the developing field in VLSI. Many researches have been conducted to achieve automatic transistor sizing which is a complex task due to its large design area and communication gap between different node and topology. In this paper, automatic transistor sizing is implemented using a combinational methods of Graph Convolutional Neural Network (GCN) and Reinforcement Learning (RL). In the graphical structure the transistor are represented as apexes and the wires are represented as boundaries. Reinforcement learning techniques acts a communication bridge between every node and topology of all circuit. This brings proper communication and understanding among the circuit design. Thus the Figure of Merit (FOM) is increased and the experimental results are compared with different topologies. It is proved that the circuit with prior knowledge about the system, performs well.

scholarly journals Extraction of Ship Images using Deep Learning

2020 ◽  
Vol 9 (5) ◽  
pp. 635-638
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Environmental Changes ◽  
The Other ◽  
Traditional Methods ◽  
Network Algorithm ◽  
Marine Industry ◽  
The Neural Network ◽  
Learning Techniques ◽  
Neural Network Algorithm

Ship Extraction is very important in the marine industry. Extraction of ships is helpful to the fishers to find the other ships nearly around the particular area. Still today the fishers are to find the ships using some traditional methods. But now it became difficult due to environmental changes. So, by using the deep learning techniques like the CNN algorithm the ship extraction can be identified effectively. Generally, the ships are identified as narrow bow and parallel hull edge, etc. Here, the Existing system they have used the Tensor flow, to see the performance of the datasets, using Recall and precision. In the proposed system, we are using CNN deep learning techniques to identify the ships. By finding the ships with the techniques, the time will be saved and the productivity can be increased. The features of the ship image are taken and trained using the neural network algorithm and then the prediction is done by testing the images.

Intelligent scheduling using a neural network model in conjunction with reinforcement learning

2005 ◽  
Vol 219 (2) ◽  
pp. 229-235
Author(s):  
C J Fourie
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reinforcement Learning ◽  
Learning From Experience ◽  
Scheduling System ◽  
Simulated Environment ◽  
The Neural Network ◽  
Learning Techniques ◽  
Proposed Model ◽  
Intelligent Scheduling

This paper describes the use of an artificial neural network in conjunction with reinforcement learning techniques to develop an intelligent scheduling system that is capable of learning from experience. In a simulated environment the model controls a mobile robot that transports material to machines. States of ‘happiness’ are defined for each machine, which are the inputs to the neural network. The output of the neural network is the decision on which machine to service next. After every decision, a critic evaluates the decision and a teacher ‘rewards’ the network to encourage good decisions and discourage bad decisions. From the results obtained, it is concluded that the proposed model is capable of learning from past experience and thereby improving the intelligence of the system.

scholarly journals Plant leaf Disease Identification using Deep learning techniques

2020 ◽  
Vol 9 (5) ◽  
pp. 462-464
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Plant Disease ◽  
Environmental Changes ◽  
Vital Role ◽  
Network Algorithm ◽  
The Neural Network ◽  
Learning Techniques ◽  
Neural Network Algorithm ◽  
Leaf Image

Agriculture is an important source of our country’s growth. The major loss in an agricultural economy is because of the plant disease. Though technology plays a vital role in all the fields still today the agriculture field is using the old methodologies. Successful cultivation depends on identifying plant disease. Previously the identification was done manually by the experienced people but now it became difficult due to environmental changes. By using the deep learning techniques the plant disease can be identified effectively. Vgg16 and ResNet are the proposed techniques to increase accuracy than the existing system. The disease can be identified with images of the leaves by applying those deep learning techniques. Detection can be involved in steps like image acquisition, image pre-processing, image segmentation, feature extraction, and classification. By controlling the disease, productivity can be increased. The features of the leaf image are taken and trained using the neural network algorithm and then the prediction is done by testing the images. The features of the leaf image are taken and trained using the neural network algorithm and then the prediction is done by testing the images

A NEURAL NETWORK ALGORITHM FOR DENSITY MEASUREMENT OF MULTIPHASE FLOW

2012 ◽  
Vol 24 (2) ◽  
pp. 89-103 ◽  
Author(s):  
Nabeel Al-Rawahi ◽  
Mahmoud Meribout ◽  
Ahmed Al-Naamany ◽  
Ali Al-Bimani ◽  
Adel Meribout
Keyword(s):  
Neural Network ◽  
Multiphase Flow ◽  
Density Measurement ◽  
Network Algorithm ◽  
Neural Network Algorithm
Sign in / Sign up

Export Citation Format

Share Document