Comments on "On hidden nodes in neural nets" by G. Mirchandani and W. Cao

1991 ◽  
Vol 38 (11) ◽  
pp. 1410 ◽  
Author(s):  
G.M. Georgiou
Keyword(s):  
Neural Nets ◽  
Hidden Nodes

On hidden nodes for neural nets

1989 ◽  
Vol 36 (5) ◽  
pp. 661-664 ◽  
Author(s):  
G. Mirchandani ◽  
W. Cao
Keyword(s):  
Neural Nets ◽  
Hidden Nodes

Proposal of Capacity Analysis in Wireless Sensor Networks with Multi-Hop Transmissions and Hidden Nodes

2015 ◽  
Vol E98.B (9) ◽  
pp. 1749-1757
Author(s):  
Yun WEN ◽  
Kazuyuki OZAKI ◽  
Hiroshi FUJITA ◽  
Teruhisa NINOMIYA ◽  
Makoto YOSHIDA
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Capacity Analysis ◽  
Hidden Nodes

Advanced Statistical Tools for Improving Yield and Reliability

1999 ◽  
Author(s):  
Richard C. Kittler
Keyword(s):  
Data Mining ◽  
Analysis Data ◽  
Neural Nets ◽  
Future Trends ◽  
Statistical Tools ◽  
Mining Technology ◽  
Explanatory Variables ◽  
Data Coverage ◽  
Statistical Approaches ◽  
Adequate Data

Abstract Analysis of manufacturing data as a tool for failure analysts often meets with roadblocks due to the complex non-linear behaviors of the relationships between failure rates and explanatory variables drawn from process history. The current work describes how the use of a comprehensive engineering database and data mining technology over-comes some of these difficulties and enables new classes of problems to be solved. The characteristics of the database design necessary for adequate data coverage and unit traceability are discussed. Data mining technology is explained and contrasted with traditional statistical approaches as well as those of expert systems, neural nets, and signature analysis. Data mining is applied to a number of common problem scenarios. Finally, future trends in data mining technology relevant to failure analysis are discussed.

Rancang Bangun Sistem Pakar Prediksi Stres Belajar Dengan Neural Network Algoritma Backpropagation

2016 ◽  
Vol 7 (2) ◽  
pp. 105-112
Author(s):  
Adhi Kusnadi ◽  
Idul Putra
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Stress Level ◽  
Network Structure ◽  
Human Being ◽  
Accuracy Rate ◽  
Backpropagation Algorithm ◽  
Stress Prediction ◽  
Index Terms ◽  
Hidden Nodes

Stress will definitely be experienced by every human being and the level of stress experienced by each individual is different. Stress experienced by students certainly will disturb their study if it is not handled quickly and appropriately. Therefore we have created an expert system using a neural network backpropagation algorithm to help counselors to predict the stress level of students. The network structure of the experiment consists of 26 input nodes, 5 hidden nodes, and 2 the output nodes, learning rate of 0.1, momentum of 0.1, and epoch of 5000, with a 100% accuracy rate. Index Terms - Stress on study, expert system, neural network, Stress Prediction

Sign in / Sign up

Export Citation Format

Share Document