A novel approach to interface design for a neural network expert system

2003 ◽  
Author(s):  
I.E. Russell ◽  
H.A. Fatmi
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Interface Design ◽  
Novel Approach

scholarly journals A Neural Network Truth Maintenance System

1991 ◽  
Vol 113 (1) ◽  
pp. 187-191
Author(s):  
W. P. Mounfield ◽  
S. Guddanti
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Expert System ◽  
Integrated Circuits ◽  
Limit Cycles ◽  
Experimental Setup ◽  
Maintenance System ◽  
Novel Approach ◽  
Truth Maintenance ◽  
Valid Solution

A novel approach using neural networks to solve expert system problems is presented in this paper. Facts are represented by neurons and their interconnections form the knowledge base. The Truth Maintenance System neural network arrives at a valid solution provided the solution exists. A valid solution is a consistent set of facts. If the solution does not exist the network limit cycles. An experimental setup was built and tested using conventional integrated circuits. The hardware design is suitable for VLSI implementation for large, real-time problems.

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

A novel identification approach for shearer running status through integration of rough sets and improved wavelet neural network

2016 ◽  
Vol 230 (16) ◽  
pp. 2792-2805 ◽  
Author(s):  
Lei Si ◽  
Zhongbin Wang ◽  
Xinhua Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Rough Sets ◽  
Particle Swarm Optimization Algorithm ◽  
Wavelet Neural Network ◽  
Decision Table ◽  
Swarm Optimization ◽  
Novel Approach ◽  
Identification Approach ◽  
Simulation Results

In order to accurately and conveniently identify the shearer running status, a novel approach based on the integration of rough sets (RS) and improved wavelet neural network (WNN) was proposed. The decision table of RS was discretized through genetic algorithm and the attribution reduction was realized by MIBARK algorithm to simply the samples of WNN. Furthermore, an improved particle swarm optimization algorithm was proposed to optimize the parameters of WNN and the flowchart of proposed approach was designed. Then, a simulation example was provided and some comparisons with other methods were carried out. The simulation results indicated that the proposed approach was feasible and outperforming others. Finally, an industrial application example of mining automation production was demonstrated to verify the effect of proposed system.

scholarly journals Prediction of Fouling in Condenser Based on Fuzzy Stage Identification and Chebyshev Neural Network

2013 ◽  
Vol 13 (2) ◽  
pp. 94-99 ◽  
Author(s):  
Shaosheng Fan ◽  
Qingchang Zhong
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Parameter Optimization ◽  
Adaptive Algorithm ◽  
External Disturbance ◽  
Operational Parameters ◽  
Chebyshev Neural Network ◽  
Adaptive Parameter ◽  
Novel Approach ◽  
Variation Trends

The prediction of fouling in condenser is heavily influenced by the periodic fouling process and dynamics change of the operational parameters, to deal with this problem, a novel approach based on fuzzy stage identification and Chebyshev neural network is proposed. In the approach, the overall fouling is separated into hard fouling and soft fouling, the variation trends of these two kinds of fouling are approximated by using Chebyshev neural network, respectively, in order to make the prediction model more accurate and robust, a fuzzy stage identification method and adaptive algorithm considering external disturbance are introduced, based on the approach, a prediction model is constructed and experiment on an actual condenser is carried out, the results show the proposed approach is more effective than asymptotic fouling model and adaptive parameter optimization prediction model.

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