Software Development for Black Tea's Physical Variable and Quality Class Relationship Analyzing Using Correlation Adaptive Vis. Pat. Recognition Artificial Neural Network Based Expert System: Proof of Concept of Auto Parameter Choosing Expert System

2010 ◽  
Author(s):  
Renan Prasta Jenie
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Expert System ◽  
Software Development ◽  
Physical Variable ◽  
Proof Of Concept ◽  
Quality Class ◽  
Artificial Neural

Electroencephalographic staging of hepatic encephalopathy by an artificial neural network and an expert system

2005 ◽  
Vol 35 (5-6) ◽  
pp. 162-167 ◽  
Author(s):  
A. Pellegrini ◽  
E. Ubiali ◽  
R. Orsato ◽  
S. Schiff ◽  
A. Gatta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hepatic Encephalopathy ◽  
Expert System ◽  
Artificial Neural

scholarly journals Analyses of the method development of decisions in an expert system with the use of information from an artificial neural network

2018 ◽  
Vol 178 ◽  
pp. 07002 ◽  
Author(s):  
Stanisław Duer ◽  
Konrad Zajkowski ◽  
Serghei Scaticailov ◽  
Paweł Wrzesień
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Expert System ◽  
Intelligent Systems ◽  
Method Development ◽  
Diagnostic System ◽  
Intelligence System ◽  
Intelligent Maintenance ◽  
The Neural Network ◽  
Artificial Neural

The present article covers the use of an artificial intelligence system in the organization of the prevention of technical objects. For this purpose, the composition of this system including an intelligent diagnostic system and an intelligent maintenance system was characterized and described. An artificial neural network and an expert system, which work among others on the basis of the information developed by the neural network, perform a special function in these systems. It was mentioned in the article that the mathematical model of the regeneration process of the functional properties (prevention) of an object forms the basis of the organization of the prevention activities of technical devices and objects with the use of intelligent systems. This model demonstrated the possibilities and directions for the use of maintenance intelligent systems.

Construction and Functions of Self-Study System for Power Plant Operation

2000 ◽  
Author(s):  
Shengli Tang ◽  
Zuwei He ◽  
Tao Chang ◽  
Liming Xuan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Expert System ◽  
Power Plant ◽  
Knowledge Base ◽  
Practical Experience ◽  
Plant Operation ◽  
Self Study ◽  
Power Plant Operation ◽  
Artificial Neural

Abstract In this paper, the Construction and functions of the self-study system for power plant operation is introduced. As a self-study system, it consists of two parts, a simulator and knowledge base. The knowledge base has been built by the combination of expert system and artificial neural network, which supports the system with practical experience and theoretic knowledge. The trainees’ knowledge can be improved by using the system. The realization of the intelligent training function, applications of expert system and artificial neural network are mainly introduced in this paper.

scholarly journals Predictive Modeling of a Leaf Conceptual Midpoint Quasi-Color (CMQ) Using an Artificial Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3938
Author(s):  
Ivan Simko
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Predictive Modeling ◽  
Leafy Vegetables ◽  
Proof Of Concept ◽  
Plant Leaves ◽  
Actual Color ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Very High

The color of plant leaves is moderated by the content of pigments, which can show considerable dorsiventral distribution. Two typical examples are leafy vegetables and ornamentals, wherein red and green color surfaces can be seen on the same leaf. The proof of concept is provided for predictive modeling of a leaf conceptual mid-point quasi-color (CMQ) from the content of pigments. The CMQ idea is based on the hypothesis that the content of pigments in leaves is associated with the combined color from both surfaces. The CMQ, which is calculated from CIELab color coordinates at adaxial and abaxial antipodes, is thus not an actual color, but a notion that can be used in modeling. The CMQ coordinates, predicted from the content of chlorophylls and anthocyanins by means of an artificial neural network (ANN), matched well with the CMQ coordinates empirically found on photosynthetically active leaves of lettuce (Lactuca sativa L.), but also with other plant species with comparable leaf attributes. Modeled values of lightness (qL*) decreased with the increasing content of both pigments, while the redness or greenness (qa*) and yellowness or blueness (qb*) of the CMQ were affected more by a relative content of chlorophylls and anthocyanins in leaves. The highest vividness of quasi-colors (qC*) was modeled for leaves with a high content of either pigment alone. The model predicted a substantially duller quasi-color for leaves with chlorophylls and anthocyanins present together, particularly when both pigments were present at very high levels.

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