Artificial Neural Networks Optimization Method for Radioactive Source Localization

1995 ◽  
Vol 110 (2) ◽  
pp. 228-237 ◽  
Author(s):  
Eitan Wacholder ◽  
Ezra Elias ◽  
Yoram Merlis
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Source Localization ◽  
Optimization Method ◽  
Radioactive Source ◽  
Artificial Neural ◽  
Neural Networks Optimization

Intelligence Selection System for Honing Parameter Based on Genetics and Artificial Neural Networks

2010 ◽  
Vol 102-104 ◽  
pp. 846-850
Author(s):  
Wen Yu Pu ◽  
Yan Nian Rui ◽  
Lian Sheng Zhao ◽  
Chun Yan Zhang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Selection System ◽  
Process Data ◽  
Replacement Strategy ◽  
Machining Quality ◽  
Main Factors ◽  
Artificial Neural ◽  
Neural Networks Optimization ◽  
The Relationship

Appropriate selecting of process parameters influences the machining quality greatly. For honing, the main factors are product precision, material components and productivity. In view of this situation, a intelligence selection model for honing parameter based on genetics and artificial neural networks was built by using excellent robustness, fault-tolerance of artificial neural networks optimization process and excellent self-optimum of genetic algorithm. It can simulate the decision making progress of experienced operators, abstract the relationship from process data and machining incidence, realize the purpose of intelligence selection honing parameter through copying, exchanging, aberrance, replacement strategy and neural networks training. Besides, experiment was performed and the results helped optimize the theories model. Both the theory and experiment show the updated level and feasibility of this system.

Generation of Optimal Artificial Neural Networks Using a Pattern Search Algorithm: Application to Approximation of Chemical Systems

2008 ◽  
Vol 20 (2) ◽  
pp. 573-601 ◽  
Author(s):  
Matthias Ihme ◽  
Alison L. Marsden ◽  
Heinz Pitsch
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Source Term ◽  
Transfer Functions ◽  
Search Algorithm ◽  
Optimization Method ◽  
Pattern Search ◽  
Memory Storage ◽  
Categorical Variables ◽  
Artificial Neural

A pattern search optimization method is applied to the generation of optimal artificial neural networks (ANNs). Optimization is performed using a mixed variable extension to the generalized pattern search method. This method offers the advantage that categorical variables, such as neural transfer functions and nodal connectivities, can be used as parameters in optimization. When used together with a surrogate, the resulting algorithm is highly efficient for expensive objective functions. Results demonstrate the effectiveness of this method in optimizing an ANN for the number of neurons, the type of transfer function, and the connectivity among neurons. The optimization method is applied to a chemistry approximation of practical relevance. In this application, temperature and a chemical source term are approximated as functions of two independent parameters using optimal ANNs. Comparison of the performance of optimal ANNs with conventional tabulation methods demonstrates equivalent accuracy by considerable savings in memory storage. The architecture of the optimal ANN for the approximation of the chemical source term consists of a fully connected feedforward network having four nonlinear hidden layers and 117 synaptic weights. An equivalent representation of the chemical source term using tabulation techniques would require a 500 × 500 grid point discretization of the parameter space.

Artificial neural networks for source localization in the human brain

1991 ◽  
Vol 4 (1) ◽  
pp. 3-21 ◽  
Author(s):  
Udantha R. Abeyratne ◽  
Yohsuke Kinouchi ◽  
Hideo Oki ◽  
Jun Okada ◽  
Fumio Shichijo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Source Localization ◽  
Artificial Neural
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